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Articles 1 - 30 of 34
Full-Text Articles in Social and Behavioral Sciences
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 …
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.
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 …
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 …
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 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 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 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 …
Fitting Event-History Models To Uneventful Data, Douglas A. Wolf, Thomas M. Gill
Fitting Event-History Models To Uneventful Data, Douglas A. Wolf, Thomas M. Gill
Center for Policy Research
Data with which to study disability dynamics usually take the form of successive current-status measures of disability rather than a record of events or spell durations. One recent paper presented a semi-Markov model of disability dynamics in which spell durations were inferred from sequences of current-status measures taken at 12-month intervals. In that analysis, it was assumed that no unobserved disablement transitions occurred between annual interviews. We use data from a longitudinal survey in which participants' disability was measured at monthly intervals, and simulate the survival curves for remaining disabled that would be obtained with 1- and 12-month follow-up intervals. …
Testing For Instability In Factor Structure Of Yield Curves, Dennis Philip, Chihwa Kao, Giovanni Urga
Testing For Instability In Factor Structure Of Yield Curves, Dennis Philip, Chihwa Kao, Giovanni Urga
Center for Policy Research
A widely relied upon but a formally untested consideration is the issue of stability in actors underlying the term structure of interest rates. In testing for stability, practitioners as well as academics have employed ad hoc techniques such as splitting the sample into a few sub-periods and determining whether the factor loadings have appeared to be similar over all sub-periods. Various authors have found mixed evidence on stability in the actors. In this paper we develop a formal testing procedure to evaluate the factor structure stability of the US zero coupon yield term structure. We find the factor structure of …
Copula-Based Tests For Cross-Sectional Independence In Panel Models, Chihwa Kao, Giovanni Urga
Copula-Based Tests For Cross-Sectional Independence In Panel Models, Chihwa Kao, Giovanni Urga
Center for Policy Research
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Worldwide Econometrics Rankings: 1989-2005, Badi H. Baltagi
Worldwide Econometrics Rankings: 1989-2005, Badi H. Baltagi
Center for Policy Research
This paper updates Baltagi's (2003, Econometric Theory 19, 165-224) rankings of academic institutions by publication activity in econometrics from 1989-1999 to 1989-2005. This ranking is based on 16 leading international journals that publish econometrics articles. It is compared with the prior rankings by Hall (1980, 1987) for the period 1980-1988. In addition, a list of the top 150 individual producers of econometrics in these 16 journals over this 17-year period is provided. This is done for theoretical econometrics as well as all contributions in econometrics. Sensitivity analysis is provided using (i) alternative weighting factors given to the 16 journals taking …
Panel Cointegration With Global Stochastic Trends, Jushan Bai, Chihwa Kao, Serena Ng
Panel Cointegration With Global Stochastic Trends, Jushan Bai, Chihwa Kao, Serena Ng
Center for Policy Research
This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservable I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously updated and bias-corrected) and the CupFM (continuously updated and fully modified) estimators. We establish their consistency and derive their limiting distributions. Both are asymptotically unbiased and asymptotically normal and permit inference to be conducted using standard test statistics. The …
A Monte Carlo Study Of Efficiency Estimates From Frontier Models, William Clinton Horrace, Seth O. Richards
A Monte Carlo Study Of Efficiency Estimates From Frontier Models, William Clinton Horrace, Seth O. Richards
Center for Policy Research
Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimated (a) the conditional means of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are more reliable in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance in the stochastic frontier environment.
A Monte Carlo Study For Pure And Pretest Estimators Of A Panel Data Model With Spatially Auto Correlated Disturbances, Badi H. Baltagi, Peter Egger, Michael Pfaffermayr
A Monte Carlo Study For Pure And Pretest Estimators Of A Panel Data Model With Spatially Auto Correlated Disturbances, Badi H. Baltagi, Peter Egger, Michael Pfaffermayr
Center for Policy Research
This paper examines the consequences of model misspecification using a panel data model with spatially auto correlated disturbances. The performance of several maximum likelihood estimators assuming different specifications for this model are compared using Monte Carlo experiments. These include (i) MLE of a random effects model that ignore the spatial correlation; (ii) MLE described in Anselin (1988) which assumes that the individual effects are not spatially auto correlated; (iii) MLE described in Kapoor et al. (2006) which assumes that both the individual effects and the remainder error are governed by the same spatial autocorrelation; (iv) MLE described in Baltagi et …
Identifying Technically Efficient Fishing Vessels: A Non-Empty, Minimal Subset Approach, Alfonso Flores-Lagunes, William Clinton Horrace, Kurt E. Schnier
Identifying Technically Efficient Fishing Vessels: A Non-Empty, Minimal Subset Approach, Alfonso Flores-Lagunes, William Clinton Horrace, Kurt E. Schnier
Center for Policy Research
There is a growing resource economics literature, concerning the estimation of the technical efficiency of fishing vessels utilizing the stochastic frontier model. In these models, vessel output is regressed on a linear function of vessel inputs and a random composed error. Using parametric assumptions on the regression residual, estimates of vessel technical efficiency are calculated as the mean of a truncated normal distribution and are often reported in a rank statistic as a measure of a captain's skill and used to estimate excess capacity within fisheries. We demonstrate analytically that these measures are potentially flawed, and extend the results of …
Panel Unit Root Tests And Spatial Dependence, Badi H. Baltagi, Georges Bresson, Alain Pirotte
Panel Unit Root Tests And Spatial Dependence, Badi H. Baltagi, Georges Bresson, Alain Pirotte
Center for Policy Research
This paper studies the performance of panel unit root tests when spatial effects are present that account for cross-section correlation. Monte Carlo simulations show that there can be considerable size distortions in panel unit root tests when the true specification exhibits spatial error correlation. These tests are applied to a panel data set on net real income from the 1000 largest French communes observed over the period 1985-1998.