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Information Recovery and Causality: A Tribute to George Judge

Author

Listed:
  • Gordon Rausser

    (Department of Agricultural and Resource Economics, University of California, Berkeley, California 94720)

  • David A. Bessler

    (Department of Agricultural Economics, Texas A&M University, College Station, Texas 77843)

Abstract

In Professor George Judge's pursuit of information recovery and isolating causality in noisy effects observational data, there is a critical distinction between deductive and inductive empirical analysis. For the former, we bring together a synthesis of the literature that has emerged since Koopmans' measurement with theory philosophy. For the latter, we present a host of methodologies that attempt to isolate the causal mechanisms existing in patterns revealed in noisy measurement data. The deductive focus is limited by available theoretical constructs, whereas the inductive focus is fraught with data mining complications, ultimately finding its potential validation in forecasting.

Suggested Citation

  • Gordon Rausser & David A. Bessler, 2016. "Information Recovery and Causality: A Tribute to George Judge," Annual Review of Resource Economics, Annual Reviews, vol. 8(1), pages 7-23, October.
    • Handle: RePEc:anr:reseco:v:8:y:2016:p:7-23
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    File URL: http://www.annualreviews.org/doi/10.1146/annurev-resource-121615-032137
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    More about this item

    Keywords

    econometrics; causality; forecasting; big data; econometric paradigms; maintained hypotheses; data mining;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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