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Fully modified least squares estimation and inference for systems of cointegrating polynomial regressions

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  • Wagner, Martin

Abstract

We consider fully modified least squares estimation for systems of cointegrating polynomial regressions, i.e., systems of regressions that include deterministic variables, integrated processes and their powers as regressors. The errors are allowed to be correlated across equations, over time and with the regressors. Whilst, of course, fully modified OLS and GLS estimation coincide – for any regular weighting matrix – without restrictions on the parameters and with the same regressors in all equations, this equivalence breaks down, in general, in case of parameter restrictions and/or different regressors across equations. Consequently, we discuss in detail restricted fully modified GLS estimators and inference based upon them.

Suggested Citation

  • Wagner, Martin, 2023. "Fully modified least squares estimation and inference for systems of cointegrating polynomial regressions," Economics Letters, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:ecolet:v:228:y:2023:i:c:s0165176523002112
    DOI: 10.1016/j.econlet.2023.111186
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    More about this item

    Keywords

    Fully modified estimation; Cointegrating polynomial regression; Generalized least squares; Hypothesis testing;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General

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