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Vogelsang, Timothy J. "Economic Forecasting." Journal of the American Statistical Association 96, no. 453 (March 2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s386.
Elliott, Graham, and Allan Timmermann. "Economic Forecasting." Journal of Economic Literature 46, no. 1 (February 1, 2008): 3–56. http://dx.doi.org/10.1257/jel.46.1.3.
Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives. Decision theory provides a framework for both the construction and evaluation of forecasts. This framework allows an understanding of the challenges that arise from the explosion in the sheer volume of predictor variables under consideration and the forecaster's ability to entertain an endless array of forecasting models and time-varying specifications, none of which may coincide with the “true” model. We show this along with reviewing methods for comparing the forecasting performance of pairs of models or evaluating the ability of the best of many models to beat a benchmark specification.
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Суворов, Anatoliy Suvorov, Ивантер, Viktor Ivantyer, Сутягин, and Valyeriy Sutyagin. "The Main Objectives and Principles of Socio-Economic Forecasting." Administration 3, no. 1 (March 17, 2015): 8–17. http://dx.doi.org/10.12737/8785.
The methodological foundations of socio-economic forecasting are considered in this paper. The forecasting’s
definition is given; the forecasting’s role and place in national economy regulation are considered. Forecasts
types’ classification and the forecasting’s basic principles have been presented. Forecasting models’
structure and classification, as well as socio-economic forecasts’ elements and development stages have
been considered.
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Clements, Michael P., and David F. Hendry. "Forecasting economic processes." International Journal of Forecasting 14, no. 1 (March 1998): 111–31. http://dx.doi.org/10.1016/s0169-2070(97)00057-5.
Hall, Stephen G., K. Holden, D. A. Peel, and J. L. Thompson. "Economic Forecasting: An Introduction." Economica 59, no. 233 (February 1992): 132. http://dx.doi.org/10.2307/2555081.
Swanson, Norman R., Michael P. Clements, and David F. Hendry. "Forecasting Economic Time Series." Journal of the American Statistical Association 95, no. 450 (June 2000): 687. http://dx.doi.org/10.2307/2669429.
This thesis consists of three chapters on forecasting techniques in economics. In chapter 1, I use copulas to estimate multivariate density forecasts based on univariate densities from survey data. Survey-based predictions are often competitive to time series models in their forecasting performance but have a univariate focus and my estimation strategy exploits the information in the surveys’ marginal densities. I subsequently demonstrate the importance of the multivariate aspect for forecasters. In chapter 2, we propose novel tests for forecast rationality, which are robust under the presence of Markov switching. Existing tests focus on constant out-of-sample performances or use non-parametric techniques; consequently, they may lack power against the alternative of discrete switches. Investigating the Blue Chip Fi-nancial Forecasts, we find evidence against forecast unbiasedness during periods of monetary easing. Chapter 3 provides an empirical investigation of the real-time forecasting performance of quantile regressions for predicting diferent vintages of real US GDP growth. My results indicate that quantile regressions are competitive to current benchmark models and that the insample estimation strategy matters for the performance concerning difrent data vintages. Esta tesis consta de tres capítulos sobre métodos predictivos en economía. El primer capítulo propone el uso de cópulas para la elaboración de previsiones de distribuciones multivariantes utilizando datos de encuestas sobre distribuciones univariantes. Las previsiones basadas en sondeos son, a menudo, equiparables a las obtenidas por modelos de series temporales, pero sólo hay datos disponibles para distribuciones univariantes. La estrategia de estimación propuesta utiliza la información de las distribuciones univariantes de los sondeos. Posteriormente queda demostrada la importancia de la perspectiva multivariante en la elaboración de previsiones. El segundo capítulo propone nuevos tests para evaluar la racionalidad de las previsiones, los cuales, resultan sólidos bajo la presencia de Markov switching. En comparación, los tests existentes se centran en probar la prueba entera o usan técnicas no-paramétricas y tienen menos poder contra la alternativa de cambios discretos. Mediante la investigación empírica de la racionalidad del las previsiones del Blue Chip Financial Forecasts, se encuentra evidencia a favor de la hipótesis de un sesgo con Markov switching durante los periodos de relajación monetaria. El tercer capítulo es una investigación empírica de la eficacia del modelo de regresión de cuantiles para prever en tiempo real el crecimiento del PIB estadounidense. Los resultados obtenidos indican que dicho modelo es comparable a los modelos de referencia actuales y que la estrategia de estimación aplicada con diferentes muestras de datos influye los resultados.
