Hostname: page-component-74656f9884-54qdv Total loading time: 0 Render date: 2026-11-10T07:56:48.579Z Has data issue: false hasContentIssue false
  • English
  • Français

ASYMPTOTIC THEORY IN FIXED EFFECTS PANEL DATA SEEMINGLY UNRELATED PARTIALLY LINEAR REGRESSION MODELS

Published online by Cambridge University Press:  13 December 2013

Jinhong You  and
Xian Zhou
Jinhong You*
Affiliation:
Shanghai University of Finance and Economics and Key Laboratory of Mathematical Economics
Xian Zhou
Affiliation:
Macquarie University
*
*Address correspondence to Jinhong You, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China. e-mail: jinhongyou@gmail.com.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper deals with statistical inference for the fixed effects panel data seemingly unrelated partially linear regression model. The model naturally extends the traditional fixed effects panel data regression model to allow for semiparametric effects. Multiple regression equations are permitted, and the model includes the aggregated partially linear model as a special case. A weighted profile least squares estimator for the parametric components is proposed and shown to be asymptotically more efficient than those neglecting the contemporaneous correlation. Furthermore, a weighted two-stage estimator for the nonparametric components is also devised and shown to be asymptotically more efficient than those based on individual regression equations. The asymptotic normality is established for estimators of both parametric and nonparametric components. The finite-sample performance of the proposed methods is evaluated by simulation studies.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 30 , Issue 2 , April 2014 , pp. 407 - 435
Copyright
Copyright © Cambridge University Press 2013 

