Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-25T04:11:47.906Z Has data issue: false hasContentIssue false

NONPARAMETRIC ADDITIVE MODELS FOR PANELS OF TIME SERIES

Published online by Cambridge University Press:  01 April 2009

Enno Mammen*
Affiliation:
University of Mannheim
Bård Støve
Affiliation:
Norwegian School of Economics and Business Administration
Dag Tjøstheim
Affiliation:
University of Bergen
*
*Address correspondence to Enno Mammen, Department of Economics, University of Mannheim, L7, 3-5, 68131 Mannheim, Germany; e-mail: [email protected].

Abstract

This paper discusses nonparametric models for panels of time series. There is already a substantial literature on nonlinear models and nonparametric methods in a regression and time series setting. But almost without exception these developments have been limited to univariate and multivariate models of moderate dimensions. Very little has been done for panels, where the dimension, often corresponding to a number of individuals, typically is very large but where the number of observations for each individual may be small or moderate. It is the aim of this paper to start a systematic theoretical treatment of nonparametric models for panels of time series, in particular on additive models. Extending existing methodology to the panel situation is by no means trivial because already for the parametric case many problems are unsolved. Our estimation approach is based on backfitting methods.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arellano, M. (2003) Panel Data Econometrics. Oxford University Press.Google Scholar
Baltagi, B.H. (1995) Econometric Analysis of Panel Data. Wiley.Google Scholar
Baltagi, B.H., Griffin, J.M., & Xiong, W. (2000) To pool or not to pool: Homogenous versus heterogenous estimators applied to cigarette demand. Review of Economics and Statistics 82, 117126.Google Scholar
Cai, Z. & Li, Q. (2008) Nonparametric estimation of varying coefficient dynamic panel data models. Econometric Theory 24, 13211342.CrossRefGoogle Scholar
Chang, Y. (2004) Bootstrap unit root test in panels with cross-sectional dependency. Journal of Econometrics 120, 263293.Google Scholar
Fan, J. & Li, R. (2004) New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of the American Statistical Association 99, 710723.Google Scholar
Fan, J. & Yao, Q., (2003) Nonlinear Time Series. Springer-Verlag.Google Scholar
Ferraty, F. & Vieu, P., (2006) Nonparametric Functional Data Analysis: Theory and Practice. Springer-Verlag.Google Scholar
Fu, B., Li, W.-K., & Fung, W.-K. (2002) Testing model adequacy for dynamic panel data with intercorrelation. Biometrika 89, 591601.CrossRefGoogle Scholar
Härdle, W. (1990) Smoothing Techniques: With Implementation in S. Springer-Verlag.Google Scholar
Hastie, T.J. & Tibshirani, R.J., (1990) Generalized Additive Models. Chapman and Hall.Google Scholar
Henderson, D.J, Carroll, R.J., & Li, Q. (2008) Nonparametric estimation and testing of fixed effects panel data models. Journal of Econometrics 144, 257275.Google Scholar
Hjellvik, V., Chen, R. & Tjøstheim, D. (2004) Nonparametric estimation and testing in panels of intercorrelated time series. Journal of Time Series Analysis 25, 831872.CrossRefGoogle Scholar
Hjellvik, V. & Tjøstheim, D. (1999) Modelling panels of intercorrelated autoregressive time series. Biometrika 86, 573590.Google Scholar
Hsiao, C. (1986) Analysis of Panel Data. Cambridge University Press.Google Scholar
Linton, O. & Mammen, E. (2005) Estimating semiparametric ARCH (∞) models by kernel smoothing methods. Econometrica 73, 771836.Google Scholar
Linton, O.B. & Nielsen, J.P. (1995) A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika 82, 93101.Google Scholar
Mammen, E., Linton, O., & Nielsen, J.P. (1999) The existence and asymptotic properties of a backfitting projection algorithm under weak conditions. Annals of Statistics 27, 14431490.Google Scholar
Mammen, E. & Park, B. (2005) Bandwidth selection for smooth backfitting in additive models. Annals of Statistics 33, 12601294.Google Scholar
Mammen, E. & Park, B. (2006) A simple smooth backfitting method for additive models. Annals of Statistics 34, 22522271.CrossRefGoogle Scholar
Mátyás, L. & Sevestre, P. (1992) The Econometrics of Panel Data. Kluwer Academic.Google Scholar
Newey, W.K. (1994) Kernel estimation of partial means. Econometric Theory 10, 233253.Google Scholar
Nielsen, J.P. & Sperlich, S. (2005) Smooth backfitting in practice. Journal of the Royal Statistical Society, Series B 67, 4361.CrossRefGoogle Scholar
Opsomer, J.D. & Ruppert, D. (1997) Fitting a bivariate additive model by local polynomial regression. Annals of Statistics 25, 186211.Google Scholar
Profit, S. and Sperlich, S. (2004) Non-uniformity of job-matching in a transition economy—A nonparametric analysis for the Czech Republic. Applied Economics 36, 695714.Google Scholar
Ramsay, J.O. and Silverman, B.W. (1996) Functional Data Analysis. Springer-Verlag.Google Scholar
Sperlich, S., Tjøstheim, D. & Yang, L. (2002) Nonparametric estimation and testing of interaction in additive models. Econometric Theory 18, 197251.CrossRefGoogle Scholar
Tjøstheim, D. and Auestad, B.H. (1994) Nonparametric identification of nonlinear time series: Projections. Journal of the American Statistical Association 89, 13981409.Google Scholar
Wooldridge, J. (2005a) Fixed effects and related estimators for correlated random-coefficient and treatment effect panel data models. Review of Economics and Statistics 87, 385390.Google Scholar
Wooldridge, J. (2005b) Simple solutions to the initial conditions problem for dynamic nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics 20, 3954.Google Scholar