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Souza, André B. M. "Essays in economic forecasting." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672997.
This dissertation consists of two independent chapters on economic and financial
forecasting. The first chapter introduces a nonlinear forecasting framework
that combines forecasts of the sign and absolute value of a time series into conditional
mean forecasts. In contrast to linear models, the proposed framework
allows different predictors to separately impact the sign and absolute value of the
target series. An empirical application using the FRED-MD dataset shows that
forecasts from the proposed model substantially outperform linear forecasts for
series that exhibit persistent volatility dynamics, such as output and interest rates.
The second chapter, coauthored with Christian Brownlees, provides an extensive
comparison of methods to forecast downside risks to GDP growth for a panel of
24 OECD economies. We consider forecasts constructed from standard quantile
regressions as well as from conditional volatility models. Our evidence suggests
that standard volatility models such as the GARCH(1,1) are at least as accurate as
quantile regressions. Aquesta dissertació consta de dos capítols independents sobre previsió econòmica
i financera. El primer capítol introdueix un modelo de predicció
no lineal que
combina les previsions del signe i del valor absolut d’una sèrie temporal en previsions mitjanes
condicionals. A diferència dels models lineals, el modelo proposat permet que diferents variables
afectin per separat el signe i el valor absolut de la sèrie d’interés. Una aplicació empírica
que utilitza el conjunt de dades FRED-MD mostra que les previsions basades en el modelo proposat
superen substancialment les previsions lineals per a sèries que presenten dinàmiques de
volatilitat persistents, com la producció industrial i els tipus d’interès. El segon capítol,
coautorado con Christian Brownlees, proporciona una àmplia comparació
de mètodes
per predir els riscos negatius per al creixement del PIB per a un grup de 24 economies de l’OCDE.
Considerem les previsions construïdes a partir de regressions quàntils estàndard, així com a
partir de models de volatilitat condicional. La nostra evidència suggereix que els models de
volatilitat, com el GARCH (1,1), són almenys tan precisos com les regressions quantils.
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Acar, Emmanuel. "Economic evaluation of financial forecasting." Thesis, City University London, 1993. http://openaccess.city.ac.uk/8256/.
This thesis examines the economic evaluation of forecasting strategies based on past prices, bringing together academics and practitioners techniques Forecasting methods based on past prices are convex and path-dependent dynamic strategies Therefore, they must be able to profitably exploit positive serial dependences in financial prices The most important measure of financial forecasting ability is the rate of return achieved by the predictor The expected return of forecasting strategies is first investigated by applying stochastic modelling Then, the presence of serial dependences in financial prices is tested by comparing expected and observed rates of returns of forecasting strategies According to the academic literature, the expected return of investment strategies is best established by applying stochastic modelling That is done analytically for linear forecasters, assuming that the underlying process of asset returns is not only a random walk with drift but any Gaussian processes The rate of return from financial strategies is zero under the assumption of a random walk without drift, and non-zero in all the other cases Then, it is shown that many forecasting techniques used by market participants are in fact linear forecasters and consequently fall in the scope of this study. Minimising the mean squared error is a sufficient but not necessary condition to maximise returns Under the random walk without dnft assumption, error measures and profits arenegatively correlated but very few in absolute value Only the directional accuracy exhibits high degree of linear association with profits When the true Gaussian process is not known, there are cases for which a decrease in mean squared error does not imply an increase in returns Therefore the mean squared error criterion is of poor use to maximise returns when the true model is not known The directional accuracy is of no further help Market timing ability tests based on the percentage of correct forecasts have very low power in presence of low positive autocorrelations. It is why a test of the random walk hypothesis based on the joint profitability of trading rules is investigated It happens to be powerful against a broad range of linear alternatives Its ruee feature is to exhibit a power almost equal to the best of its components unknown when the true model is unknown It constitutes as well a tool to separate mean from variance non-hnear models Simple tests of adequacy of Gaussian processes are subsequently proposed from the joint profitability of trading rules Applying previous tests, the random walk hypothesis is rejected for daily exchange rates against Dollar, over the period 1982-1992 The hypothesis of normal underlying returns is very weak compared to the independence assumption Among a few Gaussian processes, the price-trend model along with some technical models appear to be the best alternatives to explain observed trading rule returns Statistical forecasters based either on ARMA(1,1) or fractional Gaussian processes do not outperform simple technical rules Taking Into account transaction costs reduce profits to zero for individual but not for institutional Investors who might have to act on strategies that assume the foreign exchange markets exhibit positive dependencies, if not inefficiencies.
