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Articles 151 - 180 of 771
Full-Text Articles in Econometrics
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 …
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 …
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.
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.
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression, Degui Li, Peter C. B. Phillips, Jiti Gao
Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression, Degui Li, Peter C. B. Phillips, Jiti Gao
Research Collection School Of Economics
This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of the data, we allow the regressors to be cointegrated and to embody a mixture of stochastic and deterministic trends, complications which result in asymptotic degeneracy of the kernel-weighted signal matrix. To address these complications new local and global rotation techniques are introduced to transform the covariate space to accommodate multiple scenarios of induced degeneracy. Under regularity conditions we derive asymptotic results that differ …
Estimation Of Fixed Effects Spatial Dynamic Panel Data Models With Small T And Unknown Heteroskedasticity, Liyao Li, Zhenlin Yang
Estimation Of Fixed Effects Spatial Dynamic Panel Data Models With Small T And Unknown Heteroskedasticity, Liyao Li, Zhenlin Yang
Research Collection School Of Economics
We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtained by adjusting the conditional quasi-score functions given the initial observations, leading to M-estimators that are free from the initial conditions and robust against unknown cross-sectional heteroskedasticity. Consistency and asymptotic normality of the proposed M-estimator are established. The standard errors are obtained by representing the estimating equations as sums of martingale differences. Monte Carlo results show that the proposed …
Specification Tests For Temporal Heterogeneity In Spatial Panel Data Models With Fixed Effects, Yuhong Xu, Zhenlin Yang
Specification Tests For Temporal Heterogeneity In Spatial Panel Data Models With Fixed Effects, Yuhong Xu, Zhenlin Yang
Research Collection School Of Economics
We propose adjusted quasi score (AQS) tests for testing the existence of temporal heterogeneity in slope and spatial parameters in spatial panel data (SPD) models, allowing for the presence of individual-specific and/or time-specific fixed effects (or in general intercept heterogeneity). The SPD model with spatial lag is treated in detail by first considering the model with individual fixed effects only, and then extending it to the model with both individual and time fixed effects. Two types of AQS tests (naïve and robust) are proposed, and their asymptotic properties are presented. These tests are then fully extended to SPD models with …
Hybrid Stochastic Local Unit Roots, Offer Lieberman, Peter C. B. Phillips
Hybrid Stochastic Local Unit Roots, Offer Lieberman, Peter C. B. Phillips
Research Collection School Of Economics
Two approaches have dominated formulations designed to capture small departures from unit root autoregressions. The first involves deterministic departures that include local-to-unity (LUR) and mildly (or moderately) integrated (MI) specifications where departures shrink to zero as the sample size n -> infinity. The second approach allows for stochastic departures from unity, leading to stochastic unit root (STUR) specifications. This paper introduces a hybrid local stochastic unit root (LSTUR) specification that has both LUR and STUR components and allows for endogeneity in the time varying coefficient that introduces structural elements to the autoregression. This hybrid model generates trajectories that, upon normalization, …
Corrigendum To "On Time-Varying Factor Models: Estimation And Testing" [J. Econometrics 198 (2017) 84-101], Liangjun Su, Xia Wang
Corrigendum To "On Time-Varying Factor Models: Estimation And Testing" [J. Econometrics 198 (2017) 84-101], Liangjun Su, Xia Wang
Research Collection School Of Economics
We note that Su and Wang (2017, On Time-varying Factor Models: Estimation and Testing, Journal of Econometrics 198, 84-101) ignore the bias terms when estimating the time-varying factor models. In this note, we correct the theoretical results on the estimation of time-varying factor models. The asymptotic results for testing the correct specification of time invariant factor loadings are not affected.
Local Powers Of Least-Squares-Based Test For Panel Fractional Ornstein-Uhlenbeck Process, Katsuto Tanaka, Weilin Xiao, Jun Yu
Local Powers Of Least-Squares-Based Test For Panel Fractional Ornstein-Uhlenbeck Process, Katsuto Tanaka, Weilin Xiao, Jun Yu
Research Collection School Of Economics
Based on the least squares estimator, this paper proposes a novel method to test the sign of the persistence parameter in a panel fractional Ornstein-Uhlenbeck process with a known Hurst parameter H. Depending on H ∈ (1/2, 1), H = 1/2, or H ∈ (0, 1/2), three test statistics are considered. In the null hypothesis the persistence parameter is zero. Based on a panel of continuous record of observations, the null asymptotic distributions are obtained when T is fixed and N is assumed to go to infinity, where T is the time span of the sample and N is the …
Testing Alphas In Conditional Time-Varying Factor Models With High Dimensional Assets, Shujie Ma, Wei Lan, Liangjun Su, Chih-Ling Tsai
Testing Alphas In Conditional Time-Varying Factor Models With High Dimensional Assets, Shujie Ma, Wei Lan, Liangjun Su, Chih-Ling Tsai
Research Collection School Of Economics
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 to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model (Fama and French, 1993) is better than CAPM (Sharpe, 1964) …
Strong Consistency Of Spectral Clustering For Stochastic Block Models, Liangjun Su, Wuyi Wang, Yichong Zhang
Strong Consistency Of Spectral Clustering For Stochastic Block Models, Liangjun Su, Wuyi Wang, Yichong Zhang
Research Collection School Of Economics
In this paper we prove the strong consistency of several methods based on the spectral clustering techniques that are widely used to study the community detection problem in stochastic block models (SBMs). We show that under some weak conditions on the minimal degree, the number of communities, and the eigenvalues of the probability block matrix, the K-means algorithm applied to the eigenvectors of the graph Laplacian associated with its first few largest eigenvalues can classify all individuals into the true community uniformly correctly almost surely. Extensions to both regularized spectral clustering and degree-corrected SBMs are also considered. We illustrate the …
Model Selection For Explosive Models, Yubo Tao, Jun Yu
Model Selection For Explosive Models, Yubo Tao, Jun Yu
Research Collection School Of Economics
This chapter examines the limit properties of information criteria (such as AIC, BIC, and HQIC) for distinguishing between the unit-root (UR) model and the various kinds of explosive models. The explosive models include the local-to-unit-root model from the explosive side the mildly explosive (ME) model, and the regular explosive model. Initial conditions with different orders of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the UR model when data come from the UR model. When data come from the local-to-unit-root model from …
Uniform Inference In Panel Autoregression, John C. Chao, Peter C. B. Phillips
Uniform Inference In Panel Autoregression, John C. Chao, Peter C. B. Phillips
Research Collection School Of Economics
This paper considers estimation and inference concerning the autoregressive coefficient (rho) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for rho that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson-Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when rho is near unity. But it is not so well known that …
Detecting Financial Collapse And Ballooning Sovereign Risk, Peter C. B. Phillips, Sp Shi
Detecting Financial Collapse And Ballooning Sovereign Risk, Peter C. B. Phillips, Sp Shi
Research Collection School Of Economics
This paper proposes a new model for capturing discontinuities in the underlying financial environment that can lead to abrupt falls, but not necessarily sustained monotonic falls, in asset prices. This notion of price dynamics is consistent with existing understanding of market crashes, which allows for a mix of market responses that are not universally negative. The model may be interpreted as a martingale composed with a randomized drift process that is designed to capture various asymmetric drivers of market sentiment. In particular, the model is capable of generating realistic patterns of price meltdowns and bond yield inflations that constitute major …