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Academic literature on the topic 'Economic forecasting Australia Econometric models'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Economic forecasting Australia Econometric models"

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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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Dissertations / Theses on the topic "Economic forecasting Australia Econometric models"

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Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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Jeon, Yongil. "Four essays on forecasting evaluation and econometric estimation /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9949690.

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Azam, Mohammad Nurul 1957. "Modelling and forecasting in the presence of structural change in the linear regression model." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/9152.

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Lazim, Mohamad Alias. "Econometric forecasting models and model evaluation : a case study of air passenger traffic flow." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296880.

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Enzinger, Sharn Emma 1973. "The economic impact of greenhouse policy upon the Australian electricity industry : an applied general equilibrium analysis." Monash University, Centre of Policy Studies, 2001. http://arrow.monash.edu.au/hdl/1959.1/8383.

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Kummerow, Max F. "A paradigm of inquiry for applied real estate research : integrating econometric and simulation methods in time and space specific forecasting models : Australian office market case study." Thesis, Curtin University, 1997. http://hdl.handle.net/20.500.11937/1574.

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Office space oversupply cost Australia billions of dollars during the 1990-92 recession. Australia, the United States, Japan, the U.K., South Africa, China, Thailand, and many other countries have suffered office oversupply cycles. Illiquid untenanted office buildings impair investors capital and cash flows, with adverse effects on macroeconomics, financial institutions, and individuals. This study aims to develop improved methods for medium term forecasting of office market adjustments to inform individual project development decisions and thereby to mitigate office oversupply cycles. Methods combine qualitative research, econometric estimation, system dynamics simulation, and institutional economics. This research operationalises a problem solving research paradigm concept advocated by Ken Lusht. The research is also indebted to the late James Graaskamp, who was successful in linking industry and academic research through time and space specific feasibility studies to inform individual property development decisions. Qualitative research and literature provided a list of contributing causes of office oversupply including random shocks, faulty forecasting methods, fee driven deals, prisoners dilemma game, system dynamics (lags and adjustment times), land use regulation, and capital market issues. Rather than choosing among these, they are all considered to be causal to varying degrees. Moreover, there is synergy between combinations of these market imperfections. Office markets are complex evolving human designed systems (not time invariant) so each cycle has unique historical features. Data on Australian office markets were used to estimate office rent adjustment equations. Simulation models in spreadsheet and system dynamics software then integrate additional information with the statistical results to produce demand, supply, and rent forecasts. Results include models for rent forecasting and models for analysis related to policy and system redesign. The dissertation ends with two chapters on institutional reforms whereby better information might find application to improve market efficiency.Keywords. Office rents, rent adjustment, office market modelling, forecasting, system dynamics.
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Marshall, Peter John 1960. "Rational versus anchored traders : exchange rate behaviour in macro models." Monash University, Dept. of Economics, 2001. http://arrow.monash.edu.au/hdl/1959.1/9048.

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Steinbach, Max Rudibert. "Essays on dynamic macroeconomics." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86196.

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Thesis (PhD)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: In the first essay of this thesis, a medium scale DSGE model is developed and estimated for the South African economy. When used for forecasting, the model is found to outperform private sector economists when forecasting CPI inflation, GDP growth and the policy rate over certain horizons. In the second essay, the benchmark DSGE model is extended to include the yield on South African 10-year government bonds. The model is then used to decompose the 10-year yield spread into (1) the structural shocks that contributed to its evolution during the inflation targeting regime of the South African Reserve Bank, as well as (2) an expected yield and a term premium. In addition, it is found that changes in the South African term premium may predict future real economic activity. Finally, the need for DSGE models to take account of financial frictions became apparent during the recent global financial crisis. As a result, the final essay incorporates a stylised banking sector into the benchmark DSGE model described above. The optimal response of the South African Reserve Bank to financial shocks is then analysed within the context of this structural model.
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Silvestrini, Andrea. "Essays on aggregation and cointegration of econometric models." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210304.

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This dissertation can be broadly divided into two independent parts. The first three chapters analyse issues related to temporal and contemporaneous aggregation of econometric models. The fourth chapter contains an application of Bayesian techniques to investigate whether the post transition fiscal policy of Poland is sustainable in the long run and consistent with an intertemporal budget constraint.

Chapter 1 surveys the econometric methodology of temporal aggregation for a wide range of univariate and multivariate time series models.

A unified overview of temporal aggregation techniques for this broad class of processes is presented in the first part of the chapter and the main results are summarized. In each case, assuming to know the underlying process at the disaggregate frequency, the aim is to find the appropriate model for the aggregated data. Additional topics concerning temporal aggregation of ARIMA-GARCH models (see Drost and Nijman, 1993) are discussed and several examples presented. Systematic sampling schemes are also reviewed.

