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The Evaluation Of Quarterly Forecast Intervals For Inflation Rate In Romania

Author

Listed:
  • Mihaela Simionescu

    (Institute for Economic Forecasting of the Romanian Academy)

  • Irina Dragan

    (Bucharest University of Economic Studies)

Abstract

The forecast uncertainty was one of the causes of the recent economic crisis and its evaluation became more necessary nowadays. The aim of this paper is to build and assess different types of forecast intervals for quarterly inflation rate in Romania. The Bootstrap Bias-corrected-accelerated (BCA) forecast intervals outperformed the intervals based on historical errors, four out of six values of inflation rate being placed in the first type of intervals during Q3:2013-Q4:2014. The likelihood ratio tests and the chi-square test indicated that there are significant differences between the ex-ante probability of 0.95 and the real probabilities for both types of forecast intervals. As a methodological novelty, Monte Carlo and bootstrap simulations were used for assessing the uncertainty in inflation rate forecasts in Romania.

Suggested Citation

  • Mihaela Simionescu & Irina Dragan, 2016. "The Evaluation Of Quarterly Forecast Intervals For Inflation Rate In Romania," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 14(1), pages 80-89, May.
    • Handle: RePEc:tuz:journl:v:14:y:2016:i:1:p:80-89
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    References listed on IDEAS

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    1. Gospodinov, Nikolay, 2002. "Bootstrap-Based Inference in Models with a Nearly Noninvertible Moving Average Component," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 254-268, April.
    2. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    3. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
    4. Baker, Scott R. & Davis, Steven J. & Bloom, Nick & Van Reenen, John, 2012. "Economic recovery and policy uncertainty," LSE Research Online Documents on Economics 57970, London School of Economics and Political Science, LSE Library.
    5. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    6. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
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    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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