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Log versus level in VAR forecasting: 16 Million empirical answers - expect the unexpected

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  • Johannes Mayr
  • Dirk Ulbricht

Abstract

The use of log-transformed data has become standard in macroeconomic forecasting with VAR models. However, its appropriateness in the context of out-of-sample forecasts has not yet been exposed to a thorough empirical investigation. With the aim of filling this void, a broad sample of VAR models is employed in a multi-country setup and approximately 16 Mio. pseudo-out-of-sample forecasts are evaluated. The results show that, on average, the knee-jerk transformation of the data is at best harmless.

Suggested Citation

  • Johannes Mayr & Dirk Ulbricht, 2007. "Log versus level in VAR forecasting: 16 Million empirical answers - expect the unexpected," ifo Working Paper Series 42, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    • Handle: RePEc:ces:ifowps:_42
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    Cited by:

    1. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    2. Donal Smith, 2016. "The International Impact of Financial Shocks: A Global VAR and Connectedness Measures Approach," Discussion Papers 16/07, Department of Economics, University of York.
    3. Gloria Gonzalez-Rivera & Yun Luo & Esther Ruiz, 2018. "Prediction Regions for Interval-valued Time Series," Working Papers 201817, University of California at Riverside, Department of Economics.
    4. Taylor, Nick, 2017. "Realised variance forecasting under Box-Cox transformations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 770-785.
    5. Elena Banica & Valentina Vasile, 2017. "Foreign trade impact on employment efficiency – an analysis using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 111-127, December.
    6. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: el caso colombiano," Coyuntura Económica, Fedesarrollo, December.
    7. Serfraz, Ayesha, 2017. "What is the effect of foreign direct investment inflows on economic growth in Pakistan? An empirical analysis in the light of religious sectarianism as catalyst for terrorism," ZÖSS-Discussion Papers 59, University of Hamburg, Centre for Economic and Sociological Studies (CESS/ZÖSS).
    8. Masayoshi Hayashi, 2013. "On the Decomposition of Regional Stabilization and Redistribution," CIRJE F-Series CIRJE-F-910, CIRJE, Faculty of Economics, University of Tokyo.

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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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