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Testing for Bias in Forecasts for Independent Multinomial Outcomes

Author

Listed:
  • Philip Hans Franses

    (Econometric Institute, Erasmus School of Economics, Burgemeester Oudlaan 50, 3062PA Rotterdam, The Netherlands)

  • Richard Paap

    (Econometric Institute, Erasmus School of Economics, Burgemeester Oudlaan 50, 3062PA Rotterdam, The Netherlands)

Abstract

This paper deals with a test on forecast bias in predicting independent multinomial outcomes where the predictions are probabilities. The new Likelihood Ratio (and Wald) test extends the familiar Mincer Zarnowitz regression to a multinomial logit model instead of a linear regression. The test is evaluated using various simulation experiments, which indicate that the size and power properties are good, even for small sample sizes, in the sense that the size is close to the used 5% level, and the power quickly reaches 1. We implement the test in an empirical setting on brand choice by individual households.

Suggested Citation

  • Philip Hans Franses & Richard Paap, 2025. "Testing for Bias in Forecasts for Independent Multinomial Outcomes," Forecasting, MDPI, vol. 7(1), pages 1-8, January.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:1:p:4-:d:1566045
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    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Anonymous, 1969. "Chapter II. Forecasts for the Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 47, pages 32-46, February.
    3. Philip Hans Franses, 2021. "Testing for bias in forecasts for independent binary outcomes," Applied Economics Letters, Taylor & Francis Journals, vol. 28(15), pages 1336-1338, September.
    4. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, January.
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