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Bezsmertna, Julia. "Modern methods of economic forecasting." Thesis, Київський національний університет технологій та дизайну, 2019. https://er.knutd.edu.ua/handle/123456789/14350.
La prévision macroéconomique à court terme est un exercice aussi complexe qu’essentiel pour la définition de la politique économique et monétaire. Les crises financières récentes ainsi que les récessions qu’ont endurées et qu’endurent aujourd’hui encore, en ce début d’année 2014, nombre de pays parmi les plus riches, témoignent de la difficulté d’anticiper les fluctuations économiques, même à des horizons proches. Les recherches effectuées dans le cadre de la thèse de doctorat qui est présentée dans ce manuscrit se sont attachées à étudier, analyser et développer des modélisations pour la prévision de croissance économique. L’ensemble d’informations à partir duquel construire une méthodologie prédictive est vaste mais également hétérogène. Celle-ci doit en effet concilier le mélange des fréquences d’échantillonnage des données et la parcimonie nécessaire à son estimation. Nous évoquons à cet effet dans un premier chapitre les éléments économétriques fondamentaux de la modélisation multi-fréquentielle. Le deuxième chapitre illustre l’apport prédictif macroéconomique que constitue l’utilisation de la volatilité des variables financières en période de retournement conjoncturel. Le troisième chapitre s’étend ensuite sur l’inférence bayésienne et nous présentons par ce biais un travail empirique issu de l’adjonction d’une volatilité stochastique à notre modèle. Enfin, le quatrième chapitre propose une étude des techniques de sélection de variables à fréquence multiple dans l’optique d’améliorer la capacité prédictive de nos modélisations. Diverses méthodologies sont à cet égard développées, leurs aptitudes empiriques sont comparées, et certains faits stylisés sont esquissés Economic downturn and recession that many countries experienced in the wake of the global financial crisis demonstrate how important but difficult it is to forecast macroeconomic fluctuations, especially within a short time horizon. The doctoral dissertation studies, analyses and develops models for economic growth forecasting. The set of information coming from economic activity is vast and disparate. In fact, time series coming from real and financial economy do not have the same characteristics, both in terms of sampling frequency and predictive power. Therefore short-term forecasting models should both allow the use of mixed-frequency data and parsimony. The first chapter is dedicated to time series econometrics within a mixed-frequency framework. The second chapter contains two empirical works that sheds light on macro-financial linkages by assessing the leading role of the daily financial volatility in macroeconomic prediction during the Great Recession. The third chapter extends mixed-frequency model into a Bayesian framework and presents an empirical study using a stochastic volatility augmented mixed data sampling model. The fourth chapter focuses on variable selection techniques in mixed-frequency models for short-term forecasting. We address the selection issue by developing mixed-frequency-based dimension reduction techniques in a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Our model succeeds in constructing an objective variable selection with broad applicability
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Franklin, Jesse C. "Forecasting the Inland Empire's Economic Recovery." Scholarship @ Claremont, 2010. http://scholarship.claremont.edu/cmc_theses/42.
