Simultaneous Spatial Panel Data Models with Common Shocks
I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further investigate the finite sample performance of both methods using Monte Carlo simulations. I find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, I apply the model to analyze the relationship between trade and GDP using a panel data over time and across countries.
AUTHORS: Lu, Lina
Even one is too much: the economic consequences of being a smoker
It is well known that smoking leads to lower wages. However, the mechanism of this negative relationship is not well understood. This analysis includes a decomposition of the wage gap between smokers and nonsmokers, with a variety of definitions of smoking status designed to reflect differences in smoking intensity. This paper finds that nearly two-thirds of the 24 percent selectivity-corrected smoking/nonsmoking wage differential derives from differences in characteristics between smokers and nonsmokers. These results suggest that it is not differences in productivity that drive the smoking wage gap. Rather, it is differences in the endowments smokers bring to the market along with unmeasured factors, such as baseline employer tolerance. In addition, we also determine that even one cigarette per day is enough to trigger the smoking wage gap and that this gap does not vary by smoking intensity.
AUTHORS: Hotchkiss, Julie L.; Pitts, M. Melinda
Does Medicaid Generosity Affect Household Income?
Almost all recent literature on Medicaid and labor supply has used Affordable Care Act (ACA)-induced Medicaid eligibility expansions in various states as natural experiments. Estimated effects on employment and earnings differ widely due to differences in the scope of eligibility expansion across states and are potentially subject to biases due to policy endogeneity. Using a Regression Kink Design (RKD) framework, this paper takes a uniquely different approach to the identification of the effect of Medicaid generosity on household income. Both state-level data and March CPS data from 1980?2013 suggest that generous federal funding of state-level Medicaid costs has a negative effect on household income. The negative impact of Medicaid generosity on household income is more pronounced at the lower end of the household income distribution and on the income and earnings of female heads.
AUTHORS: Kumar, Anil
Effects of US quantitative easing on emerging market economies
We estimate international spillover effects of US Quantitative Easing (QE) on emerging market economies. Using a Bayesian VAR on monthly US macroeconomic and financial data, we first identify the US QE shock with non-recursive identifying restrictions. We estimate strong and robust macroeconomic and financial impacts of the US QE shock on US output, consumer prices, long-term yields, and asset prices. The identified US QE shock is then used in a monthly Bayesian panel VAR for emerging market economies to infer the spillover effects on these countries. We find that an expansionary US QE shock has significant effects on financial variables in emerging market economies. It leads to an exchange rate appreciation, a reduction in long-term bond yields, a stock market boom, and an increase in capital inflows to these countries. These effects on financial variables are stronger for the ?Fragile Five? countries compared to other emerging market economies. We however do not find significant effects of the US QE shock on output and consumer prices of emerging markets.
AUTHORS: Bhattarai, Saroj; Chatterjee, Arpita; Park, Woong Yong
Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors
This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using co-variates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive de-meaning bias correction procedures to mitigate the small sample time series bias. Theoretical findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.
AUTHORS: Chudik, Alexander; Pesaran, M. Hashem
Large panel data models with cross-sectional dependence: a survey
This paper provides an overview of the recent literature on estimation and inference in large panel data models with cross-sectional dependence. It reviews panel data models with strictly exogenous regressors as well as dynamic models with weakly exogenous regressors. The paper begins with a review of the concepts of weak and strong cross-sectional dependence, and discusses the exponent of cross-sectional dependence that characterizes the different degrees of cross-sectional dependence. It considers a number of alternative estimators for static and dynamic panel data models, distinguishing between factor and spatial models of cross-sectional dependence. The paper also provides an overview of tests of independence and weak cross-sectional dependence.
AUTHORS: Chudik, Alexander; Pesaran, M. Hashem
House Price Growth Interdependencies and Comovement
This paper examines house price diffusion across metropolitan areas in the United States. We develop a generalization of the Hamilton and Owyang (2012) Markov-switching model, where we incorporate direct regional spillovers using a spatial weighting matrix. The Markov-switching framework allows consideration for house price movements that occur due to similar timing of downturns across MSAs. The inclusion of the spatial weighting matrix improves fit compared to a standard endogenous clustering model. We find seven clusters of MSAs that experience idiosyncratic recessions plus one distinct national house price cycle. Notably only the housing downturn associated with the Great Recession spread across all of the MSAs in our sample; other house price downturns remained contained to a single cluster. Previous research has found that housing cycles and business cycles are intertwined. To examine this potential relationship we apply our spatial Markov-switching model to employment growth data. We find that house price comovement and employment comovement are distinct across cities.
AUTHORS: Cohen, Jeffrey P.; Coughlin, Cletus C.; Soques, Daniel
Modelling Dependence in High Dimensions with Factor Copulas
his paper presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high dimensional applications, involving fifty or more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulation-based estimation using rank statistics is reliable even in high dimensions. We consider "scree" plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk.
AUTHORS: Oh, Dong Hwan; Patton, Andrew J.
A distinction between causal effects in structural and rubin causal models
Structural Causal Models define causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defines causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different definitions, notationally similar causal effects make distinct claims about the results of interventions to the system under investigation: Structural equations imply conditional independencies in the data that potential outcomes do not. One implication is that the DAG of a Rubin Causal Model is different from the DAG of a Structural Causal Model. Another is that Pearl?s do-calculus does not apply to potential outcomes and the Rubin Causal Model.
AUTHORS: Aliprantis, Dionissi
Evidence on the Production of Cognitive Achievement from Moving to Opportunity
This paper performs a subgroup analysis on the effect of receiving a Moving to Opportunity (MTO) housing voucher on test scores. I find evidence of heterogeneity by number of children in the household in Boston, gender in Chicago, and race/ethnicity in Los Angeles. To study the mechanisms driving voucher effect heterogeneity, I develop a generalized Rubin Causal Model and propose an estimator to identify transition-specific Local Average Treatment Effects (LATEs) of school and neighborhood quality. Although I cannot identify such LATEs with the MTO data, the analysis demonstrates that membership in a specific demographic group is more predictive of voucher effects than is the group?s average change in school or neighborhood quality. I discuss some possible explanations.
AUTHORS: Aliprantis, Dionissi