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Perera, Treshani, David Higgins, and Woon-Weng Wong. "The evaluation of the Australian office market forecast accuracy." Journal of Property Investment & Finance 36, no. 3 (April 3, 2018): 259–72. http://dx.doi.org/10.1108/jpif-04-2017-0029.
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Purpose Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events. Design/methodology/approach This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR). Findings The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts. Research limitations/implications Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data. Practical implications The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts. Originality/value The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.
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Heidari, H. "Alternative bvar models for forecasting inflation." Acta Oeconomica 61, no. 1 (March 1, 2011): 61–75. http://dx.doi.org/10.1556/aoecon.61.2011.1.4.
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This paper investigates the use of different priors to improve the inflation forecasting performance of BVAR models with Litterman’s prior. A Quasi-Bayesian method, with several different priors, is applied to a VAR model of simulated data as well as to the Australian economy from 1978:Q2 to 2006:Q4. A novel feature with this paper is the use of g-prior in the BVAR models to alleviate poor estimation of drift parameters of Traditional BVAR models. Some results are as follows: (1) In the Quasi-Bayesian framework, BVAR models with Normal-Wishart prior provide the most accurate forecasts of Australian inflation; (2) Generally in the parsimonious models, the BVAR with g-prior performs better than BVAR with Litterman’s prior; (3) In simulated data, the BVAR model with g-prior produces more accurate forecasts of driftless variable in the long-run horizons (first and second year forecast horizons).
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Islam, Tamanna, Ashfaque A. Mohib, and Shahnaz Zarin Haque. "Econometric Models for Forecasting Remittances of Bangladesh." Business and Management Studies 4, no. 1 (December 13, 2017): 1. http://dx.doi.org/10.11114/bms.v4i1.2860.
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At present, the remittance of Bangladesh (RB) is the largest source of foreign exchange earning of the country. The RB plays a critical role in alleviating the foreign-exchange constraint and supporting the balance of payments, enabling imports of capital goods and raw materials for industrial development. Remittance from overseas migrant workers certainly increases the income disparity between classes of the rural society. Therefore forecasting plays an important role to know the future situation of economic condition. This paper employed the prospective data on RB to derive a unique and suitable forecasting model. The data were collected from Bangladesh Bank (BB) during January, 1998 to December, 2003. The Autoregressive Integrated Moving Average (ARIMA) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to find out the best one. The findings indicated that the ARIMA (0,1,1) (0,2,1)12 and the GARCH (2,1) models were appropriate for our data and the GARCH (2,1) model appeared to be the best one between these.
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Masouman, Ashkan, and Charles Harvie. "Forecasting, impact analysis and uncertainty propagation in regional integrated models: A case study of Australia." Environment and Planning B: Urban Analytics and City Science 47, no. 1 (April 16, 2018): 65–83. http://dx.doi.org/10.1177/2399808318767128.
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The integration of input–output and econometric models at regional level has gained popularity for its superior performance in forecasting employment and examining the impacts of policies. There are a number of approaches to integrate the two models. This paper examines the integration of input–output with econometric modelling using two merging methodologies, namely coupling and holistic embedding. Each methodology is analysed with respect to the accuracy of its results of total and sectoral employment forecasting. Both methodologies are applied to a regional economy in Australia. The methodology which shows superior forecasting accuracy is applied to examine the significance of sectors that generate the highest number of employments relative to other sectors.
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Suryan, Viktor. "ECONOMETRIC FORECASTING MODELS FOR AIR TRAFFIC PASSENGER OF INDONESIA." Journal of the Civil Engineering Forum 3, no. 1 (August 29, 2017): 303. http://dx.doi.org/10.22146/jcef.26594.
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One of the major benefits of the air transport services operating in bigger countries is the fact that they provide a vital social economic linkage. This study is an attempt to establish the determinants of the passenger air traffic in Indonesia. The main objective of the study is to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of panel data and to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of time series data. This research also predicts the upcoming number of air traffic passenger until 2030. Air transportation and the economic activity in a country are interdependent. This work first uses the data at the country level and then at the selected airport level for review. The methodology used in this study has adopted the study for both normal regression and panel data regression techniques. Once all these steps are performed, the final equation is taken up for the forecast of the passenger inflow data in the Indonesian airports. To forecast the same, the forecasted numbers of the GDP (Gross Domestic Product) and population (independent variables were chosen as a part of the literature review exercise) are used. The result of this study shows the GDP per capita have significant related to a number of passengers which the elasticity 2.23 (time-series data) and 1.889 for panel data. The exchange rate variable is unrelated to a number of passengers as shown in the value of elasticity. In addition, the total of population gives small value for the elasticity. Moreover, the number of passengers is also affected by the dummy variable (deregulation). With three scenarios: low, medium and high for GDP per capita, the percentage of growth for total number of air traffic passenger from the year 2015 to 2030 is 199.3%, 205.7%, and 320.9% respectively.
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Langcake, Sean, and Tim Robinson. "Forecasting the Australian economy with DSGE and BVAR models." Applied Economics 50, no. 3 (April 28, 2017): 251–67. http://dx.doi.org/10.1080/00036846.2017.1319558.
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Hozer, Józef, and Mariusz Doszyń. "Econometric Models of Propensities." Folia Oeconomica Stetinensia 6, no. 1 (January 1, 2007): 15–25. http://dx.doi.org/10.2478/v10031-007-0008-1.
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Econometric Models of Propensities Human being is one of the most important sources of causative forces of events that assemble economical processes. Working out the effective tools that enable measurement of the impact of people on socio-economic processes is necessary in analyzing, troubleshooting and forecasting. In the article the issues of calculating propensities by means of properly specified econometrics models were presented. The definition of propensity was introduced. Questions connected with topic of propensities were presented in context of concepts promoted by Szczecin school of econometrics (pentagon of sources of causative forces, types of relationships in economics, geometric interpretation of personality, broom of events). Econometric models, useful in analyzing propensities, were classified on primary models, econometrics models of average propensities and econometrics models of marginal propensities. Connections between the models were described. Settlement of analytical shapes of characterized models was mentioned. In an empirical example the presented methods were used to analyze average and marginal propensity to consumption of alcoholic beverages and tobacco in the households of employees in manual labour positions in Poland in years 1993-2005.
