Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Fixed effects (4)
- Martingale difference (3)
- Asymptotic distribution (2)
- Dynamic OLS (2)
- Factor model (2)
-
- Fractional Brownian motion (2)
- Heterogeneity (2)
- High-frequency data (2)
- Latent group structure (2)
- Least squares (2)
- Local power (2)
- Local to unity (2)
- Minimum wage (2)
- Non-normality (2)
- Nonstationarity (2)
- Panel cointegration (2)
- Penalized least squares (2)
- Redundancy insurance (2)
- Spatial effects (2)
- Specification test (2)
- Time-varying coefficient (2)
- Uniform inference (2)
- Unknown heteroskedasticity (2)
- AIC (1)
- AQS estimator (1)
- Adjusted quasi score (1)
- Adjusted quasi-scores (1)
- Approximation error (1)
- Asymmetry (1)
- Asymptotic (1)
Articles 1 - 30 of 39
Full-Text Articles in Econometrics
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Research Collection School Of Economics
We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …
Quasi-Bayesian Inference For Production Frontiers, Xiaobin Liu, Thomas Tao Yang, Yichong Zhang
Quasi-Bayesian Inference For Production Frontiers, Xiaobin Liu, Thomas Tao Yang, Yichong Zhang
Research Collection School Of Economics
We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian method to produce the point estimate and confidence interval for the production frontier. We show the asymptotic properties of the proposed estimator and the validity of the inference procedure. The finite sample performance of our method is illustrated through simulations and an empirical application.
Point Optimal Testing With Roots That Are Functionally Local To Unity, Anna Bykhovskaya, Peter C. B. Phillips
Point Optimal Testing With Roots That Are Functionally Local To Unity, Anna Bykhovskaya, Peter C. B. Phillips
Research Collection School Of Economics
Limit theory for regressions involving local to unit roots (LURs) is now used extensively in time series econometric work, establishing power properties for unit root and cointegration tests, assisting the construction of uniform confidence intervals for autoregressive coefficients, and enabling the development of methods robust to departures from unit roots. The present paper shows how to generalize LUR asymptotics to cases where the localized departure from unity is a time varying function rather than a constant. Such a functional local unit root (FLUR) model has much greater generality and encompasses many cases of additional interest that appear in practical work, …
Causal Change Detection In Possibly Integrated Systems: Revisiting The Money-Income Relationship, Shuping Shi, Stan Hurn, Peter C. B. Phillips
Causal Change Detection In Possibly Integrated Systems: Revisiting The Money-Income Relationship, Shuping Shi, Stan Hurn, Peter C. B. Phillips
Research Collection School Of Economics
This paper re-examines changes in the causal link between money and income in the United States over the past half century (1959-2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a rolling window algorithm, and a recursive evolving algorithm all of which utilize subsample tests of Granger causality within a lagaugmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. Bootstrap methods are developed to control family-wise size in the implementation …
Asymptotic Properties Of Least Squares Estimator In Local To Unity Processes With Fractional Gaussian Noises, Xiaohu Wang, Weilin Xiao, Jun Yu
Asymptotic Properties Of Least Squares Estimator In Local To Unity Processes With Fractional Gaussian Noises, Xiaohu Wang, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper derives asymptotic properties of the least squares estimator of the autoregressive parameter in local to unity processes with errors being fractional Gaussian noises with the Hurst parameter H. It is shown that the estimator is consistent when H ∈ (0, 1). Moreover, the rate of convergence is n when H ∈ [0.5, 1). The rate of convergence is n2H when H ∈ (0, 0.5). Furthermore, the limit distribution of the centered least squares estimator depends on H. When H = 0.5, the limit distribution is the same as that obtained in Phillips (1987a) for the local to …
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi K. Baltagi, Alain Pirotte, Zhenlin Yang
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi K. Baltagi, Alain Pirotte, Zhenlin Yang
Research Collection School Of Economics
We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS-OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS-OPMD method provides feasible solutions. The AQS tests are formally derived and …
Persistent And Rough Volatility, Xiaobin Liu, Shuping Shi, Jun Yu
Persistent And Rough Volatility, Xiaobin Liu, Shuping Shi, Jun Yu
Research Collection School Of Economics
This paper contributes to an ongoing debate on volatility dynamics. We introduce a discrete-time fractional stochastic volatility (FSV) model based on the fractional Gaussian noise. The new model has the same limit as the fractional integrated stochastic volatility (FISV) model under the in-fill asymptotic scheme. We study the theoretical properties of both models and introduce a memory signature plot for a model-free initial assessment. A simulated maximum likelihood (SML) method, which maximizes the time-domain log-likelihoods obtained by the importance sampling technique, is employed to estimate the model parameters. Simulation studies suggest that the SML method can accurately estimate both models. …
