Special Issue "Recent Developments in Panel Data Methods"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 December 2016)

Special Issue Editors

Guest Editor
Prof. In Choi

Department of Economics, Sogang University, 35 Baekbeom-ro, Mapo-gu Seoul, 121-742 Korea
Website | E-Mail
Interests: time series; panel data
Guest Editor
Prof. Ryo Okui

NYU Shanghai, Pudong Shanghai, China
Website | E-Mail
Interests: panel data

Special Issue Information

Dear Colleagues,

Econometric research using panel data has become increasingly prevalent over the last few decades due to the emergence and availability of various panel data sets in many nations. Accordingly, methods for panel data analysis have also developed at a rapid pace. In the beginning, panel data methods were mostly for linear models under the iid assumptions and used mainly for micro data. Nowadays, more complex models are being employed for panel data analysis and macro data are also being frequently analyzed. Still, there are many challenging issues and remaining problems in panel data analysis. This Special Issue of Econometrics invites new research results for panel data analysis. Topics of this Special Issue include static panel data models, dynamic panel data models with short or long time series dimension, nonlinear panel data models, high-dimensional time series, spatial models, factor analysis, clustering, etc. Empirical research results using sophisticated panel data methods are also welcome. Deadline for the submission of papers is December 31, 2016.

Prof. In Choi
Prof. Ryo Okui
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

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Keywords

  • static panel data models
  • dynamic panel data models with short or long time series dimension
  • nonlinear panel data models
  • high-dimensional time series
  • spatial models
  • factor analysis
  • clustering

Published Papers (4 papers)

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Research

Open AccessArticle Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models
Econometrics 2017, 5(1), 14; doi:10.3390/econometrics5010014
Received: 28 December 2016 / Revised: 6 March 2017 / Accepted: 10 March 2017 / Published: 20 March 2017
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Abstract
Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated
[...] Read more.
Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
Open AccessArticle Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models
Econometrics 2016, 4(4), 47; doi:10.3390/econometrics4040047
Received: 25 May 2016 / Revised: 23 November 2016 / Accepted: 25 November 2016 / Published: 30 November 2016
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Abstract
The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed
[...] Read more.
The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dimensional optimizations when the number of regressors is large. This paper proposes a computationally feasible variation on these standard two-step GMM estimators by applying the idea of continuous-updating to the autoregressive parameter only, given the fact that the absolute value of the autoregressive parameter is less than unity as a necessary requirement for the data-generating process to be stationary. We show that our subset-continuous-updating method does not alter the asymptotic distribution of the two-step GMM estimators, and it therefore retains consistency. Our simulation results indicate that the subset-continuous-updating GMM estimators outperform their standard two-step counterparts in finite samples in terms of the estimation accuracy on the autoregressive parameter and the size of the Sargan-Hansen test. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
Open AccessArticle Testing Cross-Sectional Correlation in Large Panel Data Models with Serial Correlation
Econometrics 2016, 4(4), 44; doi:10.3390/econometrics4040044
Received: 23 July 2016 / Revised: 12 October 2016 / Accepted: 19 October 2016 / Published: 4 November 2016
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Abstract
This paper considers the problem of testing cross-sectional correlation in large panel data models with serially-correlated errors. It finds that existing tests for cross-sectional correlation encounter size distortions with serial correlation in the errors. To control the size, this paper proposes a modification
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This paper considers the problem of testing cross-sectional correlation in large panel data models with serially-correlated errors. It finds that existing tests for cross-sectional correlation encounter size distortions with serial correlation in the errors. To control the size, this paper proposes a modification of Pesaran’s Cross-sectional Dependence (CD) test to account for serial correlation of an unknown form in the error term. We derive the limiting distribution of this test as N , T . The test is distribution free and allows for unknown forms of serial correlation in the errors. Monte Carlo simulations show that the test has good size and power for large panels when serial correlation in the errors is present. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
Open AccessArticle Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters
Econometrics 2016, 4(4), 39; doi:10.3390/econometrics4040039
Received: 6 May 2016 / Revised: 18 September 2016 / Accepted: 26 September 2016 / Published: 9 October 2016
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Abstract
Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel data models with
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Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate likelihood obtained via Rao-Blackwellized particle filters. Monte Carlo studies reveal the good and stable performance of our particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic volatility exists, our approach can be better than the maximum likelihood estimator which neglects stochastic volatility and generalized method of moments (GMM) estimators. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)

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