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Forecasts with single-equation Markov-switching model: an application to the gross domestic product of Latvia

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  • Bušs, Ginters

Abstract

The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform slightly better than linear VAR models when no leading information is available. However, if reliable leading information is available, single-equation MS models tend to give somewhat less precise forecasts than linear VAR models.

Suggested Citation

  • Bušs, Ginters, 2010. "Forecasts with single-equation Markov-switching model: an application to the gross domestic product of Latvia," MPRA Paper 20688, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:20688
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    References listed on IDEAS

    as
    1. Mike Artis & Hans-Martin Krolzig & Juan Toro, 2004. "The European business cycle," Oxford Economic Papers, Oxford University Press, vol. 56(1), pages 1-44, January.
    2. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. Hans-Martin Krolzig, 2000. "Predicting Markov-Switching Vector Autoregressive Processes," Economics Series Working Papers 2000-W31, University of Oxford, Department of Economics.
    5. Benoit Bellone, 2005. "Classical Estimation of Multivariate Markov-Switching Models using MSVARlib," Econometrics 0508017, EconWPA.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Markov-switching; VAR; forecasting; leading information;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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