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Pitfalls and Possibilities in Predictive Regression

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Abstract

Financial theory and econometric methodology both struggle in formulating models that are logically sound in reconciling short run martingale behaviour for financial assets with predictable long run behavior, leaving much of the research to be empirically driven. The present paper overviews recent contributions to this subject, focussing on the main pitfalls in conducting predictive regression and on some of the possibilities offered by modern econometric methods. The latter options include indirect inference and techniques of endogenous instrumentation that use convenient temporal transforms of persistent regressors. Some additional suggestions are made for bias elimination, quantile crossing amelioration, and control of predictive model misspecification.

Suggested Citation

  • Peter C. B. Phillips, 2015. "Pitfalls and Possibilities in Predictive Regression," Cowles Foundation Discussion Papers 2003, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2003
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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2003.pdf
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    1. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
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    Cited by:

    1. Paulo M.M. Rodrigues & Matei Demetrescu, 2016. "Residual-augmented IVX predictive regression," Working Papers w201605, Banco de Portugal, Economics and Research Department.

    More about this item

    Keywords

    Bias; Endogenous instrumentation; Indirect inference; IVX estimation; Local unit roots; Mild integration; Prediction; Quantile crossing; Unit roots; Zero coverage probability;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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