The Inland Empire -Riverside and San Bernardino Counties - was one of the hardest hit areas in all of the United States during the Great Recession. Home prices have declined over 50%, significantly more than the 25% decline in the surrounding Los Angeles County, and housing starts have declined to over 90% from 2005. The Inland Empire has one of the highest unemployment rates in the US at 14.8%. This paper attempts to forecast the recovery for the Inland Empire. Employing univariate forecasts along with VAR(12) forecasts, focusing on housing starts and unemployment rates as the underlying variables, we find that there is little hope for a recovery over the next 3 years. The model predicts unemployment to either rise even more or, at best, remain stagnant. Housing starts are predicted to remain constant over the next three years.
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Thomas, M. C. "Techno-economic forecasting for packaging materials." Thesis, Swansea University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639223.
Forecasting packaging material demand is crucial to effective future planning by capital intensive material manufacturers. Yet several considerations introduce great uncertainty over the future packaging mix. Foremost is a highly heterogeneous and dynamic end-use marketplace subject to multitudinous technological, economic, consumer and legislative change-forces. These act at all levels upon a diverse and complex supply chain that suffers data paucity and, hence, opacity of cause and effect. A wide range of future-oriented decision technologies was examined to meet these challenges. None promise competitive advantage over commissioned forecasts of aggregate demand. At the sector level, the petfood market is relatively homogeneous and simple, but nonetheless significant. Neural network analysis of its causal relationships led to rich results and a simple, workable causal-forecasting model. Data paucity inspired three key development paths. First, a weakness in current implementations of genetic algorithm model input selection was exposed - result variability due to training data set division. Novel software invoked genetic algorithm input selection over exhaustive permutations of training cases to generate a result distribution, thereby partially automating model specification for wider application. Second, the neural networks were implemented in a scenario-planning spreadsheet, to isolate the more certain and less certain factors in scenario forecasting. Third, several unprecedented factors change past relationships and can undermine even the most accurately specific model. Accordingly, a Delphi survey was conducted to develop scenarios of the potential impact of remote retailing upon packaging demand. Consequently, although the five-year outlook for tinplate petfood packaging is open to interpretation, the most likely scenario is stable demand. Petfood and human food cans exhibit clear strengths in the remote-retailing scenario, but high uncertainty is envisaged for the remaining packaging applications. Such unprecedented forces should be continually monitored, and marketing activities should emphasise the strengths of tinplate in the scenarios thereby envisaged.
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Hackworth, J. F. "Forecasting the ownership growth of consumer durables." Thesis, Cranfield University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371830.
Betz, Gregor Tetens Holm. "Prediction or prophecy? the boundaries of economic foreknowledge and their socio-political consequences /." Wiesbaden : Deutscher Universitäts-Verlag, 2006. http://site.ebrary.com/id/10231757.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. Economic Forecasting and Policy. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230306448.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Sectoral Forecasting." In Economic Forecasting, 229–34. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_10.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "First Principles." In Economic Forecasting, 1–14. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_1.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Accuracy." In Economic Forecasting, 235–50. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_11.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Using the Forecasts." In Economic Forecasting, 251–64. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_12.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Communication Challenges." In Economic Forecasting, 265–74. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_13.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "A Tour of the Forecasting Institutions." In Economic Forecasting, 275–90. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_14.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Epilogue." In Economic Forecasting, 291. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_15.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "The Data." In Economic Forecasting, 15–50. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_2.
Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Incoming News and Near-Term Forecasting." In Economic Forecasting, 51–84. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_3.
Conference papers on the topic "Economic forecasting":
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Shiryaev, Mihail. "FORECASTING MODELS OF ECONOMIC DYNAMICS." In 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/14/s04.046.
Waller, Ephraim Nii Kpakpo, Pamela Delali Adablah, and Quist-Aphetsi Kester. "Markov Chain: Forecasting Economic Variables." In 2019 International Conference on Computing, Computational Modelling and Applications (ICCMA). IEEE, 2019. http://dx.doi.org/10.1109/iccma.2019.00026.
Shnaider, E., P. Hurtado, and M. Schneider. "Expert systems for economic/business forecasting." In the 1993 ACM/SIGAPP symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/162754.165215.
MAHABIR, WINSTON. "Methods of economic evaluation - Forecasting critique." In Guidance, Navigation and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4285.
