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Full-Text Articles in Social and Behavioral Sciences
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Research Collection School Of Economics
We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Research Collection School Of Economics
We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Research Collection School Of Economics
Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G-non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G-non-causality to test …
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …
A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei
A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei
Research Collection School Of Economics
This paper provides a practical test for strict exogeneity in linear panel data models with fixed effects when the number of individuals N goes to infinity while the number of time periods T is fixed. The test is based on the supremum of a sequence of Wald test statistics. Under suitable conditions, we establish the asymptotic distribution of the test statistic and consistency of the test. A bootstrap procedure is proposed to improve the finite sample performance and the validity of the procedure is justified. We investigate the finite sample performance of the test via a small set of Monte …
Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su
Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We establish the asymptotic distributions of the Lasso estimators of the regression coefficients and their post Lasso versions. We also propose and validate a data-driven method to determine the tuning parameter used in …
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino
Research Collection School Of Economics
In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Research Collection School Of Economics
Granger non-causality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G- non-causality follows from structural non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G- non-causality …
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this paper we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Research Collection School Of Economics
We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon andWeidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to …
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Research Collection School Of Economics
In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure …
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Research Collection School Of Economics
In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.
Testing For Common Trends In Semi-Parametric Panel Data Models With Fixed Effects, Yonghui Zhang, Liangjun Su, Peter C. B. Phillips
Testing For Common Trends In Semi-Parametric Panel Data Models With Fixed Effects, Yonghui Zhang, Liangjun Su, Peter C. B. Phillips
Research Collection School Of Economics
This paper proposes a non-parametric test for common trends in semi-parametric panel data models with fixed effects based on a measure of non-parametric goodness-of-fit (R2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the non-parametric R2 for each cross-section unit. The proposed test statistic is obtained by averaging all cross-sectional non-parametric R2s, …
Asymptotic Variance And Extensions Of A Denisty-Weighted-Response Semiparametric Estimator, Myoung-Jae Lee, Fali Huang, Young-Sook Kim
Asymptotic Variance And Extensions Of A Denisty-Weighted-Response Semiparametric Estimator, Myoung-Jae Lee, Fali Huang, Young-Sook Kim
Research Collection School Of Economics
Building on some early works, Lewbel (2000) proposed estimators for binary and ordered discrete response models with endogenous regressors. These estimators have been extended for panel data and for truncated and censored models by later papers. The estimators are particularly innovative in that the latent linear regression functions are pulled out of the nonlinear limited dependent variable models, which are then treated as if they were the usual linear models. But understanding the estimators and their applications have been “hampered” by less-than-ideal expositions and assumptions. For this problem, this short note reviews the estimators and makes the following three points. …
Profile Likelihood Estimation Of Partially Linear Panel Data Models With Fixed Effects, Liangjun Su, Aman Ullah
Profile Likelihood Estimation Of Partially Linear Panel Data Models With Fixed Effects, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We consider consistent estimation of partially linear panel data models with fixed effects. We propose profile-likelihood-based estimators for both the parametric and nonparametric components in the models and establish convergence rates and asymptotic normality for both estimators.
A Semi-Parametric Estimator For Censored Selection Models With Endogeneity, Myoung-Jae Lee, Francis Vella
A Semi-Parametric Estimator For Censored Selection Models With Endogeneity, Myoung-Jae Lee, Francis Vella
Research Collection School Of Economics
We propose a semi-parametric least-squares estimator for a censored-selection (type 3 tobit) model under the mean independence of the outcome equation error u from the regressors given the selection indicator and its error term ɛ. This assumption is relatively weak in comparison to alternative estimators for this model and allows certain unknown forms of heteroskedasticity, an asymmetric error distribution, and an arbitrary relationship between the u and ɛ. The estimator requires only one-dimensional smoothing on the estimate of ɛ. We generalize the estimator to allow for an endogenous regressor whose equation contains an error w related to u and discuss …
Q-Convergence With Interquartile Ranges, Sung Jin Kang, Myoung-Jae Lee
Q-Convergence With Interquartile Ranges, Sung Jin Kang, Myoung-Jae Lee
Research Collection School Of Economics
We introduce a new convergence concept ‘Q-convergence’ which defines convergence in national incomes as a shrinking interquartile range (IQR) of the national income distribution. Compared with the other convergence definitions in the literature, Q-convergence has the following advantages. First, IQR, which represents dispersion and inequality of the income distribution, is also closely linked to the two-group clustering with the lower and upper quartiles being the ‘centers’ of the two groups. Second, IQR is equivariant to increasing transformations and thus reconciles better conflicting empirical findings using level or log data. Third, IQR is insensitive to outliers, leading to robust statistical inferences. …
Analysis Of Private Transfers With Panel Fixed Effect Censored Model Estimator, Sung Jin Kang, Myoung-Jae Lee
Analysis Of Private Transfers With Panel Fixed Effect Censored Model Estimator, Sung Jin Kang, Myoung-Jae Lee
Research Collection School Of Economics
Understanding private transfer is important for safety-net policies because private transfer provides economic benefits similar to those of public programs such as unemployment insurance and pension. Applying Honoré’s [Econometrica 60 (1992) 533] panel fixed-effect censored model estimator to Korean data, we show that private transfer is altruistically motivated and there is a strong crowding-out effect of public transfer on private transfer. We also find that low-income people suffered to different degrees during the financial crisis period of 1997 to 1998. This finding and the crowding-out effect may be taken as failures of the Korean public transfer programs during the period.