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Shen, Ze, Qing Wan, and David J. Leatham. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN." Journal of Risk and Financial Management 14, no. 7 (July 20, 2021): 337. http://dx.doi.org/10.3390/jrfm14070337.
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One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.
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Vetakova, Yulia, and Irina Bulgakova. "Economic growth forecasting apparatus in regions with different reproduction structure." E3S Web of Conferences 110 (2019): 02071. http://dx.doi.org/10.1051/e3sconf/201911002071.
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Identifying the main factors of economic growth, the study and analysis of the mechanisms of their influence on the development of regions can be considered one of the most pressing problems in modern economic research. The main goal of the research is to build an econometric production model of regional economic growth on the basis of an analysis of existing analytical approaches to assessing regional economic growth. To achieve this goal, a study was conducted to assess the quality of economic growth using multiplicative models of production functions by regions of the Russian Federation, which will allow building a unique function for each region, which describes the economic potential and the future direction of regional development. As a result of the study, it was concluded that it is necessary to adjust the existing and develop new approaches to assessing the dynamics of economic development. An econometric production model of regional economic growth is presented, which is based on production functions.
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TREVOR, R. G., and S. J. THORP. "VAR FORECASTING MODELS OF THE AUSTRALIAN ECONOMY: A PRELIMINARY ANALYSIS." Australian Economic Papers 27, s1 (June 1988): 108–20. http://dx.doi.org/10.1111/j.1467-8454.1988.tb00697.x.
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Kaboudan, Mahmoud A. "Oil Revenue and Kuwait's Economy: An Econometric Approach." International Journal of Middle East Studies 20, no. 1 (February 1988): 45–66. http://dx.doi.org/10.1017/s0020743800057500.
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This paper presents a macroeconomic model for a small developing oil-exporting economy: Kuwait. The model is a simultaneous system of difference equations. Historic effects of changes in revenues from oil exports on the country's economic conditions are simulated. The model is then used to forecast these conditions through 1990, and to test two fiscal policy alternatives under the assumption that revenues from Kuwait's oil exports will remain constant from 1986 to 1990. The following are key words: developing economies; oil-exporting economies; Middle East economies; Kuwait; Kuwait's economy; policy models; macroeconomic models; econometric models; macroeconometric models; forecasting models; and policy models.
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Sievka, Victoria, Igor Shevchuk, Aleksey Stepanov, and Oksana Tykhankina. "Application of cluster models in forecasting housing construction economic potential in the region." E3S Web of Conferences 217 (2020): 11006. http://dx.doi.org/10.1051/e3sconf/202021711006.
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The article introduces the first econometric modeling of the needs in new housing construction, reconstruction, capital repairs and finishing of uncompleted construction objects in the region on the basis of cluster models. Forecasting diagrams of gaps between the existing economic potential of housing construction and a normative need in sector housing as well as the need in introducing sector housing to reach mean European standards of housing are plotted.
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JIANG, Heng, and Chunlu LIU. "IDENTIFYING DETERMINANTS OF DEMAND FOR CONSTRUCTION USING AN ECONOMETRIC APPROACH." International Journal of Strategic Property Management 19, no. 4 (December 23, 2015): 346–57. http://dx.doi.org/10.3846/1648715x.2015.1072856.
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Economic variation and its effects on construction demand have received a great deal of attention in construction economics studies. An understanding of future trends in demand for construction could influence investment strategies for a variety of parties, including construction developers, suppliers, property investors and financial institutions. This paper derives the determinants of demand for construction in Australia using an econometric approach to identify and evaluate economic indicators that affect construction demand. The forecasting contribution of different determinants of economic indicators and their categories to the demand for construction are further estimated. The results of this empirical study suggest that changes in consumer's expectation, income and production, and demography and labour force are closely correlated with the movement of construction demand; and 14 economic indicators are identified as the determinants for construction demand. It was found that the changes in construction price, national income, size of population, unemployment rate, value or export, household expenditure and interest rates play key roles in explaining future variations in the demand for construction in Australia. Some “popular” macroeconomic indicators, such as GDP, established house price and bank loans produced inconclusive results.
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Lysenko, Roman, and Nataliia Kolesnichenko. "Nowcasting of Economic Development Indicators Using the NBU’s Business Survey Results." Visnyk of the National Bank of Ukraine, no. 235 (March 30, 2016): 43–56. http://dx.doi.org/10.26531/vnbu2016.235.043.
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The article was devoted to the research of possibilities to use Business Outlook Survey results, which are carried out by National Bank of Ukraine, for the short-term forecasting of economic development, in particular, the Gross Domestic Product of Ukraine. The different methods of building of the leading index of economic development, their advantages, and their restrictions are examined. The choice of the best index, which provides for the higher accuracy of forecasting the GDP, is carried out with the use of econometric models.
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Hurina, O., V. Krylenko, and I. Novikov. "Forecasting the Main Indicators of Insurance Companies." Modern Economics 25, no. 1 (February 23, 2021): 52–57. http://dx.doi.org/10.31521/modecon.v25(2021)-08.
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Abstract. Introduction. Econometric and adaptive models make it possible to predict financial and economic indicators in the short and long term. The most common forecasting models are linear trend models, adaptive Brown, Holt, Holt-Winters, Box-Jenkins, autoregressive and other models. It has been proved that the use of adaptive forecasting models becomes especially relevant in the context of constant changes in the external environment, instability of the economic and political situation. Purpose. The purpose of this article is to substantiate the expediency of using forecasting methods when planning the development of the insurance market and to implement the procedure for forecasting the main indicators of its development using modern methods and techniques. Results. Improving the efficiency of the insurance market is facilitated by the correct organization of its planning and the direct implementation of the planned indicators. Optimality of planning is determined by the degree to which the accuracy of the predicted level of planned indicators is achieved. The methodology and results of the forecast of insurance payments made for the near future can be taken as a basis for drawing up current and strategic plans of insurance companies. Conclusions. It has been established that one of the barriers to the effective development of the insurance market in general and insurance companies in particular is the insufficient level of planning of their activities, especially in terms of forecasting key indicators. The procedure for forecasting the receipt of insurance payments was implemented using modern forecasting methods. The effectiveness of the Brown’s adaptive model for short-term planning of insurance premiums is proved. The proposed model was tested for adequacy, on its basis, recommendations were developed for further application in the practice of insurance companies. Keywords: insurance; insurance market; planning; forecasting; econometric model; adaptive model; trend extrapolation.
