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Computer-assisted generalized partial linear models

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  • Müller, Marlene

Abstract

A particular semiparametric model of interest is the generalized partial linear model (GPLM) which allows a nonparametric modeling of the influence of the continuous covariables. The paper reviews different estimation procedures based on kernel methods and test procedures on the correct specification of this model (vs. a parametric generalized linear model). Simulations and an application to a data set on East-West German migration illustrate similarities and dissimilarities of the estimators and test statistics. Semiparametric methods are highly demanding on software. Thus the presentation is completed by indicating the practical implementation in new version of the statistical computing environment XploRe.

Suggested Citation

  • Müller, Marlene, 1997. "Computer-assisted generalized partial linear models," SFB 373 Discussion Papers 1997,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:199748
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    References listed on IDEAS

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    1. HÄRDLE, Wolfgang & TURLACH, Berwin, 1992. "Nonparametric approaches to generalized linear models," LIDAM Discussion Papers CORE 1992037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    3. Härdle, W.K. & Mammen, E. & Müller, M.D., 1996. "Testing Parametric versus Semiparametric Modelling in Generalized Linear Models," Other publications TiSEM 3b9b6d39-869e-4ecd-9982-6, Tilburg University, School of Economics and Management.
    4. Härdle, W.K. & Mammen, E. & Müller, M.D., 1996. "Testing Parametric versus Semiparametric Modelling in Generalized Linear Models," Discussion Paper 1996-42, Tilburg University, Center for Economic Research.
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