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More predictable than ever, with the worst MSPE ever

Author

Listed:
  • Pablo PINCHEIRA-BROWN

    (Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.)

  • Nicolás HARDY

    (Facultad de Administración y Economía Universidad Diego Portales.)

Abstract

A fairly common approach to evaluate if a given time series Y_(t+1) is predictable, compares the Mean Squared Prediction Error (MSPE) of a plausible predictor for Y_(t+1) and the MSPE of a naïve benchmark like a constant forecast or the historical average of the predictand, which display zero or a small covariance with the target variable. If the MSPE of the plausible predictor is lower than that of the benchmark, Y_(t+1) is considered predictable, otherwise is considered unpredictable. This intuitive and standard approach might not be truly capturing the essence of predictability, which in words of some authors refers to a notion of dependence between the target variable and variables or events that happened in the past. In particular, when the plausible forecast under evaluation is inefficient, it might face a paradoxical situation: On the one hand, it could have a strong and positive correlation with the target variable, much greater than the correlation of the benchmark with the same target variable. Yet, on the other hand, it could be outperformed in terms of MSPE by the same naïve benchmark. We propose to evaluate predictability directly, with a simple test based on the covariance between the forecast and the target variable. Using Monte Carlo simulations we study size and power of three variations of this test. In general terms, they all behave reasonably well. We also compare their behavior with a traditional test of equality in MSPE. We show that our covariance tests can detect predictability even when MSPE comparisons do not. Finally, we illustrate the relevance of our observation when forecasting monthly oil returns with a forecast based on the Chilean peso.

Suggested Citation

  • Pablo PINCHEIRA-BROWN & Nicolás HARDY, 2024. "More predictable than ever, with the worst MSPE ever," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-30, December.
  • Handle: RePEc:rjr:romjef:v::y:2024:i:4:p:5-30
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Mean Squared Prediction Error; Correlation; Forecasting; Time Series; Random Walk;
    All these keywords.

    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • L74 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Construction
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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