Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 20 of 20
Full-Text Articles in Social and Behavioral Sciences
The Mundlak Spatial Estimator, Badi H. Baltagi
The Mundlak Spatial Estimator, Badi H. Baltagi
Center for Policy Research
The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated …
The Two-Way Mundlak Estimator, Badi H. Baltagi
The Two-Way Mundlak Estimator, Badi H. Baltagi
Center for Policy Research
Mundlak (1978) shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once this individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Woolridge (2021) also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented …
Robust Dynamic Space-Time Panel Data Models Using Ε- Contamination: An Application To Crop Yields And Climate Change, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Robust Dynamic Space-Time Panel Data Models Using Ε- Contamination: An Application To Crop Yields And Climate Change, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Center for Policy Research
This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach de-parts from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the …
Lasso For Stochastic Frontier Models With Many Efficient Firms, William C. Horrace, Hyunseok Jung, Yoonseok Lee
Lasso For Stochastic Frontier Models With Many Efficient Firms, William C. Horrace, Hyunseok Jung, Yoonseok Lee
Center for Policy Research
We apply the adaptive LASSO (Zou, 2006) to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 penalty with sign restrictions for firm-level inefficiencies allows simultaneous estimation of the maximal efficiency and firm-level inefficiency parameters, which results in a faster rate of convergence of the corresponding estimators than the least-squares dummy variable approach. We show that the estimator possesses the oracle property and selection consistency still holds with our proposed tuning parameter selection criterion. We also propose an efficient optimization algorithm based on coordinate descent. We apply the method to estimate …
Robust Dynamic Panel Data Models Using ��-Contamination, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Robust Dynamic Panel Data Models Using ��-Contamination, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Center for Policy Research
This paper extends the work of Baltagi et al. (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)'s g-priors for the variance-covariance matrices. We propose a general "toolbox" for a wide range of specifications which includes the dynamic …
A Panel Data Model With Generalized Higher-Order Network Effects, Badi Baltagi, Sophia Ding, Peter Egger
A Panel Data Model With Generalized Higher-Order Network Effects, Badi Baltagi, Sophia Ding, Peter Egger
Center for Policy Research
Many data situations require the consideration of network effects among the cross-sectional units of observation. In this paper, we present a generalized panel model which accounts for two features: (i) three types of network effects on the right-hand side of the model, namely through weighted dependent variable, weighted exogenous variables, as well as weighted error components, and (ii) higher-order network effects due to ex-ante unknown network-decay functions or the presence of multiplex (or multi-layer) networks among all of those. We outline the model, the basic assumptions, and present simulation results.
Growth Empirics: A Bayesian Semiparametric Model With Random Coefficients For A Panel Of Oecd Countries, Badi Baltagi, Georges Bresson, Jean-Michel Etienne
Growth Empirics: A Bayesian Semiparametric Model With Random Coefficients For A Panel Of Oecd Countries, Badi Baltagi, Georges Bresson, Jean-Michel Etienne
Center for Policy Research
This paper proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971-2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed and random-coefficients model to estimate this relationship. In particular, this paper uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), we estimate a mean field variational Bayes semiparametric model with random coefficients …
Robust Linear Static Panel Data Models Using Ε-Contamination, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Robust Linear Static Panel Data Models Using Ε-Contamination, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Center for Policy Research
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior means are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good …
The Identification And Estimation Of A Large Factor Model With Structural Instability, Badi H. Baltagi, Chihwa Kao, Fa Wang
The Identification And Estimation Of A Large Factor Model With Structural Instability, Badi H. Baltagi, Chihwa Kao, Fa Wang
Center for Policy Research
This paper tackles the identification and estimation of a high dimensional factor model with unknown number of latent factors and a single break in the number of factors and/or factor loadings occurring at unknown common date. First, we propose a least squares estimator of the change point based on the second moments of estimated pseudo factors and show that the estimation error of the proposed estimator is Op(1). We also show that the proposed estimator has some degree of robustness to misspecification of the number of pseudo factors. With the estimated change point plugged in, consistency of the estimated number …
Asymptotic Power Of The Sphericity Test Under Weak And Strong Factors In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Fa Wang
Asymptotic Power Of The Sphericity Test Under Weak And Strong Factors In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Fa Wang
Center for Policy Research
This paper studies the asymptotic power for the sphericity test in a fixed effect panel data model proposed by Baltagi, Feng and Kao (2011), (JBFK). This is done under the alternative hypotheses of weak and strong factors. By weak factors, we mean that the Euclidean norm of the vector of the factor loadings is O(1). By strong factors, we mean that the Euclidean norm of the vector of factor loadings is O(pn), where n is the number of individuals in the panel. To derive the limiting distribution of JBFK under the alternative, we first derive the limiting distribution of its …
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as special cases.
Testing For Spatial Lag And Spatial Error Dependence In A Fixed Effects Panel Data Model Using Double Length Artificial Regressions, Badi H. Baltagi, Long Liu
Testing For Spatial Lag And Spatial Error Dependence In A Fixed Effects Panel Data Model Using Double Length Artificial Regressions, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper revisits the joint and conditional Lagrange Multiplier tests derived by Debarsy and Ertur (2010) for a fixed effects spatial lag regression model with spatial auto-regressive error, and derives these tests using artificial Double Length Regressions (DLR). These DLR tests and their corresponding LM tests are compared using an empirical example and a Monte Carlo simulation.
Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu
Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu
Center for Policy Research
This paper studies the estimation of change point in panel models. We extend Bai (2010) and Feng, Kao and Lazarová (2009) to the case of stationary or nonstationary regressors and error term, and whether the change point is present or not. We prove consistency and derive the asymptotic distributions of the Ordinary Least Squares (OLS) and First Difference (FD) estimators. We find that the FD estimator is robust for all cases considered.
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
Center for Policy Research
Building upon the work of Chen et al. (2010), this paper proposes a test for sphericity of the variance-covariance matrix in a fixed effects panel data regression model without the normality assumption on the disturbances.
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.
Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach, Sung Jae Jun, Yoonseok Lee, Youngki Shin
Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach, Sung Jae Jun, Yoonseok Lee, Youngki Shin
Center for Policy Research
We propose the sharp identifiable bounds of the distribution functions of potential outcomes using a panel with fixed T. We allow for the possibility that the statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the bounds. Dynamics in the treatment decisions is allowed as long as the stationarity assumptions are satisfied. In particular, we present an example where our assumptions are satisfied and the treatment decision of the present time may depend on the treatments and the observed outcomes of the past. As an empirical illustration …
Spatial Lag Models With Nested Random Effects: An Instrumental Variable Procedure With An Application To English House Prices, Badi H. Baltagi, Bernard Fingleton, Alain Pirotte
Spatial Lag Models With Nested Random Effects: An Instrumental Variable Procedure With An Application To English House Prices, Badi H. Baltagi, Bernard Fingleton, Alain Pirotte
Center for Policy Research
This paper sets up a nested random effects spatial autoregressive panel data model to explain annual house price variation for 2000-2007 across 353 local authority districts in England. The estimation problem posed is how to allow for the endogeneity of the spatial lag variable producing the simultaneous spatial spillover of prices across districts together with the nested random effects in a panel data setting. To achieve this, the paper proposes new estimators based on the instrumental variable approaches of Kelejian and Prucha (1998) and Lee (2003) for the cross-sectional spatial autoregressive model. Monte Carlo results show that our estimators perform …
Estimation And Prediction In The Random Effects Model With Ar(P) Remainder Disturbances, Badi Baltagi, Long Liu
Estimation And Prediction In The Random Effects Model With Ar(P) Remainder Disturbances, Badi Baltagi, Long Liu
Center for Policy Research
This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example.
The Hausman-Taylor Panel Data Model With Serial Correlation, Badi Baltagi, Long Liu
The Hausman-Taylor Panel Data Model With Serial Correlation, Badi Baltagi, Long Liu
Center for Policy Research
This paper modifies the Hausman and Taylor (1981) panel data estimator to allow for serial correlation in the remainder disturbances. It demonstrates the gains in efficiency of this estimator versus the standard panel data estimators that ignore serial correlation using Monte Carlo experiments.
Test Of Hypotheses In Panel Data Models When The Regressor And Disturbances Are Possibly Nonstationary, Badi H. Baltagi, Chihwa Kao, Sanggon Na
Test Of Hypotheses In Panel Data Models When The Regressor And Disturbances Are Possibly Nonstationary, Badi H. Baltagi, Chihwa Kao, Sanggon Na
Center for Policy Research
This paper considers the problem of hypotheses testing in a simple panel data regression model with random individual effects and serially correlated disturbances. Following Baltagi, Kao and Liu (2008), we allow for the possibility of non-stationarity in the regressor and/or the disturbance term. While Baltagi et al. (2008) focus on the asymptotic properties and distributions of the standard panel data estimators, this paper focuses on test of hypotheses in this setting. One important finding is that unlike the time series case, one does not necessarily need to rely on the “super-efficient” type AR estimator by Perron and Yabu (2009) to …