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Econometrics
Special Issues
Recent Developments in Panel Data Methods
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Recent Developments in Panel Data Methods

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

Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 66133

Special Issue Editors

Prof. Dr. In Choi

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

E-Mail Website
Guest Editor
NYU Shanghai, Pudong Shanghai, China
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

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Published Papers (6 papers)

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Research

267 KiB  
Article
Synthetic Control and Inference
by Jinyong Hahn and Ruoyao Shi
Econometrics 2017, 5(4), 52; https://doi.org/10.3390/econometrics5040052 - 28 Nov 2017
Cited by 49 | Viewed by 16482
Abstract
We examine properties of permutation tests in the context of synthetic control. Permutation tests are frequently used methods of inference for synthetic control when the number of potential control units is small. We analyze the permutation tests from a repeated sampling perspective and [...] Read more.
We examine properties of permutation tests in the context of synthetic control. Permutation tests are frequently used methods of inference for synthetic control when the number of potential control units is small. We analyze the permutation tests from a repeated sampling perspective and show that the size of permutation tests may be distorted. Several alternative methods are discussed. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
585 KiB  
Article
Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity
by Junrong Liu, Robin C. Sickles and E. G. Tsionas
Econometrics 2017, 5(3), 33; https://doi.org/10.3390/econometrics5030033 - 28 Jul 2017
Cited by 8 | Viewed by 7873
Abstract
This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of unobserved [...] Read more.
This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of unobserved effects. The latter have been addressed in a non-Bayesian framework by Bai (2009) and Kneip et al. (2012), among others. Monte Carlo experiments are used to examine the finite-sample performance of our estimators. An empirical study of efficiency trends in the largest banks operating in the U.S. from 1990 to 2009 illustrates our new estimators. The study concludes that scale economies in intermediation services have been largely exploited by these large U.S. banks. Full article
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
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1134 KiB  
Article
Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models
by Jan Kiviet, Milan Pleus and Rutger Poldermans
Econometrics 2017, 5(1), 14; https://doi.org/10.3390/econometrics5010014 - 20 Mar 2017
Cited by 47 | Viewed by 10814
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)
763 KiB  
Article
Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models
by Richard A. Ashley and Xiaojin Sun
Econometrics 2016, 4(4), 47; https://doi.org/10.3390/econometrics4040047 - 30 Nov 2016
Cited by 7 | Viewed by 7161
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)
325 KiB  
Article
Testing Cross-Sectional Correlation in Large Panel Data Models with Serial Correlation
by Badi H. Baltagi, Chihwa Kao and Bin Peng
Econometrics 2016, 4(4), 44; https://doi.org/10.3390/econometrics4040044 - 4 Nov 2016
Cited by 52 | Viewed by 14346
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 [...] Read more.
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)
741 KiB  
Article
Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters
by Wen Xu
Econometrics 2016, 4(4), 39; https://doi.org/10.3390/econometrics4040039 - 9 Oct 2016
Cited by 1 | Viewed by 7279
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 [...] Read more.
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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