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Mean Squared Prediction Error Reduction With Instrumental Variables

Author

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  • Antonis Michis

    (Central Bank of Cyprus)

Abstract

The mean squared prediction error of the linear regression model is examined when estimation is performed with instrumental variables. It is shown that increasing the number of instruments in the estimation procedure, can reduce the mean squared prediction error of the model through more efficient estimation of the coefficient vector.

Suggested Citation

  • Antonis Michis, 2016. "Mean Squared Prediction Error Reduction With Instrumental Variables," Working Papers 2016-5, Central Bank of Cyprus.
    • Handle: RePEc:cyb:wpaper:2016-5
    as

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    File URL: https://www.centralbank.cy/images/media/pdf/Working_Paper_MSPE_with_I_July_2016.pdf
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    References listed on IDEAS

    as
    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    2. Cragg, John G, 1983. "More Efficient Estimation in the Presence of Heteroscedasticity of Unknown Form," Econometrica, Econometric Society, vol. 51(3), pages 751-763, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Mean squared prediction error; efficiency; instrumental variables;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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