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Testing omitted variables in VARs

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  • Andrea Beccarini

    (University of Muenster)

Abstract

A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression estimates. The empirical part considers two illustrative examples: a univariate regression analysis, based on the rational expectation-augmented Phillips curve; and a VAR with output, inflation and interest rates where a “price puzzle” arises.

Suggested Citation

  • Andrea Beccarini, 2024. "Testing omitted variables in VARs," Statistical Papers, Springer, vol. 65(5), pages 3093-3109, July.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:5:d:10.1007_s00362-023-01513-1
    DOI: 10.1007/s00362-023-01513-1
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    More about this item

    Keywords

    Kalman filter; Hausman test; Omitted variable bias; Phillips curve; Price puzzle; VAR;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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