Liangjun Su
Biographic Data
| ID | 8880929 |
|---|---|
| NAME | Liangjun Su |
| GIVEN NAMES | Liangjun |
| FAMILY NAME | Su |
| SIGNATURE | SU L |
| AFFILIATIONS | Singapore Management University |
| ORCID | 0000-0002-0565-6928 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 20 |
| WORKS AS EDITOR | 1 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
The Econometric Theory Awards 2026
High Dimensional Discrete Choice Models With Interactive Fixed Effects Applied to Causal Inference
We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illu…
CCE Estimation of Heterogeneous Panel Quantile Regression Models with Relatively Small T
We consider a common correlated effects estimator for panel quantile regression models featuring heterogeneous slope coefficients and interactive fixed effects. Using convolution smoothing to facilitate the derivation of asymptotic properties, our method allows the number of time periods (T) to grow at a much slower rate than the number of cross-sectional units (N). We establish that the mean group estimator converges to a limiting normal distrib…
Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity
This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underl…
A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network
In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smooth…
Distinguishing Time-Varying Factor Models
Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that …
L 2 -Relaxation
We propose l2-relaxation, which is a novel convex optimization problem, to tackle a forecast combination with many forecasts or a minimum variance portfolio with many assets. l2-relaxation minimizes the squared Euclidean norm of the weight vector subject to a set of relaxed linear inequalities to balance the bias and variance. It delivers optimality with approximately equal within-group weights when latent block equicorrelation patterns dominate …
Oracle Efficient Estimation of Heterogeneous Dynamic Panel Data Models with Interactive Fixed Effects
We propose a two-step procedure to estimate a heterogeneous dynamic panel data model with interactive fixed effects. We establish the asymptotic properties of the estimators and show that the final estimator is oracle efficient. We also propose a specification test for the null hypothesis of homogeneous slopes and study the asymptotic properties of the test statistic under both local and global alternatives. Simulations demonstrate the fine perfo…
Estimation and Inference on Time-Varying Favar Models
We introduce a time-varying (TV) factor-augmented vector autoregressive (FAVAR) model to capture the TV behavior in the factor loadings and the VAR coefficients. To consistently estimate the TV parameters, we first obtain the unobserved common factors via the local principal component analysis (PCA) and then estimate the TV-FAVAR model via a local smoothing approach. The limiting distribution of the proposed estimators is established. To gauge po…
Detecting Unobserved Heterogeneity in Efficient Prices via Classifier-Lasso
This article proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-La…
Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets
For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented…
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures
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 classif…
Specification Test for Spatial Autoregressive Models
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 sa…
A Combined Approach to the Inference of Conditional Factor Models
This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature—the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in t…
The Oxford Handbook Of Applied Nonparametric And Semiparametric Econometrics And Statistics
Additive Nonparametric Regression in the Presence of Endogenous Regressors
In this article we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient, and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and…
Nonparametric Testing for Asymmetric Information
Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of inte…
Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling
We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also …
Estimation and Forecasting of Dynamic Conditional Covariance
We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct specification of PCC models, and study its asymptotic properties. We evaluate the finite sample performance of our test …
Semiparametric Estimator of Time Series Conditional Variance
We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecif…
Testing Conditional Uncorrelatedness
We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well …
No prominent works on this page.
Testing Conditional Uncorrelatedness
We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well …
Semiparametric Estimator of Time Series Conditional Variance
We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecif…
Estimation and Forecasting of Dynamic Conditional Covariance
We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct specification of PCC models, and study its asymptotic properties. We evaluate the finite sample performance of our test …
Nonparametric Testing for Asymmetric Information
Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of inte…
Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling
We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also …
The Oxford Handbook Of Applied Nonparametric And Semiparametric Econometrics And Statistics
Additive Nonparametric Regression in the Presence of Endogenous Regressors
In this article we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient, and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and…
A Combined Approach to the Inference of Conditional Factor Models
This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature—the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in t…
Specification Test for Spatial Autoregressive Models
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 sa…
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures
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 classif…
Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets
For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented…
Detecting Unobserved Heterogeneity in Efficient Prices via Classifier-Lasso
This article proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-La…
Oracle Efficient Estimation of Heterogeneous Dynamic Panel Data Models with Interactive Fixed Effects
We propose a two-step procedure to estimate a heterogeneous dynamic panel data model with interactive fixed effects. We establish the asymptotic properties of the estimators and show that the final estimator is oracle efficient. We also propose a specification test for the null hypothesis of homogeneous slopes and study the asymptotic properties of the test statistic under both local and global alternatives. Simulations demonstrate the fine perfo…
Estimation and Inference on Time-Varying Favar Models
We introduce a time-varying (TV) factor-augmented vector autoregressive (FAVAR) model to capture the TV behavior in the factor loadings and the VAR coefficients. To consistently estimate the TV parameters, we first obtain the unobserved common factors via the local principal component analysis (PCA) and then estimate the TV-FAVAR model via a local smoothing approach. The limiting distribution of the proposed estimators is established. To gauge po…
Distinguishing Time-Varying Factor Models
Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that …
L 2 -Relaxation
We propose l2-relaxation, which is a novel convex optimization problem, to tackle a forecast combination with many forecasts or a minimum variance portfolio with many assets. l2-relaxation minimizes the squared Euclidean norm of the weight vector subject to a set of relaxed linear inequalities to balance the bias and variance. It delivers optimality with approximately equal within-group weights when latent block equicorrelation patterns dominate …
The Econometric Theory Awards 2026
High Dimensional Discrete Choice Models With Interactive Fixed Effects Applied to Causal Inference
We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illu…
CCE Estimation of Heterogeneous Panel Quantile Regression Models with Relatively Small T
We consider a common correlated effects estimator for panel quantile regression models featuring heterogeneous slope coefficients and interactive fixed effects. Using convolution smoothing to facilitate the derivation of asymptotic properties, our method allows the number of time periods (T) to grow at a much slower rate than the number of cross-sectional units (N). We establish that the mean group estimator converges to a limiting normal distrib…
Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity
This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underl…
A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network
In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smooth…
Econometrics (18 works) · Mathematics (16 works) · Statistics (16 works) · Computer Science (12 works) · Monetary Policy and Economic Impact (10 works) · Economics (9 works) · Statistical Methods and Inference (9 works) · Estimator (8 works) · Nonparametric statistics (8 works) · Financial Risk and Volatility Modeling (7 works)