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How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan

Author

Listed:
  • Chia-Lin Chang

    (Department of Applied Economics, National Chung Hsing University)

  • Philip Hans Franses

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

Abstract

A government's ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially.

Suggested Citation

  • Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010. "How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan," KIER Working Papers 720, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:720
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    Cited by:

    1. Chang, Chia-Lin & de Bruijn, Bert & Franses, Philip Hans & McAleer, Michael, 2013. "Analyzing fixed-event forecast revisions," International Journal of Forecasting, Elsevier, vol. 29(4), pages 622-627.
    2. Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael, 2013. "Are forecast updates progressive?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 9-18.
    3. Franses, Ph.H.B.F. & McAleer, M.J. & Legerstee, R., 2010. "Evaluating Macroeconomic Forecast: A Review of Some Recent Developments," Econometric Institute Research Papers EI 2010-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    5. Jeffrey Frankel, 2013. "A Solution to Fiscal Procyclicality: The Structural Budget Institutions Pioneered by Chile," Central Banking, Analysis, and Economic Policies Book Series, in: Luis Felipe Céspedes & Jordi Galí (ed.),Fiscal Policy and Macroeconomic Performance, edition 1, volume 17, chapter 9, pages 323-391, Central Bank of Chile.
    6. Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010. "Evaluating Combined Non-Replicable Forecasts," Working Papers in Economics 10/74, University of Canterbury, Department of Economics and Finance.
    7. Chang, Chia Lin & Franses, Philip Hans & Mcaleer, Michael, 2012. "Evaluating Individual and Mean Non-Replicable Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 22-43, September.
    8. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2014. "Evaluating Macroeconomic Forecasts: A Concise Review Of Some Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 195-208, April.
    9. Mihaela Simionescu, 2014. "Directional accuracy for inflation and unemployment rate predictions in Romania," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 7(2), pages 129-138, September.
    10. Chang, Chun-Ping & Lee, Chien-Chiang & Hsieh, Meng-Chi, 2015. "Does globalization promote real output? Evidence from quantile cointegration regression," Economic Modelling, Elsevier, vol. 44(C), pages 25-36.
    11. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.
    12. Chang, C-L. & McAleer, M.J. & Franses, Ph.H.B.F., 2010. "Combining Non-Replicable Forecasts," Econometric Institute Research Papers EI 2010-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    14. Xie, Zixiong & Hsu, Shih-Hsun, 2016. "Time varying biases and the state of the economy," International Journal of Forecasting, Elsevier, vol. 32(3), pages 716-725.
    15. Frankel, Jeffrey, 2011. "A Solution to Overoptimistic Forecasts and Fiscal Procyclicality: The Structural Budget Institutions Pioneered by Chile," Working Paper Series 11-012, Harvard University, John F. Kennedy School of Government.
    16. Harin, Alexander, 2014. "General correcting formulae for forecasts," MPRA Paper 55283, University Library of Munich, Germany.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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