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Estimation Bias and Feasible Conditional Forecasts from the First-Order Moving Average Model

Author

Listed:
  • Bao Yong

    (Department of Economics, Purdue University, 403 W. State Street, West Lafayette, IN 47907, USA)

  • Zhang Ru

    (Department of Economics, University of California, Riverside, CA 92521, USA)

Abstract

The quasi-maximum likelihood estimator (QMLE) of parameters in the first-order moving average model can be biased in finite samples. We develop the second-order analytical bias of the QMLE and investigate whether this estimation bias can lead to biased feasible optimal forecasts conditional on the available sample observations. We find that the feasible multiple-step-ahead forecasts are unbiased under any nonnormal distribution, and the one-step-ahead forecast is unbiased under symmetric distributions.

Suggested Citation

  • Bao Yong & Zhang Ru, 2013. "Estimation Bias and Feasible Conditional Forecasts from the First-Order Moving Average Model," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 63-80, July.
    • Handle: RePEc:bpj:jtsmet:v:6:y:2013:i:1:p:63-80:n:4
    DOI: 10.1515/jtse-2013-0015
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    References listed on IDEAS

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    8. James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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