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Full-Text Articles in Econometrics
Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang
Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang
Research Collection School Of Economics
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope …
Uniform Nonparametric Inference For Time Series, Jia Li, Zhipeng Liao
Uniform Nonparametric Inference For Time Series, Jia Li, Zhipeng Liao
Research Collection School Of Economics
This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of heteroskedasticity and autocorrelation consistent (HAC) estimators with increasing dimension are established for making feasible inference. An empirical application on the unemployment volatility …
Uniform Nonparametric Inference For Time Series Using Stata, Jia Li, Zhipeng Liao, Mengsi Gao
Uniform Nonparametric Inference For Time Series Using Stata, Jia Li, Zhipeng Liao, Mengsi Gao
Research Collection School Of Economics
In this article, we introduce a command, tssreg, that conducts nonparametric series estimation and uniform inference for time-series data, including the case with independent data as a special case. This command can be used to nonparametrically estimate the conditional expectation function and the uniform confidence band at a user-specified confidence level, based on an econometric theory that accommodates general time-series dependence. The uniform inference tool can also be used to perform nonparametric specification tests for conditional moment restrictions commonly seen in dynamic equilibrium models.
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Research Collection School Of Economics
We develop tests for deciding whether a large cross-section of asset prices obey an exact factor structure at the times of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of asset returns with asymptotically increasing cross-sectional dimension and sampling frequency, and essentially no restriction on the relative magnitude of these two dimensions of the panel. The test is formed from the high-frequency returns at the times when the risk factors are detected to have a jump. The test statistic is a cross-sectional average of a measure of discrepancy in …
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu
Research Collection School Of Economics
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 …
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
Two test statistics are proposed to determine model specification after a model is estimated by an MCMC method. The first test is the MCMC version of IOSA test and its asymptotic null distribution is normal. The second test is motivated from the power enhancement technique of Fan et al. (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the first test. It is shown that J0 converges to zero when the null model is correctly specified and diverges when the null model is misspecified. Also …
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Research Collection School Of Economics
This article considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix Wn 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 …
A Specification Test Based On The Mcmc Output, Yong Li, Jun Yu, Tao Zeng
A Specification Test Based On The Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
A test statistic is proposed to assess themodel specification after the model is estimated by Bayesian MCMC methods. Thenew test is motivated from the power enhancement technique of Fan, Liao and Yao(2015). It combines a component (J1) that tests anull point hypothesis in an expanded model and a power enhancement component (J0) obtained from the null model. It is shown that J0 converges to zero when the null model is correctly specified anddiverges when the null model is misspecified. Also shown is that J1 is asymptotically X2-distributed, suggesting that theproposed test is asymptotically pivotal, when the null model is correctlyspecified. …
A Martingale Difference-Divergence-Based Test For Specification, Liangjun Su, Xin Zheng
A Martingale Difference-Divergence-Based Test For Specification, Liangjun Su, Xin Zheng
Research Collection School Of Economics
In this paper we propose a novel consistent model specification test based on the martingale difference divergence (MDD) of the error term given the covariates. The MDD equals zero if and only if error term is conditionally mean independent of the covariates. Our MDD test does not require any nonparametric estimation under the alternative and it is applicable even if we have many covariates in the regression model. We establish the asymptotic distributions of our test statistic under the null and a sequence of Pitman local alternatives converging to the null at the usual parametric rate. Simulations suggest that our …
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 …
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 …
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 …
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the …
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
Research Collection School Of Economics
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 …
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.
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
Research Collection School Of Economics
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 …
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
Research Collection School Of Economics
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 …
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a consistent nonparametric test for linearity in panel data models with interactive fixed effects. To construct the test statistic, we need to estimate the model under the null hypothesis of linearity and then obtain the restricted residuals. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed both under the null hypothesis and a sequence of Pitman local alternatives. To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap p-values. A small set of Monte Carlo simulations illustrates that our test performs well in …
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
A Bayesian test statistic is proposed to assess the model specification after the model is estimated by Bayesian MCMC methods. The proposed approach does not require an alternative model to be specified and is applicable to a variety of models, including latent variable models, structural dynamic choice models, and dynamics stochastic general equilibrium (DSGE) models, for which frequentist methods are difficult to use. The properties of the test statistic are established and its implementation is discussed. The test is easy to use and the test statistic can be calculated from MCMC outputs even when there are latent variables. The method …
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture the anomaly effects of some asset-specific characteristics and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known "error-in-variable" problem associated with the commonly used …
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 …
Specification Testing For Transformation Models With An Application To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Specification Testing For Transformation Models With An Application To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Research Collection School Of Economics
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 …
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a consistent nonparametric test for linearity in a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives by using the concept of conditional strong mixing that was recently …
Specification Testing For Transformation Models, Arthur Lewbel, Xun Lu, Liangjun Su
Specification Testing For Transformation Models, Arthur Lewbel, Xun Lu, Liangjun Su
Research Collection School Of Economics
Consider a nonseparable model Y=R(X,U) where Y and X are observed, while U is unobserved and conditionally independent of X. This paper provides the first nonparametric test of whether R takes the form of a transformation model, meaning that Y is monotonic in the sum of a function of X plus a function of 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. Monte Carlo experiments show that our test performs well in …
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric component to capture the anomaly effects of some asset-specific characteristics, and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known “error-in-variable” (EIV) problem associated with the commonly used …
Nonparametric Dynamic Panel Data Models: Kernel Estimation And Specification Testing, Liangjun Su, Xun Lu
Nonparametric Dynamic Panel Data Models: Kernel Estimation And Specification Testing, Liangjun Su, Xun Lu
Research Collection School Of Economics
Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced model—the fact that the two additive components have the same functional form, and the unknown function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity in typical dynamic panel data …
Nonparametric Dynamic Panel Data Models With Interactive Fixed Effects: Sieve Estimation And Specification Testing, Liangjun Su, Yonghui Zhang
Nonparametric Dynamic Panel Data Models With Interactive Fixed Effects: Sieve Estimation And Specification Testing, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper we analyze nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension and the time dimension are large. We propose sieve estimation for the nonparametric function by extending Bai’s (2009) principal component analysis (PCA) to our nonparametric framework. Based on the asymptotic expansion of the Gaussian quasi-log-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. …