Special issue on forecasting, use of survey data on expectations, and panel data applications: editors’ introduction

[First paragraph]This is a Special Issue of Empirical Economics (EE) in honor of Kajal Lahiri. Kajal is Distinguished Professor of Economics at SUNY- Albany. Kajal finished his Ph.D. in economics in 1975 from the University of Rochester. He has published over 120 articles in professional journals like the American Economic Review, Econometrica, Journal of Econometrics, JASA, JAMA, and written/coedited a number of books and journal volumes

Prediction in a Generalized Spatial Panel Data Model with Serial Correlation

This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (Spatial panels: random components versus fixed effects. International Economic Review 2012; 53: 1369–1412.), which encompasses many 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

Health care expenditure and income in the OECD reconsidered: Evidence from panel data

This paper reconsiders the long-run economic relationship between health care expenditure and income using a panel of 20 OECD countries observed over the period 1971-2004. In particular, the paper studies the non-stationarity and cointegration properties between health care spending and income. This is done in a panel data context controlling for both cross- section dependence and unobserved heterogeneity. Cross-section dependence is modelled through a common factor model and through spatial dependence. Heterogeneity is handled through fixed effects in a panel homogeneous model and through a panel heterogeneous model. Our findings suggest that health care is a necessity rather than a luxury, with an elasticity much smaller than that estimated in previous studies

Structural changes in heterogeneous panels with endogenous regressors

This paper extends Pesaran's (Econometrica, 2006, 74, 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope heterogeneity and cross‐sectional dependence, we find that Pesaran's CCE approach is also valid when dealing with unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. This is supported by Monte Carlo experiments

Structural changes in heterogeneous panels with endogenous regressors

This paper extends Pesaran's (Econometrica, 2006, 74, 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope heterogeneity and cross‐sectional dependence, we find that Pesaran's CCE approach is also valid when dealing with unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. This is supported by Monte Carlo experiments

Asymptotic power of the sphericity test under weak and strong factors in a fixed effects panel data model

This paper studies the asymptotic power for the sphericity test in a fixed effect panel data model proposed by Baltagi et al. (2011 Baltagi, B. H., Feng, Q., Kao, C. (2011). Testing for sphericity in a fixed effects panel data model. Econometrics Journal 14:25–47.), (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 , 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 raw data counterpart. Our results show that, when the factor is strong, the test statistic diverges in probability to infinity as fast as Op(nT). However, when the factor is weak, its limiting distribution is a rightward mean shift of the limit distribution under the null. Second, we derive the asymptotic behavior of the difference between JBFK and its raw data counterpart. Our results show that when the factor is strong, this difference is as large as Op(n). In contrast, when the factor is weak, this difference converges in probability to a constant. Taken together, these results imply that when the factor is strong, JBFK is consistent, but when the factor is weak, JBFK is inconsistent even though its asymptotic power is nontrivial

Determinants of firm-level domestic sales and exports with spillovers: Evidence from China

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

Identification and estimation of a large factor model with structural instability

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 of pre and post-break factors and convergence rate of the estimated pre and post-break factor space are then established under fairly general assumptions. The finite sample performance of our estimators is investigated using Monte Carlo experiments

Network effects on labor contracts of internal migrants in China: a spatial autoregressive model

This paper studies the fact that 37% of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant’s decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the co-villager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants’ contract decisions: job referral mechanism, limited information on contract benefits, and the “mini-labor union” formed among co-villagers, which substitutes for a formal contract. In the subsample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications