A comparative study of parametric and semiparametric autoregressive models

  • Authors

    • Sunil Sapra Department of Economics and StatisticsCalifornia State University5151 State University DrLos Angeles, CA 90032, USA
    2022-04-05
    https://doi.org/10.14419/ijaes.v10i1.31978
  • Autoregressive (AR) Models, Semiparametric Autoregressive Models, Autoregressive Generalized Additive Models (GAM-AR)), Autoregressive Multivariate Adaptive Regression Splines (MARS-AR).

  • Abstract

    Dynamic linear regression models are used widely in applied econometric research. Most applications employ linear autoregressive (AR) models, distributed lag (DL) models or autoregressive distributed lag (ARDL) models. These models, however, perform poorly for data sets with unknown, complex nonlinear patterns. This paper studies nonlinear and semiparametric extensions of the dynamic linear regression model and explores the autoregressive (AR) extensions of two semiparametric techniques to allow unknown forms of nonlinearities in the regression function. The autoregressive GAM (GAM-AR) and autoregressive multivariate adaptive regression splines (MARS-AR) studied in the paper automatically discover and incorporate nonlinearities in autoregressive (AR) models.  Performance comparisons among these semiparametric AR models and the linear AR model are carried out via their application to Australian data on growth in GDP and unemployment using RMSE and GCV measures.

     

     

    Author Biography

    • Sunil Sapra, Department of Economics and StatisticsCalifornia State University5151 State University DrLos Angeles, CA 90032, USA

      Profesor, Department of Economics and Statistics, California State University, Los Angeles, USA

      Sunil Sapra, earned both his M.Phil. and Ph.D. in Econometrics at Columbia University, New York. He is currently a Professor of Economics and Statistics at CaliforniaStateUniversity, Los Angeles. Prior to joining Cal. State, LA, he taught at State University of New York, Buffalo and held the prestigious ASA/NSF/Census Research Fellowship (1989-90) at the Bureau of the Census, Washington, D. C. He is an expert in Business Statistics at the Westlaw Roundtable Group. He has published more than 70 articles in some of the most prestigious statistics and econometrics journals. His research on semi-parametric econometrics, missing data problems, nonlinear statistical and econometric models, robust statistical procedures, limited dependent variables and duration data analysis has been published in Econometric Theory, The American Statistician, International Journal of Advanced Statistics and Probability, Statistica Neerlandica, Statistical Papers, Sankhya, Economics e-journal, Bulletin of Economic Research, Economics Letters, Applied Economics Letters, and Empirical Economics Letters. He serves on the editorial boards of several statistics and economics journals. His research has been cited in statistics and econometrics textbooks and journals as well as a widely-used volume on statistical distributions. He received the Outstanding Professor Award from California State University, Los Angeles in 2003 for excellence in teaching and research. He has been listed in Who’s Who among American Teachers and Educators (11th Edition, 2007).

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  • How to Cite

    Sapra, S. (2022). A comparative study of parametric and semiparametric autoregressive models. International Journal of Accounting and Economics Studies, 10(1), 15-19. https://doi.org/10.14419/ijaes.v10i1.31978

    Received date: 2022-02-13

    Accepted date: 2022-03-20

    Published date: 2022-04-05