Vertakova, Yulia. "GDP FORECASTING FOR PROACTIVE MANAGEMENT OF TERRITORIAL DEVELOPMENT OF ECONOMICS: OVERVIEW OF ECONOMIC APPROACHES AND FORECASTING MODELS." In 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/13/s04.105.
Grishchenko, Marina, and Mariya Tsvil. "FORECASTING THE ECONOMIC PROCESS USING ECONOMETRIC ANALYSIS." In Economy of Russia: problems, trends, forecasts. au: AUS PUBLISHERS, 2021. http://dx.doi.org/10.26526/conferencearticle_61cc296be8dee5.11017419.
The article considers the necessity of using econometric research methods in the analysis and forecasting of economic processes and phenomena. A concrete example of managerial decision making examined in the given article is based on the assumption of an econometric model and it’s ability to analyze the effectiveness of applying different types of advertising on the company's activities
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Gonçalves, Victor H., and João L. G. Rosa. "Forecasting economic time series using chaotic neural networks." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4470.
This paper describes the application of KIII, a biologically more plausible neural network model, for forecasting economic time series. K-sets are connectionist models based on neural populations and have been used in many machine learning applications. In this paper, this method was applied to IPCA, a Brazilian consumer price index surveyed by IBGE. The values ranged from August 1994 to June 2017. Experiments were performed using four non-parametric models and seven parametric methods. The statistical metric RMSE was used to compare methods performance. Freeman KIII sets worked well as a filter, but it was not a good prediction method. This paper contributes with the use of non-parametrics models for forecasting inflation in a developing country.
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Obraztsov, Sergei M., Dmitri V. Chelegatski, Inna N. Louneva, and Alexander L. Shimkevich. "Economic forecasting by the deterministic-adaptive method." In the conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/253341.253398.
Song Lingli, Deng Changhong, Xu Qiushi, and Zhou Chu. "Load forecasting considering the regional economic environment." In International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1220.
Snowberg, Erik, Justin Wolfers, and Eric Zitzewitz. Prediction Markets for Economic Forecasting. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18222.
Higgins, Patrick, Tao Zha, and Karen Zhong. Forecasting China's Economic Growth and Inflation. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22402.
Zarnowitz, Victor. The Record and Improvability of Economic Forecasting. Cambridge, MA: National Bureau of Economic Research, December 1986. http://dx.doi.org/10.3386/w2099.
Giacomini, Raffaella. Economic theory and forecasting: lessons from the literature. Cemmap, September 2014. http://dx.doi.org/10.1920/wp.cem.2014.4114.
Nenov, Iliyan, George Mengov, Kaloyan Ganev, and Ralitsa Simeonova-Ganeva. Neurocomputational Economic Forecasting with a Handful of Data. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2021. http://dx.doi.org/10.7546/crabs.2021.10.11.
Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.
This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Dolmas, Sheila, Evan F. Koenig, and Jeremy M. Piger. The Use and Abuse of 'Real-Time' Data in Economic Forecasting. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.015.
Hafer, R. W. Forecasting Economic Activity: Comparing the Accuracy of Survey and Time Series Predictions. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.012.
Leigh, Andrew, and Justin Wolfers. Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets. Cambridge, MA: National Bureau of Economic Research, February 2006. http://dx.doi.org/10.3386/w12053.
Galeano-Ramírez, Franky Juliano, Nicolás Martínez-Cortés, Carlos D. Rojas-Martínez, and Margaret Guerrero. Nowcasting Colombian Economic Activity: DFM and Factor-MIDAS approaches. Banco de la República, August 2021. http://dx.doi.org/10.32468/be.1168.
Economic policy decision-making requires constantly assessing the state of economic activity. However, this is not an easy task: official figures have significant lags, and the timely information is usually partial and has different frequencies. This paper applies two types of short-term forecasting methodologies (Factor-MIDAS and DFM) for Colombian economic activity involving information with mixed frequencies. We present a heuristic process to select relevant variables, and we evaluate the proposed models' fits by comparing them with traditional forecasting methodologies. Overall, DFM and Factor-MIDAS forecasts are better than those generated by conventional methodologies, especially as the flow of information increases. In times of COVID-19, the model with the best relative fit was the DFM.
