Model detection and estimation for varying coefficient panel data models with fixed effects

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

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Estimating latent group structure in time-varying coefficient panel data models

Econometrics Journal ◽

10.1093/ectj/utz008 ◽

2019 ◽

Author(s):

Jia Chen

Keyword(s):

Panel Data ◽

Data Models ◽

Panel Data Models ◽

Time Varying ◽

Clustering Method ◽

Varying Coefficient ◽

Varying Coefficients ◽

Time Varying Coefficients ◽

Latent Group ◽

Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

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Robust estimation and moment selection in dynamic fixed-effects panel data models

Computational Statistics ◽

10.1007/s00180-017-0782-7 ◽

2017 ◽

Vol 33 (2) ◽

pp. 675-708

Author(s):

P. Čížek ◽

M. Aquaro

Keyword(s):

Panel Data ◽

Robust Estimation ◽

Fixed Effects ◽

Data Models ◽

Panel Data Models ◽

Moment Selection

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TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

Econometric Theory ◽

10.1017/s0266466613000017 ◽

2013 ◽

Vol 29 (6) ◽

pp. 1079-1135 ◽

Cited By ~ 47

Author(s):

Liangjun Su ◽

Qihui Chen

Keyword(s):

Panel Data ◽

Fixed Effects ◽

Data Models ◽

Development Economic ◽

Strong Mixing ◽

Panel Data Models ◽

Finite Sample ◽

Test Statistic ◽

Lm Test ◽

Interactive Fixed Effects

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

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