Multivariate models, which show interesting features under temporal aggregation (Breitung and Swanson, 2002, Marcellino, 1999, Hafner, 2008), are examined in the second part of the chapter. In particular, the focus is on temporal aggregation of VARMA models and on the related concept of spurious instantaneous causality, which is not a time series property invariant to temporal aggregation. On the other hand, as pointed out by Marcellino (1999), other important time series features as cointegration and presence of unit roots are invariant to temporal aggregation and are not induced by it.

Some empirical applications based on macroeconomic and financial data illustrate all the techniques surveyed and the main results.

Chapter 2 is an attempt to monitor fiscal variables in the Euro area, building an early warning signal indicator for assessing the development of public finances in the short-run and exploiting the existence of monthly budgetary statistics from France, taken as "example country".

The application is conducted focusing on the cash State deficit, looking at components from the revenue and expenditure sides. For each component, monthly ARIMA models are estimated and then temporally aggregated to the annual frequency, as the policy makers are interested in yearly predictions.

The short-run forecasting exercises carried out for years 2002, 2003 and 2004 highlight the fact that the one-step-ahead predictions based on the temporally aggregated models generally outperform those delivered by standard monthly ARIMA modeling, as well as the official forecasts made available by the French government, for each of the eleven components and thus for the whole State deficit. More importantly, by the middle of the year, very accurate predictions for the current year are made available.

The proposed method could be extremely useful, providing policy makers with a valuable indicator when assessing the development of public finances in the short-run (one year horizon or even less).

Chapter 3 deals with the issue of forecasting contemporaneous time series aggregates. The performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA (VARMA) processes is compared. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is a predictor obtained from aggregation of univariate forecasts for the individual components of the data generating vector process.

The econometric framework is broadly based on Lütkepohl (1987). The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods in the bivariate VMA(1) case is provided. It is argued that the condition of equality of predictors as stated in Lütkepohl (1987), although necessary and sufficient for the equality of the predictors, is sufficient (but not necessary) for the equality of mean squared errors.

Furthermore, it is shown that the same forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure.

Finally, an empirical application that involves the problem of forecasting the Italian monetary aggregate M1 on the basis of annual time series ranging from 1948 until 1998, prior to the creation of the European Economic and Monetary Union (EMU), is presented to show the relevance of the topic. In the empirical application, the framework is further generalized to deal with heteroskedastic and cross-correlated innovations.

Chapter 4 deals with a cointegration analysis applied to the empirical investigation of fiscal sustainability. The focus is on a particular country: Poland. The choice of Poland is not random. First, the motivation stems from the fact that fiscal sustainability is a central topic for most of the economies of Eastern Europe. Second, this is one of the first countries to start the transition process to a market economy (since 1989), providing a relatively favorable institutional setting within which to study fiscal sustainability (see Green, Holmes and Kowalski, 2001). The emphasis is on the feasibility of a permanent deficit in the long-run, meaning whether a government can continue to operate under its current fiscal policy indefinitely.

The empirical analysis to examine debt stabilization is made up by two steps.

First, a Bayesian methodology is applied to conduct inference about the cointegrating relationship between budget revenues and (inclusive of interest) expenditures and to select the cointegrating rank. This task is complicated by the conceptual difficulty linked to the choice of the prior distributions for the parameters relevant to the economic problem under study (Villani, 2005).

Second, Bayesian inference is applied to the estimation of the normalized cointegrating vector between budget revenues and expenditures. With a single cointegrating equation, some known results concerning the posterior density of the cointegrating vector may be used (see Bauwens, Lubrano and Richard, 1999).

The priors used in the paper leads to straightforward posterior calculations which can be easily performed.

Moreover, the posterior analysis leads to a careful assessment of the magnitude of the cointegrating vector. Finally, it is shown to what extent the likelihood of the data is important in revising the available prior information, relying on numerical integration techniques based on deterministic methods.


Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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Ben-Belhassen, Boubaker. "Econometric models of the Argentine cereal economy : a focus on policy simulation analysis /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842508.

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Books on the topic "Economic forecasting Australia Econometric models"

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Adaptation and survival in Australian agriculture: A computable general equilibrium analysis of the impact of economic shocks originating outside the domestic agricultural sector. Melbourne: Oxford University Press, 1986.

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Econometric and forecasting models. Lewiston, N.Y: Edwin Mellen, 2013.

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L, Rubinfeld Daniel, ed. Econometric models and economic forecasts. 4th ed. Boston, Mass: Irwin/McGraw-Hill, 1998.

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Pindyck, Robert S. Econometric models and economic forecasts. 3rd ed. New York: McGraw-Hill, 1991.

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Green, Rodney D. Forecasting with computer models: Econometric, population, and energy forecasting. New York: Praeger, 1985.

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Forecasting with computer models: Econometric, population, and energy forecasting. New York: Praeger, 1985.

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Green, Rodney D. Forecasting with computer models: Econometric, population, and energy forecasting. New York: Praeger, 1985.

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Harrison, Richard. Forecasting with measurement errors in dynamic models. London: Bank of England, 2004.

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Macro-economic forecasting: A sociological appraisal. London: Routledge, 1999.