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Gerasimov, Aleksey N., Evgeny I. Gromov, Yury S. Skripnichenko, Oksana P. Grigoryeva, and Victoria Yu Skripnichenko. "Models and Forecasts of the Export Potential of the Regional Economic System." REGIONOLOGY 30, no. 4 (December 30, 2022): 762–82. http://dx.doi.org/10.15507/2413-1407.121.030.202204.762-782.
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Introduction. The export potential of the regional agricultural market causes great interest from both the scientific community and government agencies. Many scientific studies are devoted to the search for methods to increase the export potential in order to ensure the sustainable development of regional economic systems. The article proposes and tests the original author’s algorithm for creating a dynamic econometric model for forecasting the volumes of production, sales and exports of the main types of livestock products at the regional level. The purpose of the article is to assess the export potential of the main products of the regional agricultural market based on the built dynamic econometric models. Materials and Methods. The research is based on a set of empirical data of result and input variables characterizing the production, sales and export of the main livestock products in the region for the period 2010–2020. Research methods include dynamic analysis, econometric modeling and forecasting. The dynamic analysis carried out made it possible to assess the change in the production, sale and export of the main agricultural products in the region, to assess the current trends. Based on the constructed econometric models, the most significant factors influencing the resulting variables were identified, the specifications and verification of the models were carried out. The method of extrapolation of the identified trends made it possible to evaluate the predicted values of the resulting variables for the medium term. Results. Based on the selected input variables, models of production, sale and export of milk, wool and eggs by agricultural producers in the region were built. From a variety of alternative models, models with the best statistical quality characteristics were selected. The high level of quality of the obtained models made it possible to use them for predictive calculations of the levels of resulting variables for the period 2021–2026. Comparison of the results of the forecasts made it possible to identify the types of livestock products that already have a high level of exportability. In addition, types of products with a low level of exportability were identified, which have a high potential for increase. Discussion and Conclusion. As a result of using econometric modeling methods, dynamic models were obtained that made it possible to obtain a forecast for the development of livestock in a region with a high export potential in the near future. The practical significance of this article lies in the possibility of influencing the production, sale and export of livestock products in the region through a change in the corresponding set of factor variables of the models. Thus, the resulting dynamic models can be used both by agricultural producers for planning economic activities, and by regional authorities when drawing up regional development programs.
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Baker, T. Brent, and Raymond G. Deardorf. "Development and Application of a Revenue and Ridership Forecasting Model for Ferry Service." Transportation Research Record: Journal of the Transportation Research Board 1608, no. 1 (January 1997): 40–46. http://dx.doi.org/10.3141/1608-05.
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A combination of statistical approaches is used to develop near-to mid-range ridership and revenue forecasting models for Washington State Ferries for use in quarterly budget updates. Econometric regression models use historical and forecast trends in state economic and demographic variables to project systemwide ridership by six fare categories for different fare scenarios. Time series analysis models are used to project ferry ridership at the individual route level by six fare categories. The sum of the time series route forecasts is then calibrated to the econometric systemwide totals to yield unconstrained ridership forecasts by route and fare category. A capacity constraint model handles cases where the demand for vehicle travel exceeds vessel capacity by generating ridership ceilings for different service scenarios. Finally, the appropriate fares are applied to the ridership projections to arrive at revenue forecasts over a 10-year horizon.
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Chen, An-Sing, and Mark T. Leung. "Dynamic Foreign Currency Trading Guided by Adaptive Forecasting." Review of Pacific Basin Financial Markets and Policies 01, no. 03 (September 1998): 383–418. http://dx.doi.org/10.1142/s0219091598000247.
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The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. The optimal conditions implied by the investment rules maximize the expected profits given the expected changes in exchange rates and the interest rate differentials between domestic and foreign countries. Both empirical and simulation experiments suggest that the proposed nonlinear adaptive forecasting model not only produces better forecasts but also results in higher investment returns than other types of models. The effect of risk aversion is also considered in the investment simulation.
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Ulanchuk, V., E. Zharun, N. Korotieiev, A. Nepochatenko, and S. Sokoliuk. "Econometric approaches to forecasting financial support of socio-economic development of the region." Collected Works of Uman National University of Horticulture 2, no. 99 (December 22, 2021): 163–71. http://dx.doi.org/10.31395/2415-8240-2021-99-2-163-171.
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In the given article it is noted that the level of forecasting of processes of social development is determined by the efficiency of planning and management of economy and other spheres. Social and economic forecasting of basic trends of social development allows use of special calculation and logic methods, giving the opportunity to determine parameters of functioning of separate elements of productive forces in their interrelation and interdependence. At the current stage of regional development of the state, the forecasting of the management of social-economic processes in the region is urgent, and the need for their improvement in order to obtain effective tools for determining the main guidelines and directions of regional policy. Predictions that include scientific justification should be central to the planned decisions of state authorities and the implementation of social-economic policies in the region, to determine the main directions of its future development, place and role in the national economy. The process of forming a modern system of forecasting regional development in Ukraine took place under conditions of a large-scale state transformation and reorganization. The change in the political regime and reform of the Ukrainian economy, which began in the 1990s, led to the inversion of the role of the territory in the system of public administration. Regions that previously had very limited rights in the agricultural sector, received the right to make political, economic, social, cultural and other decisions on their own. Economic forecasting is necessary for determining ways of society development and economic resources which provide its achievement, for revealing most likely and economically efficient variants of long-term, medium term and current plans, grounding main directions of economic and technical politics, forecasting the consequences of the made decisions and measures taken at present. Application of econometric models in economics gives the opportunity to distinguish and formally describe the most significant, the most essential relations of economic variables and objects, as well as to get new knowledge about the object in the inductive way. In such model, in the simplified form, by many assumptions, the main dependence between economic indicators is determined.
