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VC - A Program for Estimating Time-Varying Coefficients

Author

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  • Schlicht, Ekkehart

Abstract

VC implements Schlicht's method for estimating a linear regression with time-varying coefficients. The variances are estimated by a moments estimator that does not require the disturbances to be Gaussian, but if they are, it coincides with the corresponding maximum likelihood estimate in large samples. It has a direct descriptive interpretation and performs better than the corresponding likelihood estimator in small samples. The program runs under Windows.

Suggested Citation

  • Schlicht, Ekkehart, 2021. "VC - A Program for Estimating Time-Varying Coefficients," Discussion Papers in Economics 74981, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenec:74981
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    File URL: https://epub.ub.uni-muenchen.de/74981/1/VC-v.6.zip
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    Cited by:

    1. Afonso, António & Carvalho, Francisco Tiago, 2022. "Time-varying cyclicality of fiscal policy: The case of the Euro area," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    2. António Afonso & Francisco Tiago Carvalho, 2021. "Euro area time-varying cyclicality of fiscal policy," Working Papers REM 2021/0202, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    More about this item

    Keywords

    Time-series analysis; linear model; state-space estimation; time-varying coefficients; moments estimation; Kalman filtering; penalized least squares.;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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