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- Specification test (6)
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- Structural change (3)
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- Adaptive Estimation (1)
- Adaptive Lasso (1)
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- Asymptotics; Bootstrap; Quasi-MLE; Transformed panels; Variance-covariance matrix estimate. (1)
- Bootstrap standard errors (1)
- Break point (1)
- Classification (1)
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- Common threshold effect (1)
- Conditional Heteroskedasticity (1)
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Articles 1 - 21 of 21
Full-Text Articles in Econometrics
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Liangjun Su
in multiple linear regression models via group fused Lasso (least absolute shrinkage
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
Liangjun Su
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 …
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Liangjun Su
Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Liangjun Su
In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified …
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Y. Chen, A. Ullah
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Y. Chen, A. Ullah
Liangjun Su
We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for …
Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang
Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang
Liangjun Su
Monotonicity in a scalar unobservable is a common assumption when modeling heterogeneity in structural models. Among other things, it allows one to recover the underlying structural function from certain conditional quantiles of observables. Nevertheless, monotonicity is a strong assumption and in some economic applications unlikely to hold, e.g., random coefficient models. Its failure can have substantive adverse consequences, in particular inconsistency of any estimator that is based on it. Having a test for this hypothesis is hence desirable. This paper provides such a test for cross-section data. We show how to exploit an exclusion restriction together with a conditional independence …
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Liangjun Su
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time …
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Q. Chen
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Q. Chen
Liangjun Su
This paper proposes a residual-based LM test for slope homogeneity in large dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals, and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions which allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional …
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Liangjun Su
We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.
Specification Testing For Transformation Models With Applications To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Specification Testing For Transformation Models With Applications To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Liangjun Su
This paper provides a nonparametric test of the specification of a transformation model. Specifically, we test whether an observable outcome Y is monotonic in the sum of a function of observable covariates X plus an unobservable error U. Transformation models of this form are commonly assumed in economics, including, e.g., standard specifications of duration models and hedonic pricing models. Our test statistic is asymptotically normal under local alternatives and consistent against nonparametric alternatives violating the implied restriction. Monte Carlo experiments show that our test performs well in finite samples. We apply our results to test for specifications of generalized accelerated …
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
Liangjun Su
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 …
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Liangjun Su
This paper considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove the consistency of our test. To improve the finite sample performance of our test, we also propose a residual-based wild bootstrap and justify its asymptotic validity. We conduct a small set of Monte Carlo simulations to investigate the …
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
Liangjun Su
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 …
Qml Estimation Of Dynamic Panel Data Models With Spatial Errors, Liangjun Su, Zhenlin Yang
Qml Estimation Of Dynamic Panel Data Models With Spatial Errors, Liangjun Su, Zhenlin Yang
Liangjun Su
We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may …
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
Liangjun Su
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 …
Instrumental Variable Quantile Estimation Of Spatial Autoregressive Models, Liangjun Su, Zhenlin Yang
Instrumental Variable Quantile Estimation Of Spatial Autoregressive Models, Liangjun Su, Zhenlin Yang
Liangjun Su
We propose an instrumental variable quantile regression (IVQR) estimator for spatial autoregressive (SAR) models. Like the GMM estimators of Lin and Lee (2006) and Kelejian and Prucha (2006), the IVQR estimator is robust against heteroscedasticity. Unlike the GMM estimators, the IVQR estimator is also robust against outliers and requires weaker moment conditions. More importantly, it allows us to characterize the heterogeneous impact of variables on different points (quantiles) of a response distribution. We derive the limiting distribution of the new estimator. Simulation results show that the new estimator performs well in finite samples at various quantile points. In the special …
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Y. Chen, A. Ullah
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Y. Chen, A. Ullah
Liangjun Su
We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for …
Conditional Independence Specification Testing For Dependent Processes With Local Polynomial Quantile Regression, Liangjun Su, Halbert L. White
Conditional Independence Specification Testing For Dependent Processes With Local Polynomial Quantile Regression, Liangjun Su, Halbert L. White
Liangjun Su
We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also …
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju
Liangjun Su
The paper develops a systematic estimation and inference procedure for quantile regression models where there may exist a common threshold effect across different quantile indices. We first propose a sup-Wald test for the existence of a threshold effect, and then study the asymptotic properties of the estimators in a threshold quantile regression model under the shrinking-threshold-effect framework. We consider several tests for the presence of a common threshold value across different quantile indices and obtain their limiting distributions. We apply our methodology to study the pricing strategy for reputation via the use of a dataset from Taobao.com. In our economic …
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Liangjun Su
In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton-Raphson estimator and the local profile likelihood estimator are investigated. We show that the proposed estimators are asymptotically equivalent to the infeasible local likelihood estimators [e.g., Aerts and Claeskens (1997) Journal of the American Statistical Association 92, 1536-1545], which require knowledge of the error distribution. Simulation evidence suggests that when the distribution of …
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Liangjun Su
This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference …