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Articles 1 - 30 of 43
Full-Text Articles in Econometrics
The Mundlak Estimator In A Panel Data Model With Serially Correlated Error Component Disturbances, Badi H. Baltagi, Long Liu
The Mundlak Estimator In A Panel Data Model With Serially Correlated Error Component Disturbances, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper shows that the classic Mundlak (1978) result where the random effects estimator reduces to the fixed effects estimator when the regressors are all correlated with the individual effects, may not hold if the remainder disturbances have a general serial correlation variance-covariance matrix. This includes the popular AR(1), MA(1) and ARMA(p, q) processes for serial correlation. This is illustrated with an empirical example for the AR(1) case.
Identifying Common Trend Determinants In Panel Data, Yoonseok Lee, Peter C. Phillips, Suyong Song, Donggyu Sul
Identifying Common Trend Determinants In Panel Data, Yoonseok Lee, Peter C. Phillips, Suyong Song, Donggyu Sul
Center for Policy Research
This paper develops a novel method for identifying observable determinants of latent common trends in nonstationary panel data, which are typically removed or controlled in two-way fixed effects regressions. By examining cross sectional dispersion processes, we assess whether panel series exhibit distributional convergence toward specific observed time series, revealing them as long run determinants of the underlying latent trend. The approach also offers a new perspective on cointegration between time series and panel data, focusing on the relative variation of the panel data with respect to the cointegration error. Applying this method to U.S. state-level crime rates demonstrates that the …
Time Invariant Variables In The Mundlak And Hausman-Taylor Panel Data Models, Badi H. Baltagi, Long Liu
Time Invariant Variables In The Mundlak And Hausman-Taylor Panel Data Models, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper shows that the classic augmented Mundlak (1978) regression yields the between estimator for time-invariant variables. It is well known that the estimates of the time varying variables yield the fixed effects estimates. While the latter are consistent for this correlated random effects model, the between estimates are not. The between estimator is consistent only when the Hausman (1978) test does not reject the null based on between versus fixed effects. An alternative modified Hausman and Taylor (1981) estimator is proposed. Monte Carlo experiments are performed to compare various estimators under a correlated random effects as well as a …
Two Special Spatial Weight Matrices And Their Effects On Estimation And Testing In Spatial Regressions, Badi H. Baltagi, Long Liu
Two Special Spatial Weight Matrices And Their Effects On Estimation And Testing In Spatial Regressions, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper collects in one place some of the consequences of using two special spatial weight matrices in the spatial econometrics literature. The two weight matrices considered are the equal weight spatial matrix and the complete bipartite network. In particular, we summarize the effects of these special weight matrices on the statistical properties of commonly used spatial estimators as well as commonly used tests for spatial dependence.
The Basics Of The Mundlak And Chamberlain Projections, Badi H. Baltagi, Tom Wansbeek
The Basics Of The Mundlak And Chamberlain Projections, Badi H. Baltagi, Tom Wansbeek
Center for Policy Research
One of the best-known results in panel data econometrics, due to Mundlak (1978), is the equality of the random-effects and fixed-effects estimators when the individual effects are correlated with the means over time of the regressors. Chamberlain (1980) showed that the same result holds when the individual effects are correlated with the regressors for all moments in time separately. In this chapter, we review basic elements of the Mundlak and Chamberlain projections. We emphasize the simplicity that is often obtained when the model is transformed into the within and between-model, following Arellano (1993). Topics that we discuss include the augmented …
Estimation Of Serially Correlated Error Components Models Using Whittle’S Approximate Maximum Likelihood Method, Badi H. Baltagi, Georges Bresson, Jean-Michel Etienne
Estimation Of Serially Correlated Error Components Models Using Whittle’S Approximate Maximum Likelihood Method, Badi H. Baltagi, Georges Bresson, Jean-Michel Etienne
Center for Policy Research
This chapter studies the estimation of error components models with serial correlation of the 𝐴𝑅𝑀𝐴(𝑝, 𝑞) type using Whittle’s (Whittle, 1953) approximate maximum likelihood method. This is done for the one-way and two-way error components panel data model. Monte Carlo simulations are performed that investigate the small sample performance of this method.
Estimation And Testing In A Fixed Effects Panel Data Model With Serially Correlated Error Component Disturbances, Badi H. Baltagi, Long Liu
Estimation And Testing In A Fixed Effects Panel Data Model With Serially Correlated Error Component Disturbances, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper revisits the fixed effects panel data model with AR(1) remainder disturbances and provides a bias corrected estimator for the serial correlation coefficient based on first differencing the panel regression to get rid of the fixed effects. This bias corrected estimator builds upon the estimator proposed by Han and Phillips (2010). Asymptotic properties as well as Monte Carlo results are provided that show the better performance of this new proposed bias corrected estimator. This is extended to the unbalanced panel data case and also illustrated using the empirical application in Donohue and Levitt (2001).
Two-Stage Least Squares Estimation In A Spatial Lag Model Under A Complete Bipartite Network, Badi H. Baltagi, Long Liu
Two-Stage Least Squares Estimation In A Spatial Lag Model Under A Complete Bipartite Network, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper considers a spatial lag model with a complete bipartite network weighting matrix which is important in network theory. We show that two-stage least squares is equivalent to ordinary least squares and both estimators are inconsistent for the cross-section data case. This result has also been derived for the spatial lag model with an equal weight matrix by Kelejian and Prucha (2002). We also show that the fixed effects two-stage least squares estimator is consistent in case we have panel data and the spatial lag model includes time fixed effects. This is different from the result for an equal …
Nonstationary Heterogeneous Panels With Multiple Structural Changes, Badi H. Baltagi, Qu Feng, Wei Wang
Nonstationary Heterogeneous Panels With Multiple Structural Changes, Badi H. Baltagi, Qu Feng, Wei Wang
Center for Policy Research
Nonstationary panels have been widely used in empirical studies in macroeconomics and finance. This paper considers multiple structural changes in nonstationary heterogeneous panels with common factors. Kapetanios, Pesaran, Yamagata (2011) showed that unobserved nonstationary factors can be proxied by cross-sectional averages of observable data. This means that unobserved error factors can be treated as additional regressors, and different break points in slopes and error factor loadings can be considered as multiple breaks in linear regression models with panel data. We generalize the least squares approach by Bai and Perron (1998) to nonstationary panels and show that the break points in …
Determinants Of Firm-Level Domestic Sales And Exports With Spillovers: Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina
Determinants Of Firm-Level Domestic Sales And Exports With Spillovers: Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina
Center for Policy Research
This paper studies the determinants of firm-level revenues, as a measure of the performance of firms in China's domestic and export markets. The analysis of the determinants of the aforementioned outcomes calls for a mixed linear-nonlinear econometric approach. The paper proposes specifying a system of equations, which is inspired by Basmann's work and recent theoretical work in international economics and conducts comparative static analyses regarding the role of exogenous shocks to the system to flesh out the relative importance of transmissions across outcomes.
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 …
The Academic Effects Of Chronic Exposure To Neighborhood Violence, Amy Ellen Schwartz, Agustina Laurito, Johanna Lacoe, Patrick Sharkey, Ingrid Gould Ellen
The Academic Effects Of Chronic Exposure To Neighborhood Violence, Amy Ellen Schwartz, Agustina Laurito, Johanna Lacoe, Patrick Sharkey, Ingrid Gould Ellen
Center for Policy Research
We estimate the causal effect of repeated exposure to violent crime on test scores in New York City. We use two distinct empirical strategies; value-added models linking student performance on standardized exams to violent crimes on a student’s residential block, and a regression discontinuity approach that identifies the acute effect of an additional crime exposure within a one-week window. Exposure to violent crime reduces academic performance. Value added models suggest the average effect is very small; approximately -0.01 standard deviations in English Language Arts (ELA) and mathematics. RD models suggest a larger effect, particularly among children previously exposed. The marginal …
Stationary Points For Parametric Stochastic Frontier Models, William C. Horrace, Ian A. Wright
Stationary Points For Parametric Stochastic Frontier Models, William C. Horrace, Ian A. Wright
Center for Policy Research
The results of Waldman (1982) on the Normal-Half Normal stochastic frontier model are generalized using the theory of the Dirac delta (Dirac, 1930), and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and "wrong skew" results are derived or simulated for common parametric assumptions on the model. Identification is discussed.
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.
Bayesian Spatial Bivariate Panel Probit Estimation, Badi Baltagi, Peter H. Egger, Michaela Kesina
Bayesian Spatial Bivariate Panel Probit Estimation, Badi Baltagi, Peter H. Egger, Michaela Kesina
Center for Policy Research
This paper formulates and analyzes Bayesian model variants for the analysis of systems of spatial panel data with binary dependent variables. The paper focuses on cases where latent variables of cross-sectional units in an equation of the system contemporaneously depend on the values of the same and, eventually, other latent variables of other cross-sectional units. Moreover, the paper discusses cases where time-invariant effects are exogenous versus endogenous. Such models may have numerous applications in industrial economics, public economics, or international economics. The paper illustrates that the performance of Bayesian estimation methods for such models is supportive of their use with …
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.
Averaged Instrumental Variables Estimators, Yoonseok Lee, Yu Zhou
Averaged Instrumental Variables Estimators, Yoonseok Lee, Yu Zhou
Center for Policy Research
We develop averaged instrumental variables estimators as a way to deal with many weak instruments. We propose a weighted average of the preliminary k-class estimators, where each estimator is obtained using different subsets of the available instrumental variables. The averaged estimators are shown to be consistent and to satisfy asymptotic normality. Furthermore, its approximate mean squared error reveals that using a small number of instruments for each preliminary k-class estimator reduces the finite sample bias, while averaging prevents the variance from inflating. Monte Carlo simulations find that the averaged estimators compare favorably with alternative instrumental-variable-selection approaches when the strength levels …
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Center for Policy Research
This paper extends Pesaran's (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency …
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.