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Pindyck, Robert S. Econometric models and economic forecasts. 4th ed. Boston, MA: McGraw, 1998.

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Book chapters on the topic "Economic forecasting Australia Econometric models"

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Buckmann, Marcus, Andreas Joseph, and Helena Robertson. "Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting." In Data Science for Economics and Finance, 43–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_3.

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AbstractWe present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.
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"Forecasting with Econometric Models." In Economic Forecasting: The State of the Art, 134–58. Routledge, 2016. http://dx.doi.org/10.4324/9781315480695-14.

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Geda, Alemayehu, Fredrik Huizinga, and Addis Yimer. "Exogenous Shocks and Macroeconomic Policy Analysis using Applied Macro-Econometric Models in Africa." In Economic Modeling, Analysis, and Policy for Sustainability, 74–129. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0094-0.ch006.

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In this study we have developed a macro-econometric model for a typical supply constrained African economy. This is aimed at developing a theoretical and empirical template for such policy tools which are increasingly demanded in Africa. We have concretized it by building a macro-econometric model for Rwanda. The Rwanda macro-econometric model has 107 equations of which 72 are endogenous. In addition, a supplementary ARIMA based model with 33 equations for exogenous variable is built to make the model useful for forecasting. The fiscal, balance of payment and money supply block of the model is fairly disaggregated to offer an adequate picture of the macro economy. An econometric estimation of the core behavioral equations of the model using equilibrium [error]-correction approach is made with the database that stretches from 1960 to 2009. The model is similar to successful macro models in the region such as that of the KIPPRA-Treasury model of Kenya. It can also easily be further extended to the support budgeting, forecasting and macroeconomic policy analysis work at the relevant ministries in Africa such as the Ministry of Finance in Rwanda. We have managed to successfully solve the model from 1999 to 2009 and forecast major macro outcomes from 2010 to 2014. We have also used it to conduct a policy simulation exercise which is very important for policy makers such as those in Rwanda. We hope this model offers a theoretical and empirical framework for building macro model across Africa which is increasingly being demanded in many countries.
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CHRISTODOULAKIS, N. M. "EXTENSIONS OF LINEARISATION TO LARGE ECONOMETRIC MODELS WITH RATIONAL EXPECTATIONS††The help received from P. Levine, S. Holly and F. Breedon, all at the Centre for Economic Forecasting of London Business School, to run the model, is gratefully acknowledged." In System-Theoretic Methods in Economic Modelling II, 629–42. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-08-037932-6.50016-7.

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Conference papers on the topic "Economic forecasting Australia Econometric models"

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Anandavel, Lithicka, Ansh Sharma, Naveenkumar S., Suresh Sankaranarayanan, and Anis Salwa Binti Mohd Khairuddin. "Intelligent Demand Forecasting Using Deep Learning." In International Technical Postgraduate Conference 2022. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.141.7.

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One type of energy demand is the electricity demand, which measures the electricity consumption Wh (watt-hour). Forecasting this electricity demand is very crucial and plays a fundamental role in the electrical industry, as it provides the basis for making decisions in the operation and planning procedures of power systems. Forecasting is important for development experts and are of great interest to energy authorities, power utilities, and private investors. Inaccurate projections can have disastrous social and economic implications, whether they over-or under-predict demand. Supply shortages and forced power outages occur from underestimating demand, wreaking havoc on productivity and economic growth. Overestimating demand can result in overinvestment in generation capacity, financial hardship, and, eventually, higher power costs. This paper has validated several methodologies such as ARIMA, XGBOOST, LSTM and Bi-LSTM towards forecasting the energy demand for different regions of Australia during different season. The models were validated towards energy demand forecasting in terms of error and accuracy resulting in LSTM with 2 layers outperforming the other models.
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Lleshaj, Llesh. "Volatility Estimation of Euribor and Equilibrium Forecasting." In 7th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2021. http://dx.doi.org/10.31410/eraz.2021.171.

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Abstract:
Euribor rates (Euro Interbank Offered Rate) rates are considered to be the most important reference rates in the European money market. The interest rates do provide the basis for the price and interest rates of all kinds of financial products like interest rate swaps, interest rate futures, saving accounts and mortgages. Since September 2014, this index has per­formed with negative rates. In recent years, several European central banks have imposed negative interest rates on commercial banks, as the only way to stimulate their nations’ economies. Under these circumstances, the purpose of this study is to estimate the gap of the negative rates which are still increasing constantly. This fact puts in question the financial stability in many countries and the effect of monetary policy on stimulating economic growth around European countries. According to the daily data 2016 - 2021, this study has analyzed the volatility of the Euribor index related to efficient market hypothesis and volatility clustering. Applying advanced volatility econometric methods, GARCH volatility models are derived and the long-run equilibrium is predicted. Practical Implications are related to the empiri­cal impacts that ought to be taken into consideration by the banking sector and other financial institutions to make decisions with the Euribor index.
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