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Prediction intervals for inflation and unemployment rate in Romania. A Bayesian approach

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  • Simionescu, Mihaela

Abstract

The main aim of this paper is to provide forecast intervals for inflation and unemployment rate in Romania, bringing methodological novelties in the construction and evaluation of the prediction intervals. Considering the period 2004-2017 as forecast horizon, only few intervals included the registered values on the variables, but in the last stage when all the prior information has been used, the forecast intervals were very short. The proposed Bayesian technique for assessing prediction intervals was better than traditional approaches based on statistic tests.

Suggested Citation

  • Simionescu, Mihaela, 2017. "Prediction intervals for inflation and unemployment rate in Romania. A Bayesian approach," GLO Discussion Paper Series 82, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:82
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    References listed on IDEAS

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    1. Kenneth F. Wallis, 2008. "Forecast Uncertainty, Its Representation And Evaluation," World Scientific Book Chapters, in: Roberto S Mariano & Yiu-Kuen Tse (ed.), Econometric Forecasting And High-Frequency Data Analysis, chapter 1, pages 1-51, World Scientific Publishing Co. Pte. Ltd..
    2. Weihua Guan, 2003. "From the help desk: Bootstrapped standard errors," Stata Journal, StataCorp LP, vol. 3(1), pages 71-80, March.
    3. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    4. Gospodinov, Nikolay, 2002. "Median unbiased forecasts for highly persistent autoregressive processes," Journal of Econometrics, Elsevier, vol. 111(1), pages 85-101, November.
    5. 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.
    6. Mihaela BRATU, 2012. "Forecast Intervals for Inflation in Romania," Timisoara Journal of Economics, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 5(17), pages 145-152.
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    More about this item

    Keywords

    forecast interval; Bayesian interval; inflation; unemployment;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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