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 …
Unconditional Quantile Regression With High-Dimensional Data, Yuya Sasaki, Takuya Ura, Yichong Zhang
Unconditional Quantile Regression With High-Dimensional Data, Yuya Sasaki, Takuya Ura, Yichong Zhang
Research Collection School Of Economics
Credible counterfactual analysis requires high-dimensional controls. This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel doubly robust score for double/debiased estimation and inference for the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference for the Lasso double/debiased estimator, and develop asymptotic theories to guarantee that the bootstrap works. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that i) marginal effects of counterfactually extending the duration of the exposure to the …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum …
Universal Minimum Wage Is Not Suitable For Singapore, Zhengxiao Wu
Universal Minimum Wage Is Not Suitable For Singapore, Zhengxiao Wu
Research Collection School Of Economics
In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao examined the concept of a universal minimum wage, and discussed how it is not suitable for Singapore.
Maximum Likelihood Estimation For The Fractional Vasicek Model, Katsuto Tanaka, Weilin Xiao, Jun Yu
Maximum Likelihood Estimation For The Fractional Vasicek Model, Katsuto Tanaka, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper estimates the drift parameters in the fractional Vasicek model from a continuous record of observations via maximum likelihood (ML). The asymptotic theory for the ML estimates (MLE) is established in the stationary case, the explosive case, and the boundary case for the entire range of the Hurst parameter, providing a complete treatment of asymptotic analysis. It is shown that changing the sign of the persistence parameter changes the asymptotic theory for the MLE, including the rate of convergence and the limiting distribution. It is also found that the asymptotic theory depends on the value of the Hurst parameter.
Estimation Of Conditional Average Treatment Effects With High-Dimensional Data, Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang
Estimation Of Conditional Average Treatment Effects With High-Dimensional Data, Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang
Research Collection School Of Economics
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. This is a key feature since identification is generally more credible if the full vector of conditioning variables, including possible transformations, is high-dimensional. The second stage consists of a low-dimensional kernel regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator …
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.
Activation Of Trpa1 Nociceptor Promotes Systemic Adult Mammalian Skin Regeneration, Jenny J. Wei, Hali S. Kim, Casey A. Spencer, Donna Brennan-Crispi, Ying Zheng, Nicolette M. Johnson, Misha Rosenbach, Christopher Miller, Denis H. Y. Leung, George Cotsarelis, Thomas H. Leung
Activation Of Trpa1 Nociceptor Promotes Systemic Adult Mammalian Skin Regeneration, Jenny J. Wei, Hali S. Kim, Casey A. Spencer, Donna Brennan-Crispi, Ying Zheng, Nicolette M. Johnson, Misha Rosenbach, Christopher Miller, Denis H. Y. Leung, George Cotsarelis, Thomas H. Leung
Research Collection School Of Economics
Adult mammalian wounds, with rare exception, heal with fibrotic scars that severely disrupt tissue architecture and function. Regenerative medicine seeks methods to avoid scar formation and restore the original tissue structures. We show in three adult mouse models that pharmacologic activation of the nociceptor TRPA1 on cutaneous sensory neurons reduces scar formation and can also promote tissue regeneration. Local activation of TRPA1 induces tissue regeneration on distant untreated areas of injury, demonstrating a systemic effect. Activated TRPA1 stimulates local production of interleukin-23 (IL-23) by dermal dendritic cells, leading to activation of circulating dermal IL-17–producing γδ T cells. Genetic ablation of …
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Research Collection School Of Economics
In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard …
Redundancy Insurance Is Not Unemployment Insurance, Zhengxiao Wu
Redundancy Insurance Is Not Unemployment Insurance, Zhengxiao Wu
Research Collection School Of Economics
In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao discussed the difference between redundancy insurance and unemployment insurance. He shared Japan's example, where the cost for unemployment insurance is higher than redundancy insurance, and added that being an unprecedented policy, more care should be taken when implementing it.