Adaptive Elastic Net Gmm Estimation With Many Invalid Moment Conditions: Simultaneous Model And Moment Selection, Mehmet Caner, Xu Han, Yoonseok Lee
Adaptive Elastic Net Gmm Estimation With Many Invalid Moment Conditions: Simultaneous Model And Moment Selection, Mehmet Caner, Xu Han, Yoonseok Lee
Center for Policy Research
This paper develops the adaptive elastic net GMM estimator in large dimensional models with many possibly invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the quadratic regularization and the adaptively weighted lasso shrinkage. The new estimation procedure consistently selects both the nonzero structural parameters and the valid moment conditions. At the same time, it uses information only from the valid moment conditions to estimate the selected structural parameters and thus achieves …
Sources Of Productivity Spillovers: Panel Data Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina
Sources Of Productivity Spillovers: Panel Data Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina
Center for Policy Research
This paper assesses sources of productivity spillovers in China's electric and electronic manufacturing industry using a rich panel data-set of 25,360 firms observed over the period 2004-2007. This industry is characterized by its important reliance on technology. In particular, the paper focuses on the role of other firms' productivity as well as productivity shifters in affecting own firm-level total factor productivity. In addition, this paper examines the possible difference between spillovers from foreign-owned units and from units which participate at global markets through exporting in comparison to domestically-owned and non-exporting units. We find evidence of stronger spillovers from exporting firms …
Firm-Level Productivity Spillovers In China’S Chemical Industry: A Spatial Hausman-Taylor Approach, Peter H. Egger, Badi H. Baltagi, Michaela Kesina
Firm-Level Productivity Spillovers In China’S Chemical Industry: A Spatial Hausman-Taylor Approach, Peter H. Egger, Badi H. Baltagi, Michaela Kesina
Center for Policy Research
This paper assesses the role of intra-sectoral spillovers in total factor productivity across Chinese producers in the chemical industry. We use a rich panel data-set of 12,552 firms observed over the period 2004-2006 and model output by the firm as a function of skilled and unskilled labor, capital, materials, and total factor productivity, which is broadly defined. The latter is a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable total factor productivity. Despite the richness of our data-set, it suffers from the lack of time …
Random Effects, Fixed Effects And Hausman’S Test For The Generalized Mixed Regressive Spatial Autoregressive Panel, Badi Baltagi, Long Liu
Random Effects, Fixed Effects And Hausman’S Test For The Generalized Mixed Regressive Spatial Autoregressive Panel, Badi Baltagi, Long Liu
Center for Policy Research
This paper suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive panel data model. This extends the generalized spatial panel model of Baltagi, Egger and Pfaffermayr (2013) by the inclusion of a spatial lag dependent variable. The estimation method utilizes the Generalized Moments method suggested by Kapoor, Kelejian, and Prucha (2007) for a spatial autoregressive panel data model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the …
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.
Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini
Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini
Center for Policy Research
We consider a production function model that transforms worker inputs into outputs through peer effect networks. The distinguishing features of this production model are that the network is formal and observable through worker scheduling, and selection into the network is done by a manager. We discuss identification and suggest a variety of estimation techniques. In particular, we tackle endogeneity issues arising from selection into groups and exposure to common group factors by employing a polychotomous Heckman-type selection correction. We illustrate our method using data from the Syracuse University Men’s Basketball team, where at any point in time the coach selects …
Testing For Heteroskedasticity And Spatial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Seuck Heun Song, Jae Hyeok Kwon
Testing For Heteroskedasticity And Spatial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Seuck Heun Song, Jae Hyeok Kwon
Center for Policy Research
A panel data regression model with heteroskedastic as well as spatially correlated disturbance is considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation, or spatial correlation in testing for homoskedasticity. Monte Carlo results show that these LM tests as well as their LR counterparts perform well even for small N and …
Testing For Heteroskedasticity And Serial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Byoung Cheol Jung, Seuck Heun Song
Testing For Heteroskedasticity And Serial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Byoung Cheol Jung, Seuck Heun Song
Center for Policy Research
This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests, along with their likelihood ratio alternatives, have good size and power under various forms …
Testing For Random Effects And Spatial Lag Dependence In Panel Data Models, Badi H. Baltagi, Long Liu
Testing For Random Effects And Spatial Lag Dependence In Panel Data Models, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper derives a joint Lagrande Multiplier (LM) test which simultaneously tests for the absence of spatial lag dependence and random individual effects in a panel data regression model. It turns out that this LM statistic is the sum of two standard LM statistics. The first one tests for the absence of spatial lag dependence ignoring the random individual effects, and the second one tests for the absence of random individual effects ignoring the spatial lag dependence. This paper also derives two conditional LM tests. The first one tests for the absence of random individual effects without ignoring the possible …