References

REFERENCES

Abrevaya, J. (1999) Leapfrog estimation of a fixed-effects model with unknown transformation of the dependent variable. Journal of Econometrics 93, 203–228.Google Scholar
Ahn, S.C. & Schmidt, P. (2000) Estimation of linear panel data models using GMM. In Mátyás, L. (ed.), Generalized Method of Moments Estimation. Ch. 8, 211–249. Cambridge University Press.Google Scholar
Atak, A., Linton, O., Xiao, Z. (2011) A semiparametric panel model for climate change in the United Kingdom. Journal of Econometrics 164, 92–115.Google Scholar
Avery, R.B. (1977) Error components and seemingly unrelated regressions. Econometrica 45, 199–209.Google Scholar
Baltagi, B.H. (1980) On seemingly unrelated regressions with error components. Econometrica 48, 1547–1551.CrossRefGoogle Scholar
Baltagi, B.H. (1995) Econometric Analysis of Panel Data. Wiley.Google Scholar
Baltagi, B.H. & Li, D. (2002) Series estimation of partially linear panel data models with fixed effects. Annals of Economics and Finance 3, 103–116.Google Scholar
Beierlein, J.G., Dunn, J.W. & McConnon, J.C. Jr. (1981) The demand for electricity and natural gas in the northeastern United States. Review of Economics and Statistics 63, 403–408.Google Scholar
Bickel, P.J. & Kwon, J. (2001) Inference for semiparametric models: Some questions and an answer. With comments and a rejoinder by the authors. Statistics Sinica 11, 863–960.Google Scholar
Breusch, T.S. & Pagan, A.R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287–1294.Google Scholar
Carroll, R.J., Doug, M., Larry, F. & Victor, K. (2006) Seemingly unrelated measurement error models, with application to nutritional epidemiology. Biometrics 62, 75–84.Google Scholar
Entorf, H. (1997) Random walks with drifts: Nonsense regression and spurious fixed-effect estimation. Journal of Econometrics 80, 287–296.Google Scholar
Fan, J., Peng, H. & Huang, T. (2005) Semilinear high-dimensional model for normalization of microarray data: A theoretical analysis and partial consistency. Journal of the American Statistical Association 100, 781–813.Google Scholar
Gallant, R. (1975) Seemingly unrelated nonlinear regressions. Journal of Econometrics 3, 35–50.Google Scholar
Ghazal, G.A. (1986) The error of forecast for seemingly unrelated regression (SUR) equations. Egyptian Statistical Journal 30, 35–64.Google Scholar
Hahn, J. (1999) How informative is the initial condition in the dynamic panel model with fixed effects? Journal of Econometrics 93, 309–326.Google Scholar
Hahn, J. (2001) The information bound of a dynamic panel logit model with fixed effects. Econometric Theory 17, 913–932.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, 257–276.Google Scholar
Horowitz, J.L. & Lee, S. (2004) Semiparametric estimation of a panel data proportional hazards model with fixed effects. Journal of Econometrics 119, 155–198.Google Scholar
Horowitz, J.L. & Mammen, E. (2004) Nonparametric estimation of an additive model with a link function. Annals of Statistics 32, 2412–2443.Google Scholar
Kakwani, N.C. (1974) A note on the efficiency of the Zellner’s seemingly unrelated regressions estimator. Annals of the Institute of Statistical Mathematics 26, 361–362.Google Scholar
Koop, G, Poirer, D. & Tobias, J. (2005) Semiparametric Bayesian inference in multiple equation models. Journal of Applied Econometrics 20, 723–747.Google Scholar
Kurata, H. (1999) On the efficiencies of several generalized least squares estimators in a seemingly unrelated regression model and a heteroscedastic model. Journal of Multivariate Analysis 70,86–94.Google Scholar
Liu, A. (2002) Efficient estimation of two seemingly unrelated regression equations. Journal of Multivariate Analysis 82, 445–456.Google Scholar
Mack, Y.P. & Silverman, B.W. (1982) Weak and strong uniform consistency of kernel regression estimates. Zoitschcift für Wahrscheinlichkeits theorie und Verwandte Gebiete 61, 405–415.Google Scholar
Mandy, D.M. & Martins-Filho, C. (1993) Seemingly unrelated regressions under additive heteroscedasticity: Theory and share equation applications. Journal of Econometrics 58, 315–346.Google Scholar
Neudecker, H. & Windmeijer, F.A. (1991) R 2 in seemingly unrelated regression equations. Statistical Neerlandica 45, 405–411.Google Scholar
Neyman, J. & Scott, E. (1948) Consistent estimates based on partially consistent observations.Econometrica 16, 1–32.CrossRefGoogle Scholar
Ng, V.M. (2002) Robust Bayesian inference for seemingly unrelated regressions with elliptical errors. Journal of Multivariate Analysis 83, 409–414.Google Scholar
Robinson, P. (1988) Root-N-consistent semiparametric regression. Econometrica 56, 931–954.Google Scholar
Rocke, D.M. (1989) Bootstrap Bartlett adjustment in seemingly unrelated regression. Journal of the American Statistical Association 84, 598–601.Google Scholar
Smith, M. & Kohn, R. (2000) Nonparametric seemingly unrelated regression. Journal of Econometrics 98, 257–281.Google Scholar
Srivastava, V.K. & Giles, E.A. (1987) Seemingly Unrelated Regression Equations Models. Estimation and Inference. Statistics: Textbooks and Monographs, 80. Marcel Dekker.Google Scholar
Stone, C.J. (1986) Additive regression and other nonparametric models. Annals of Statistics 13, 689–705.Google Scholar
Su, L. & Ullah, A. (2006) Profile likelihood estimation of partially linear panel data models with fixed effects. Economics Letters 92, 75–81.Google Scholar
Verbon, H.A. (1980) Testing for heteroscedasticity in a model of seemingly unrelated regression equations with variance components (SUREVC). Economics Letters 2, 149–153.Google Scholar
Wan, G.H., Griffiths, W.E., & Anderson, J.R. (1992) Using panel data to estimate risk effects in seemingly unrelated production functions. Empirical Economics 17, 35–49.Google Scholar
Wang, N. (2003) Marginal nonparametric kernel regression accounting for within-subject correlation. Biometrika 90, 43–52.Google Scholar
Wang, Y.D., Guo, W.S., & Brown, B. (2000) Spline smoothing for bivariate data with application between hormones. Statistica Sinica 10, 377–397.Google Scholar
Welsh, A.H. & Yee, T.W. (2006) Local regression for vector responses. Journal of Statical Planning and Inference 135, 307–331.Google Scholar
You, J.H. & Chen, G.M. (2006) Estimation in a semiparametric varying-coefficient partially linear errors-in-variables model. Journal of Multivariate Analysis 97, 324–341.Google Scholar
You, J.H. & Zhou, H. (2008) A two-stage method for semilinear in-slide regression model. Journal of Multivariate Analysis 99, 1610–1634.Google Scholar
Zellner, A. (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57, 348–368.Google Scholar
Zellner, A. (1963) Estimators for seemingly unrelated equations: Some exact finite sample results. Journal of the American Statistical Association 58, 977–992.Google Scholar
Zhou, B., Xu, Q.F. & You, J.H. (2011) Efficient estimation of seemingly unrelated nonparametric regression models. Metrika 73, 121–138.Google Scholar