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A new approach to bootstrap inference in functional coefficient models

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  • Herwartz, Helmut
  • Xu, Fang

Abstract

We introduce a new, factor based bootstrap approach which is robust under heteroskedastic error terms for inference in functional coefficient models. Modeling the functional coefficient parametrically, the bootstrap approximation of an F statistic is shown to hold asymptotically. In simulation studies with both parametric and nonparametric functional coefficients, factor based bootstrap inference outperforms the wild bootstrap and pairs bootstrap approach according to its size features. Applying the functional coefficient model to a cross sectional investment regression on savings, the saving retention coefficient is found to depend on third variables as the population growth rate and the openness ratio.

Suggested Citation

  • Herwartz, Helmut & Xu, Fang, 2007. "A new approach to bootstrap inference in functional coefficient models," Economics Working Papers 2007-15, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:5614
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    References listed on IDEAS

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    1. Aneiros-Perez, G. & Vilar-Fernandez, J.M., 2008. "Local polynomial estimation in partial linear regression models under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2757-2777, January.
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    4. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
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    6. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    7. Horowitz, J.L., 1992. "Testing a Parametric Model Against a Semiparametric Alternative," Papers 9219, Tilburg - Center for Economic Research.
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    1. Herwartz, H. & Xu, F., 2010. "A functional coefficient model view of the Feldstein-Horioka puzzle," Journal of International Money and Finance, Elsevier, vol. 29(1), pages 37-54, February.
    2. Herwartz, Helmut & Roestel, Jan, 2017. "Mundell’s trilemma: Policy trade-offs within the middle ground," Journal of International Money and Finance, Elsevier, vol. 75(C), pages 1-13.
    3. Herwartz, Helmut & Walle, Yabibal M., 2014. "Openness and the finance-growth nexus," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 235-247.
    4. Herwartz, Helmut & Walle, Yabibal M., 2014. "Determinants of the link between financial and economic development: Evidence from a functional coefficient model," Economic Modelling, Elsevier, vol. 37(C), pages 417-427.
    5. Ahrens, Steffen & Hartmann, Matthias, 2014. "State-dependence vs. timedependence: An empirical multi-country investigation of price sluggishness," Kiel Working Papers 1907, Kiel Institute for the World Economy.
    6. Steffen Ahrens & Matthias Hartmann, 2015. "Cross-sectional evidence on state-dependent versus time-dependent price setting," Economics Bulletin, AccessEcon, vol. 35(4), pages 2701-2709.
    7. Helmut Herwartz & Israel Waichman, 2010. "A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis," Computational Statistics, Springer, vol. 25(4), pages 725-732, December.
    8. Guo Shuang & Wei Chuanhua, 2015. "Testing for Spatial Lag Effects in Varying Coefficient Spatial Autoregressive Models," Journal of Systems Science and Information, De Gruyter, vol. 3(6), pages 561-567, December.
    9. Helmut Herwartz & Bernd Theilen, 2010. "The determinants of health‐care expenditure: new results from semiparametric estimation," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 964-978, August.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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