In-Fill Asymptotic Theory For Structural Break Point In Autoregressions, Liang Jiang, Xiaohu Wang, Jun Yu
In-Fill Asymptotic Theory For Structural Break Point In Autoregressions, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
This article obtains the exact distribution of the maximum likelihood estimator of structural break point in the Ornstein-Uhlenbeck process when a continuous record is available. The exact distribution is asymmetric, tri-modal, dependent on the initial condition. These three properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in autoregressive (AR) models. Motivated by these observations, the article then develops an in-fill asymptotic theory for the LS estimator of structural break point in the AR(1) coefficient. The in-fill asymptotic distribution is also asymmetric, tri-modal, dependent on the initial condition, and delivers …
Forecasting Singapore Gdp Using The Spf Data, Tian Xie, Jun Yu
Forecasting Singapore Gdp Using The Spf Data, Tian Xie, Jun Yu
Research Collection School Of Economics
In this article, we use econometric methods, machine learning methods, and a hybrid method to forecast the GDP growth rate in Singapore based on the Survey of Professional Forecasters (SPF). We compare the performance of these methods with the sample median used by the Monetary Authority of Singapore (MAS). It is shown that the relationship between the actual GDP growth rates and the forecasts from individual professionals is highly nonlinear and non-additive, making it hard for all linear methods and the sample median to perform well. It is found that the hybrid method performs the best, reducing the mean squared …
Two Suggestions To Wp Mp Jamus Lim, Zhengxiao Wu
Two Suggestions To Wp Mp Jamus Lim, Zhengxiao Wu
Research Collection School Of Economics
In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao discussed the arguments presented by Workers’ Party’s candidate Associate Professor Jamus Lim during the live broadcast of a political debate, and put forth two suggestions for Assoc Prof Lim.
Realized Semicovariances, Tim Bollerslev, Jia Li, Andrew J. Patton, Rogier Quaedvlieg
Realized Semicovariances, Tim Bollerslev, Jia Li, Andrew J. Patton, Rogier Quaedvlieg
Research Collection School Of Economics
We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high-frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first-order asymptotic results highlight how the same-sign and mixed-sign components load differently on economic information related to stochastic correlation and jumps. The second-order asymptotic results reveal the structure underlying the same-sign semicovariances, as manifested in the form of co-drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross-section of individual stocks to empirically …
Deviance Information Criterion For Latent Variable Models And Misspecified Models, Yong Li, Jun Yu, Tao Zeng
Deviance Information Criterion For Latent Variable Models And Misspecified Models, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
Deviance information criterion (DIC) has been widely used for Bayesian model comparison, especially after Markov chain Monte Carlo (MCMC) is used to estimate candidate models. This paper first studies the problem of using DIC to compare latent variable models when DIC is calculated from the conditional likelihood. In particular, it is shown that the conditional likelihood approach undermines theoretical underpinnings of DIC. A new version of DIC, namely DICL, is proposed to compare latent variable models. The large sample properties of DICL are studied. A frequentist justification of DICL is provided. Like AIC, DICL provides an asymptotically unbiased estimator to …
Estimating Selection Models Without Instrument With Stata, Xavier D’Haultfœuille, Arnaud Maurel, Xiaoyun Qiu, Yichong Zhang
Estimating Selection Models Without Instrument With Stata, Xavier D’Haultfœuille, Arnaud Maurel, Xiaoyun Qiu, Yichong Zhang
Research Collection School Of Economics
This article presents the eqregsel command for implementing the estimationand bootstrap inference of sample selection models via extremal quantile regression. The command estimates a semiparametric sample selection model withoutinstrument or large support regressor, and outputs the point estimates of the ho-mogenous linear coefficients, their bootstrap standard errors, as well as the p-valuefor a specification test.