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Suryan, Viktor, Anggi Nidya Sari, Direstu Amalia, and M. Fathar Habillah. "Econometric Forecasting models for Air Freight in Indonesia (And How Will It be Affected by COVID-19?)." Journal of Airport Engineering Technology (JAET) 1, no. 1 (December 29, 2020): 30–33. http://dx.doi.org/10.52989/jaet.v1i1.5.
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Forecasting using the economic factor as indicators seems significant related. The experts also suggest predict the number of traffic in transport by approaching the econometric models. This paper predicting the upcoming number of air freight until 2030. The result shows GDP is significant related to number of air freight. However, the economic crisis also contributes to decreasing the value. Also, the Covid-19 impact the economic in the country. Predicting the number of air freight in 2020 is going down. The number seems gradually grow after two years and assume the pandemic is over soon
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Banerjee, Swagata “Ban”, and Babatunde A. Obembe. "Econometric Forecasting of Irrigation Water Demand Conserves a Valuable Natural Resource." Journal of Agricultural and Applied Economics 45, no. 3 (August 2013): 557–68. http://dx.doi.org/10.1017/s107407080000506x.
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Natural causes (such as droughts), non-natural causes (such as competing uses), and government policies limit the supply of water for agriculture in general and irrigating crops in particular. Under such reduced water supply scenarios, existing physical models reduce irrigation proportionally among crops in the farmer's portfolio, disregarding temporal changes in economic and/or institutional conditions. Hence, changes in crop mix resulting from expectations about risks and returns are ignored. A method is developed that considers those changes and accounts for economic substitution and expansion effects. Forecasting studies based on this method with surface water in Georgia and Alabama demonstrate the relative strength of econometric modeling vis-à-vis physical methods. Results from a study using this method for ground water in Mississippi verify the robustness of those findings. Results from policy-induced simulation scenarios indicate water savings of 12% to 27% using the innovative method developed. Although better irrigation water demand forecasting in crop production was the key objective of this pilot project, conservation of a valuable natural resource (water) has turned out to be a key consequence.
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Banerjee, Swagata “Ban”, Irfan Y. Tareen, Lewell F. Gunter, Jimmy Bramblett, and Michael E. Wetzstein. "Forecasting Irrigation Water Demand: A Case Study on the Flint River Basin in Georgia." Journal of Agricultural and Applied Economics 39, no. 3 (December 2007): 641–55. http://dx.doi.org/10.1017/s1074070800023324.
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Southeast drought conditions have accentuated the demand for irrigation in the face of restricted water supply. For allocating this supply, Georgia held an auction for withdrawing irrigated acreage. This auction withdrew 33,000 acres from irrigation, resulting in a physical estimate of a 399 acre-feet daily increase in water flow. The actual reduction is driven by crop distributional changes on the basis of economic substitution and expansion effects. In contrast to the physical estimates, an econometric model that considers these effects is developed. The differences between the physical and econometric models result in an increase in the estimate of water savings of around 19% to 24%.
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Swanson, Norman R., and Halbert White. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models." International Journal of Forecasting 13, no. 4 (December 1997): 439–61. http://dx.doi.org/10.1016/s0169-2070(97)00030-7.
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Sava, Andrii, Vitalii Biskup, Inna Petruk, Nataliia Pokotylska, and Pavlina Fuhelo. "Substantiation of models for forecasting the regional social and economic rural development." Independent Journal of Management & Production 11, no. 8 (May 1, 2020): 626. http://dx.doi.org/10.14807/ijmp.v11i8.1223.
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The areas of application of mathematical and statistical methods for determining the prognostic model of rural development on the example of all Ukraine taking into account the economic, social and environmental component are explored in this article. Econometric modelling is the basis of the research methodology in this work. As a tool for analysis, a specific system of evaluation of selected indicators and their correlation and regression processing is applied. The results showed that the most influential factors for the level of development of rural territories of Ukraine are ten among the 60 indicators, grouped by the three components. They characterize the economic, social and environmental component and determine some impact on the functioning of the country's territorial system as a whole. The mathematical expression of the prognostic model of rural development is established using the regression analysis. A set of measures for regulation of rural development is developed on the basis of these results, which envisages the implementation of measures in the areas of software, normative and legal support. It is expected that this study will help public authorities make more effective decisions on addressing key issues of social and economic processes in rural areas of individual regions, Ukraine as a whole and other countries.
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Stock, James H., and Mark W. Watson. "Twenty Years of Time Series Econometrics in Ten Pictures." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 59–86. http://dx.doi.org/10.1257/jep.31.2.59.
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This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.
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Ejaz, Muhammad, and Javed Iqbal. "Estimation and Forecasting of Industrial Production Index." LAHORE JOURNAL OF ECONOMICS 26, no. 1 (January 1, 2021): 1–30. http://dx.doi.org/10.35536/lje.2021.v26.i1.a1.
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It is essential that policymakers consider cyclical changes in output. Monthly industrial production is one of the most important and commonly used macroeconomic indicators for this purpose. However, monthly estimates of industrial production are not available for Pakistan. Instead, policymakers rely on a large-scale manufacturing (LSM) index that accounts for only 10 percent of GDP. Another limitation of this index is that it accounts primarily for private sector industry, leaving out the direct public sector presence in industrial production. Economic policymakers rely heavily on the LSM index to gauge economic activity in Pakistan. In this study, we compute a new industrial production index (IPI) that extends to the whole industrial sector in Pakistan, incorporating additional information that the LSM index misses. Post-estimation, we build seven econometric models reflecting conditions in the real, financial, and external sectors to estimate year-on-year changes in the new IPI. Our results show that the root mean square error of the ARDL model reflecting financial conditions is lowest of the models tested, which included AR, VAR, and BVAR, across all horizons.
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Ejaz, Muhammad, and Javed Iqbal. "Estimation and Forecasting of Industrial Production Index." LAHORE JOURNAL OF ECONOMICS 26, no. 1 (January 1, 2021): 1–30. http://dx.doi.org/10.35536/lje.2021.v26.i1.a1.
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It is essential that policymakers consider cyclical changes in output. Monthly industrial production is one of the most important and commonly used macroeconomic indicators for this purpose. However, monthly estimates of industrial production are not available for Pakistan. Instead, policymakers rely on a large-scale manufacturing (LSM) index that accounts for only 10 percent of GDP. Another limitation of this index is that it accounts primarily for private sector industry, leaving out the direct public sector presence in industrial production. Economic policymakers rely heavily on the LSM index to gauge economic activity in Pakistan. In this study, we compute a new industrial production index (IPI) that extends to the whole industrial sector in Pakistan, incorporating additional information that the LSM index misses. Post-estimation, we build seven econometric models reflecting conditions in the real, financial, and external sectors to estimate year-on-year changes in the new IPI. Our results show that the root mean square error of the ARDL model reflecting financial conditions is lowest of the models tested, which included AR, VAR, and BVAR, across all horizons.
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Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (January 1, 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Rötheli, Tobias. "Heuristics versus econometrics as a basis for forecasting international inflation differentials." foresight 21, no. 2 (April 8, 2019): 216–26. http://dx.doi.org/10.1108/fs-07-2018-0070.
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Purpose This study aims to address the issue of prediction of inflation differences for an economy that considers either fixing its exchange rate or joining a currency union. In this setting, individual countries have limited control over their inflation, and anticipating the possible course of domestic inflation relative to inflation abroad becomes an important input in policy-making. In this context, the author compares simple forecast heuristics and econometric modeling. Design/methodology/approach The study compares two basically different approaches. The first approach of forecasting consists of simple heuristics. Various heuristics are considered that differ with respect to the economic reasoning that goes into quantifying the forecast rules. The simplest such forecasting heuristic suggests that the average over all available observations of inflation differentials should be taken as a predictor for the future. Bringing more economic insight to bear suggests a further heuristic according to which historical inflation differentials should be adjusted for changes in the nominal exchange rate. A further variant of this approach suggests that a forecast should exclusively rely on data from earlier times under a pegged exchange rate. A fundamentally different approach to prediction builds on dynamic econometric models estimated by using all available historical data independent of the currency regime. Findings The author studies three small member countries of the Eurozone, i.e. Finland, Luxembourg and Portugal. For the evaluation of the various forecasting strategies, he performs out-of-sample predictions over a horizon of five years. The comparison of the four different forecasting strategies documents that the variant of the forecast heuristic that draws on data from earlier experiences under fixed exchange rates performs better than the forecast based on the estimated econometric model. Practical implications The findings of this study provide helpful guidelines for countries considering either joining a currency union or fixing their exchange rate. The author shows that a simple forecasting heuristic gives sound advice for assessing the likely course of inflation. Originality/value This study describes how economic theory can guide the selection of historical data for assessing likely future developments. The analysis shows that using a simple heuristic based on historical analogy can lead to better forecasts than the analytically more sophisticated approach of econometric modeling.
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Mu, Boyu, and Yue Li. "Modelling Innovation and Growth in Panel Data." International Journal of Innovation and Entrepreneurship 1, no. 1 (December 19, 2022): 1. http://dx.doi.org/10.56502/ijie1010003.
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This paper comprehensively reviews how innovation and growth are modelled in theoretical and empirical literature. We distinguish between economic modelling (microfounded) and econometric modelling (ad hoc). The two modelling approaches are complementary to each other for their comparative advantages in causality identification and forecasting performance. Popular models of the two approaches are illustrated and compared. We also propose an eclectic approach to combine the two approaches in one analysis framework.
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Khan, Ashfaque H. "Ashok Parikh. The Estimation and Forecasting of Trade Shares. Bangkok: United Nations Publication, 1986.344 pp.(Development Papers No.5)." Pakistan Development Review 28, no. 2 (June 1, 1989): 168–70. http://dx.doi.org/10.30541/v28i2pp.168-170.
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Macro-econometric' models have proved their worth as tools of intelligent policy-making in a large number of countries where the required data are reasonably good. Hence, despite lingering doubts cast by the rational-expectations school about its utility for macro-economic management, macro-econometric model building has continued unabated. It is, however, well known that national econometric models tell an incomplete story about the international linkages of national economies. Even though every respectable national model contains foreign trade equations, the country-Wide macro-models do not contain the equations to permit the model-builders to incorporate explicit1y the inter-country linkages. It was this realization that led to the creation of Project LINK at the University of Pennsylvania in 1968 under the supervision of Professor Lawrence Klein. Similar efforts are also being pursued by the ESCAP Secretariat thrOUgh Project Asian SUb-Unk to promote a better understanding of the extent of interdependence among the economies of the region. Lately the Asian and Pacific Development Centre, (APDC) in collaboration with the Pakistan Ipstitute of Development Economics (PIDE), has undertaken similar efforts to link the economies of South Asian countries to promote a better understanding of the extent of interdependence through international trade among the economies of the region.
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Arkhipova, M. Yu, and A. I. Smirnov. "Current Trends in Crop Yield Forecasting Based on the Use of Econometric Models." Voprosy statistiki 27, no. 5 (October 26, 2020): 65–75. http://dx.doi.org/10.34023/2313-6383-2020-27-5-65-75.
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Agriculture is one of the most important branches of the national economy and the main supplier of food and raw materials for many industries. Agricultural sector in Russia has recently been undergoing renewal and growth due to the intensifi cation and application of modern innovative technologies for monitoring the state of fields using satellite images based on computer vision systems. At the same time, there is still a number of problems and challenges that require prompt solutions. One of them is developing new forecasting models and methods for key resulting indicators of agricultural development and have an advantage over existing models. To improve the accuracy of forecasting models, it is necessary to rely on a broad range of available statistical indicators and new modern econometric tools. The paper presents a set of methodological developments for modeling and forecasting crop yields based on the use of new econometric models that allow working with a truncated regression by limiting the range of possible negative values, statistical estimations of the introduced indicators that focus on the ecological component, as well as structural and general economic indicators. The suggested models allow obtaining more accurate forecasts compared to traditional popular models based on the least squares method. The work relies on Rosstat data for 100 agricultural fields located in municipalities of 43 regions of Russia, selected in proportion to the volume of crop production in this region. The results of this study are of interest to international and Russian organizations of various levels, whose activities are related to the issues of making managerial decisions aimed at ensuring food security of the country, improving the level and quality of life of the population, as well as organizations designed to provide modern conditions for farming on the ground.
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Zholudeva, Vera V. "Econometric modeling of the higher education system in Yaroslavl region." Open Education 22, no. 4 (August 28, 2018): 12–20. http://dx.doi.org/10.21686/1818-4243-2018-4-12-20.
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The objective of the study is to analyze the models that describe the processes, running in the education. The article concludes that currently there are important changes and new trends in the sphere of higher education in Russia: the development of higher education is carried out in the conditions of the effective use of modern information technologies. The author emphasized the analysis of the use of distance learning technologies in the higher education system, which is especially important for our country because of the vast territory, the remoteness of many regions from the centers of educational services, due to the growing high cost of these services.The development of Internet technologies, multimedia in conjunction with the growing popularity, the Internet makes it possible to promote education to a new level. That is why today the demand for distance learning in Russia is equal, and in some universities has exceeded the demand for full-time education. In the near future distance learning will take on the main burden in the system of professional training and retraining of specialists due to its mobility, mass, availability and relative cheapness.Also in this article the basic quantitative regularities of the market of higher education of the Yaroslavl region in relation to the economy are determined. In the article, econometric modeling is chosen as a tool for management in the field of vocational education. This is due to the fact that it is able to identify trends and patterns of changes in the indicators of education development in the region, to determine the consequences of a development strategy that contributes to the understanding of the processes taking place in the higher education system. Econometric models, used for forecasting in the education system are analyzed; their advantages and disadvantages are revealed. Some of them are disclosed in the paper on the example of modeling the system of higher education in the Yaroslavl region.As the result of analyzing the statistical data of the regional office of Federal State Statistics Service in Yaroslavl region the following models were developed: a model that shows how the application of distance technologies in higher education is related to socio-economic indicators; the regression model of correlation between the system of higher education and the economy (GRP); the model of forecasting the number of students in different educational categories; the econometrical model of connectivity between the education expenditures and economic factors. The paper evaluates the impact of educational and demographic indicators on the education level index of the Yaroslavl region. The econometrical models, constructed in the research, represent the informational basis for modernization of regional higher education system and elaboration of social-economic strategies of the regional development. The proposed statistical tools of evaluation and forecasting education system development can be used for decision-making and planning on the regional level.
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Alwee, Razana, Siti Mariyam Hj Shamsuddin, and Roselina Sallehuddin. "Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/951475.
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Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
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Prevedouros, Panos D. "Origin-Specific Visitor Demand Forecasting at Honolulu International Airport." Transportation Research Record: Journal of the Transportation Research Board 1600, no. 1 (January 1997): 18–27. http://dx.doi.org/10.3141/1600-03.
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The development of a PC-based and easy-to-use-and-update econometric model system for forecasting arrivals at the Honolulu International Airport is presented. A model system instead of a single model was designed so that differential growth rates from various origins as well as arrivals affected by curfews at the origin or the destination, or both, can be estimated. The airport system of the state facilitates the only mode of transportation into and out of Hawaii. Planning based on reliable demand forecasts is therefore essential. Separate models of arrivals from Australia and New Zealand, Canada, Germany, Korea, and the United Kingdom were specified and estimated using the Cochrane-Orcutt regression method. Several diagnostic tests were employed to arrive at the final models, as problems of correlation (over time) and collinearity (among variables) were present. Independent variables include the gross domestic product, population, monetary exchange rate, and unemployment rate of the origin countries. Historical values for the independent variables were taken from the publications of international organizations. Variables for wars that tend to affect flying security and natural disasters in Hawaii that affect the supply of tourist accommodations were included in the model specifications.
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Doszyń, Mariusz. "Forecasting Randomly Distributed Zero-Inflated Time Series." Folia Oeconomica Stetinensia 17, no. 1 (June 27, 2017): 7–19. http://dx.doi.org/10.1515/foli-2017-0001.
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Abstract The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.
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Hoque, Asraul, and Abdul Latif. "Forecasting exchange rate for the Australian dollar via-à-vis the US dollar using multivariate time-series models." Applied Economics 25, no. 3 (March 1993): 403–7. http://dx.doi.org/10.1080/00036849300000047.
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Yurchenko, Nataliia. "Theoretical and methodological fundamentals of determination of content and areas of application of socio-economic, political, economic-mathematical models." FOOD RESOURCES 9, no. 16 (June 25, 2021): 279–96. http://dx.doi.org/10.31073/foodresources2021-16-26.
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The subject of research - socio-economic, political and economic-mathematical models. The aim of the article is to study the theoretical and methodological principles of determining the content of categories and areas of application of socio-economic, political, economic and mathematical models in Ukraine and the world. The article analyzes the process of formation of the socio-economic model of the state and the factors that affect it, as well as compares the main types of modern socio-economic models: welfare state, minimal state, developmental state). The advantages and disadvantages of these models, their characteristics, examples and results of their application in different countries at different stages of development were identified. It is shown that the success of state-building processes depends on the type of socio-economic model used by the country. It was found that in the modern world there are no socio-economic models in "pure" form and "classical" models contain elements of other models. The thesis is confirmed that periodic adjustment or even change of models is justified. The main models of political and managerial relations in the system of public administration are analyzed. The political model inherent in Ukraine has been determined. The necessary preconditions for the stable development of political relations have been established. The article analyzes the modern economic and mathematical apparatus for modeling and forecasting the performance of the state budget and macroeconomic indicators of the country. Econometric methods that are quite common in the practice of modeling and forecasting of financial and economic activities are directly considered and compared; normative balance methods (model of inter-sectoral balance); expert systems, among which we can distinguish systems based on fuzzy logic; artificial neural networks; simulation models (Monte Carlo method, system-dynamic modeling); models of general economic equilibrium. Based on the research, the advantages and disadvantages of the methods used in this area are highlighted, and a number of proposals for their application in the economy of Ukraine are formulated.
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Menculini, Lorenzo, Andrea Marini, Massimiliano Proietti, Alberto Garinei, Alessio Bozza, Cecilia Moretti, and Marcello Marconi. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices." Forecasting 3, no. 3 (September 15, 2021): 644–62. http://dx.doi.org/10.3390/forecast3030040.
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Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.
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Boiko, Vitalii, Olha Mulska, Ihor Baranyak, and Olha Levytska. "Ukrainian Migration Aspirations towards Germany: Analysis and Development Scenarios." Comparative Economic Research. Central and Eastern Europe 24, no. 1 (March 30, 2021): 65–84. http://dx.doi.org/10.18778/1508-2008.24.04.
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Based on the multiple regression model and scenario approach to forecasting, the article estimates the Ukrainian migration aspirations towards Germany (the scale of migration, the economic activity of migrants, and their economic benefits). It is argued that major transformations in the gender-age structure of the German population may cause a demographic crisis and labour market imbalances. Our projections indicate the growing role of foreign human resources in the German economy. When modeling the scale of emigration from Ukraine, an integrated approach is applied, considering not only trends of pull-push factors but also special aspects of the German migration policy and the outflow of 8–10 million Ukrainian migrant workers. Given the poor statistical data on the scale of labour emigration needed for constructing reliable econometric models, the use of expert forecasting method remains the most optimal technique for assessing potential migration flows and migration systems.
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Басовский, Леонид, Leonid Basovskiy, Д. Ломовцев, D. Lomovcev, Елена Басовская, and Elena Basovskaya. "Forecasting the Transition of the Economy of the Russian Federation to the Dominance of the Fifth Techno-Economic Paradigm." Scientific Research and Development. Economics 5, no. 5 (November 1, 2017): 4–8. http://dx.doi.org/10.12737/article_59e5d4d58ae9a9.77383979.
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The purpose of the work was to assess the timing of the transition of the Russian economy and the economy of individual Russian regions to the dominance of the fifth techno-economic paradigm. By constructing econometric models of economic dynamics, the timing of the beginning of the dominance of the fifth technoeconomic paradigm was estimated. The transition to the domination of the fifth techno-economic paradigm in the economy of the country can expected in 2040. The economy of individual regions has already made the transition to the dominance of the fifth techno-economic paradigm. In the economy of a large part of the regions, the transition to the dominance of the fifth techno-economic paradigm can expected in many years and decades. Estimates of the expected timeframe for the transition to the dominance of the fifth techno-economic paradigm obtained on basis of the assumption that the trends of economic development prevailing in 2001–2015 were preserved.
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Басовский, Леонид, Leonid Basovskiy, Татьяна Аверина, Tatyana Averina, Елена Басовская, Elena Basovskaya, Андрей Шишкин, and Andrey Shishkin. "Forecasting the Transition of the Economy of the Russian Federation to the Dominance of the Fifth Techno-Economic Paradigm." Scientific Research and Development. Economics 5, no. 6 (January 10, 2018): 10–14. http://dx.doi.org/10.12737/article_5a2a53c74ee378.59187347.
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The purpose of the work was to assess the timing of the transition of the Russian economy and the economy of individual regions of Russia to the dominance of the fifth techno-economic paradigm. The contribution of the fifth technical and economic paradigm in the per capita GDP is given. Based on econometric models of economic dynamics, the time of the beginning of the domination of the fifth techno-economic paradigm in the economy of regions is estimated. The economy of individual regions has already passed to the domination of the fifth technoeconomic paradigm. In the economy of a large part of the regions, the transition to the dominance of the fifth techno-economic paradigm can expected many years and decades hence. Estimates of the expected timeframe for the transition to the dominance of the fifth techno-economic paradigm are received, based on the assumption of the preservation of economic development trends in 2001–2015.
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Maciejowska, Katarzyna, Weronika Nitka, and Tomasz Weron. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits." Energies 12, no. 4 (February 16, 2019): 631. http://dx.doi.org/10.3390/en12040631.
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Recently, a dynamic development of intermittent renewable energy sources (RES) has been observed. In order to allow for the adoption of trading contracts for unplanned events and changing weather conditions, the day-ahead markets have been complemented by intraday markets; in some countries, such as Poland, balancing markets are used for this purpose. This research focuses on a small RES generator, which has no market power and sells electricity through a larger trading company. The generator needs to decide, in advance, how much electricity is sold in the day-ahead market. The optimal decision of the generator on where to sell the production depends on the relation between prices in different markets. Unfortunately, when making the decision, the generator is not sure which market will offer a higher price. This article investigates the possible gains from utilizing forecasts of the price spread between the intraday/balancing and day-ahead markets in the decision process. It shows that the sign of the price spread can be successfully predicted with econometric models, such as ARX and probit. Moreover, our research demonstrates that the statistical measures of forecast accuracy, such as the percentage of correct sign classifications, do not necessarily coincide with economic benefits.
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Rasheed, Abdul, Muhammad Asad Ullah, and Imam Uddin. "PKR Exchange Rate Forecasting Through Univariate and Multivariate Time Series Techniques." NICE Research Journal 13, no. 4 (December 25, 2020): 49–67. http://dx.doi.org/10.51239/nrjss.v13i4.226.
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This study aims to examine and compare the accuracy of time series and econometric forecasting models in the context of the exchange rate as we know that fluctuation in the exchange rate may affect the economic activities at the macro – level. For this purpose, the author has chosen the Pakistani Rupee exchange rate against United States Dollars with the annual data from 1980 to 2018. The results revealed that the exponential model provides the most effective accuracy in forecasting rather than the Naive, ARIMA and ARDL Co-integration model. This paper has also covered the gap of unavailability of literature regarding the application of ARDL and Exponential Smoothing model for the forecasting of the exchange rate in Pakistan. It is also anticipated that historical data do not play a vital role in the forecasting of the future trend of time series i.e. Pakistani Rupees against US Dollars. However, all three-time series anticipated that the recent observations play a significant role in the speculation of the upcoming future trend.
Keywords: Forecasting, Exchange Rate, Naïve Model, ARDL Co-Integration model, Econometrics
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Bezverbny, Vadim A., and Sergey V. Pronichkin. "MODELING OF THE DEMOGRAPHIC AND LABOR POTENTIAL OF THE RYAZAN REGION IN THE CONTEXT OF ECONOMIC DEVELOPMENT PROBLEMS." Scientific Review. Series 1. Economics and Law, no. 4 (2020): 29–43. http://dx.doi.org/10.26653/2076-4650-2020-4-03.
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The article is devoted to the assessment and forecasting of demographic indicators, gross regional product, employment, labor force and unemployment by industry in the Ryazan region until 2025-2050. The article analyzes the trends in the demographic development of the Ryazan region, including the dynamics of fertility, mortality and migration. The consequences of population aging and the peculiarities of changes in the age and sex structure of the region's population are also considered. To solve the problem of modeling and forecasting, economic and mathematical models have been developed that include the parameters of socio-economic development. The social component is based on a systematic approach to forecasting employment, depending on the anthropogenic load index, which takes into account life expectancy and standard of living, literacy of the population, crime rate, ecological state and other indicators of socio-economic development of the region. The economic component uses econometric analysis by types of economic activities in the Ryazan region, as well as time series analysis to predict employment in both the medium and short term. In terms of the labor market, the labor force is forecasted taking into account the socio-economic effect of hidden unemployment. In conclusion, forecasts are made about the dynamics of unemployment in the Ryazan region and the influence of demographic factors on the formation of the labor force.
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Akberdina, V. V., and O. P. Smirnova. "Institutional Mechanism for Ensuring Economic Security and Investment in an Inter-Sectoral Regional Complex." Economics, taxes & law 11, no. 6 (December 26, 2018): 121–30. http://dx.doi.org/10.26794/1999-849x-2018-11-6-121-130.
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The relevance of the research is caused by the need to form a high-quality concept of a system for management and forecasting of the socio-economic development of the sectoral and inter-sectoral complex of a region. The subject of the research is the methods for forecasting the economic security under conditions of uncertainty. The purpose of the research was to consider and evaluate a model of dynamic integration of economic security for a regional inter-sectoral complex under uncertainty conditions based on the institutional approach principles. The paper analyzes the vectors of structural changes in the regional inter-sectoral complex that includes the mining, manufacturing and construction industries. A methodology for predicting the impact of the digital economy on the economic security of the regional inter-sectoral complex is considered. A new model of forecasting the economic security of the above-mentioned complex was built to be used as a tool for regulating the socio-economic development at the regional level. A set of key conditions for the development of an institutional mechanism to ensure the economic security of the region in terms of its sustainable operation and withstanding internal and external threats and risks were formed. In turn, the institutional concept of the economic security mechanism involves constant selection, analysis and evaluation of judgments about the economic security of a region, country or economic entity. This task is achieved by comparing the numerous characteristics of economic activity. The paper concludes that the proposed method of forecasting using econometric models makes it possible to assess the economic security of a regional inter-sectoral complex and timely respond to negative performance indicators.
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DeJong, David N., and Charles H. Whiteman. "Modeling Stock Prices without Knowing How to Induce Stationarity." Econometric Theory 10, no. 3-4 (August 1994): 701–19. http://dx.doi.org/10.1017/s0266466600008732.
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Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and alternative views are easily adopted. Viewed in light of relatively tight prior distributions that have proved useful in forecasting, the present-value model seems at odds with the data. Researchers less certain of the interaction between dividends and prices would find little reason to look beyond the present-value model.
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Srinivasaiah, Rashmi, Devappa Renuka Swamy, Aswin S. Krishna, Chandrashekar Vinayak Airsang, Dinesh C. Reddy, and Jayanth S. Shekar. "Various Models Used in Analysing Municipal Solid Waste Generation–A Review." Journal of Solid Waste Technology and Management 47, no. 3 (August 1, 2021): 569–78. http://dx.doi.org/10.5276/jswtm/2021.569.
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At present, factors such as growth in population, economic development, urbanization and improved standard of living increase the quantity and complexity of generated Municipal Solid Waste. The different approaches for developing models for forecasting municipal solid waste generation have been classified into conventional and non-conventional or artificial intelligence models. While the conventional models include sample survey, system dynamics, econometric models, time series analysis, factor driven models and multiple linear regression models, the non-conventional models include artificial neural networks, Fuzzy logic models and Adaptive Neuro Fuzzy Inference System models. In this review, various factors considered for modelling, locations of study, sources of data and various studies conducted by researchers have been tabulated in detail for identifying the major factors and models used in developed and developing countries. Non-conventional models are being preferred because of their capacity to analyse dynamic data and for their prediction accuracy.
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Chu, Xiaoquan, Yue Li, Dong Tian, Jianying Feng, and Weisong Mu. "An optimized hybrid model based on artificial intelligence for grape price forecasting." British Food Journal 121, no. 12 (November 21, 2019): 3247–65. http://dx.doi.org/10.1108/bfj-06-2019-0390.
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Purpose The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape. Design/methodology/approach The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems. Findings The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance. Practical implications This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well. Originality/value The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.
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Dulambayeva, R. T., and G. D. Yessenzholova. "Prospects for the Development of Foreign Trade of Kazakhstan with the Countries of the Regional Comprehensive Economic Partnership." Economics: the strategy and practice 17, no. 2 (June 30, 2022): 160–77. http://dx.doi.org/10.51176/1997-9967-2022-2-160-177.
Full textAbstract:
The study is devoted to the study of the prospects for the development of Kazakhstan’s foreign trade with the countries of the Comprehensive Regional Economic Partnership (CREP) by analyzing the indicators of the openness of the economy and the use of econometric forecasting models. The relevance of the work is due to the need to consider the prospects for the development of Kazakhstan’s foreign trade relations with the new trade association CREP for further formation of directions for the development of such relations based on the principles of evidence-based policy, since the results obtained in the study can serve as a basis for making public management decisions in the field of trade and customs policy. Within the framework of the article, the authors applied a strategy of combined research methods with a comparative analysis of the results of using each of them. Thus, econometric methods of Box-Jenkins forecasting, exponential smoothing with the use of tests to check their adequacy and validity were used. Forecast errors are calculated, such as the root-mean-square error, the average absolute error and the average absolute error as a percentage. The study resulted in forecasts of Kazakhstan’s foreign trade turnover with the CREP countries for the short term, which allowed us to conclude about the positive dynamics of the development of these trade relations. A more accurate forecast is presented based on the use of the exponential smoothing method, which demonstrates the smallest error size and is easier to apply.