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Research Collection School Of Economics
We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215-2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals' group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of …
Identifying Latent Grouped Patterns In Conintegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Identifying Latent Grouped Patterns In Conintegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Research Collection School Of Economics
We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215-2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals' group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of …
Econometric Methods And Data Science Techniques: A Review Of Two Strands Of Literature And An Introduction To Hybrid Methods, Tian Xie, Jun Yu, Tao Zeng
Econometric Methods And Data Science Techniques: A Review Of Two Strands Of Literature And An Introduction To Hybrid Methods, Tian Xie, Jun Yu, Tao Zeng
Research Collection School Of Economics
The data market has been growing at an exceptional pace. Consequently, more sophisticated strategies to conduct economic forecasts have been introduced with machine learning techniques. Does machine learning pose a threat to conventional econometric methods in terms of forecasting? Moreover, does machine learning present great opportunities to cross-fertilize the field of econometric forecasting? In this report, we develop a pedagogical framework that identifies complementarity and bridges between the two strands of literature. Existing econometric methods and machine learning techniques for economic forecasting are reviewed and compared. The advantages and disadvantages of these two classes of methods are discussed. A class …
Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu
Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu
Research Collection School Of Economics
This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and …
Detecting Latent Communities In Network Formation Models, Shujie Ma, Liangjun Su, Yichong Zhang
Detecting Latent Communities In Network Formation Models, Shujie Ma, Liangjun Su, Yichong Zhang
Research Collection School Of Economics
This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects. We model the coefficients of observed characteristics to have a latent community structure and the edge-wise fixed effects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number …
Asymptotic Theory For Near Integrated Processes Driven By Tempered Linear Processes, Farzad Sabzikar, Qiying Wang, Peter C. B. Phillips
Asymptotic Theory For Near Integrated Processes Driven By Tempered Linear Processes, Farzad Sabzikar, Qiying Wang, Peter C. B. Phillips
Research Collection School Of Economics
In an early article on near-unit root autoregression, Ahtola and Tiao (1984) studied the behavior of the score function in a stationary first order autoregression driven by independent Gaussian innovations as the autoregressive coefficient approached unity from below. The present paper develops asymptotic theory for near-integrated random processes and associated regressions including the score function in more general settings where the errors are tempered linear processes. Tempered processes are stationary time series that have a semi-long memory property in the sense that the autocovariogram of the process resembles that of a long memory model for moderate lags but eventually diminishes …
Robust Estimation And Inference Of Spatial Panel Data Models With Fixed Effects, Shew Fan Liu, Zhenlin Yang
Robust Estimation And Inference Of Spatial Panel Data Models With Fixed Effects, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
It is well established that the quasi maximum likelihood (QML) estimation of the spatial regression models is generally inconsistent under unknown cross-sectional heteroskedasticity (CH) and the CH-robust methods have been developed. The same issue remains for the spatial panel data (SPD) models but the similar studies based on QML approach do not seem to have been carried out. This paper focuses on the SPD model with fixed effects (FE). We argue that under unknown CH the QML estimator for the SPD-FE model is inconsistent in general, but there are ‘special cases’ where it may remain consistent although the exact conditions …