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- Keyword
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- Semimartingale (3)
- Bayes factor (2)
- Central limit theorem (2)
- Classifier-Lasso (2)
- Explosive GARCH (2)
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- Factor model (2)
- Functional coefficient (2)
- Heterogeneity (2)
- High-frequency data (2)
- Jumps (2)
- Latent structure (2)
- Least squares (2)
- Limiting process (2)
- Localization (2)
- Markov chain Monte Carlo (2)
- Panel data (2)
- Penalized sieve estimation (2)
- Polynomial splines (2)
- Specification test (2)
- Strong consistency (2)
- Time-varying coefficients (2)
- Volatility process (2)
- AIC (1)
- ARFIMA (1)
- Actuarial model (1)
- Agglomeration (1)
- Asymptotic distribution (1)
- Asymptotic distribution. (1)
- Asymptotic normality (1)
- Asymptotics under misspecified trend regression (1)
Articles 1 - 30 of 39
Full-Text Articles in Social and Behavioral Sciences
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Research Collection School Of Economics
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 classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …
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 …
Har Testing For Spurious Regression In Trend, Peter C. B. Phillips, Xiaohu Wang, Yonghui Zhang
Har Testing For Spurious Regression In Trend, Peter C. B. Phillips, Xiaohu Wang, Yonghui Zhang
Research Collection School Of Economics
The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant …
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 …
Quantifying Quality Specialization Across Space: Skills, Sorting, And Agglomeration, Pao-Li Chang, Angdi Lu, Xin Yi
Quantifying Quality Specialization Across Space: Skills, Sorting, And Agglomeration, Pao-Li Chang, Angdi Lu, Xin Yi
Research Collection School Of Economics
We quantify the supply-side determinants of quality specialization across space. Specifically, we complement the quality specialization literature in international trade and study how larger cities specialize in higher-quality goods within a country. In our general equilibrium model, firms in larger cities produce goods with higher quality, because agglomeration benefits accrue more to skilled workers who are also more efficient in upgrading quality. Two channels are at work in our model. The first channel is through the treatment effect of agglomeration, such that firms become more productive if they locate in a larger city. The second channel works through sorting, in …
Non-Separable Models With High-Dimensional Data, Liangjun Su, T Ura, Yc Zhang
Non-Separable Models With High-Dimensional Data, Liangjun Su, T Ura, Yc Zhang
Research Collection School Of Economics
This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. In the first stage we estimate the conditional mean, distribution, and density objects by penalized local least squares, penalized local maximum likelihood estimation, and numerical differentiation, respectively, where control variables are selected via a localized method of L-1-penalization at each value of the continuous treatment. In the second stage we estimate the average and marginal distribution of the potential …
Singapore As A Sustainable City: Past, Present And The Future, Tomoki Fujii, Rohan Ray
Singapore As A Sustainable City: Past, Present And The Future, Tomoki Fujii, Rohan Ray
Research Collection School Of Economics
This paper outlines Singapore’s major sustainability challenges and its policy response in the areas of land use, transportation, waste management, water, and energy. We review the current and past Concept Plans from the perspective of sustainable land use and provide an overview of transportation policy in Singapore. We also examine Singapore’s policies to manage increasing wastes and review the four tap water management plan. Finally, we look at various initiatives by the government for sustainable use of energy. While Singapore has been successful in many ways in transforming itself into one of the most prosperous and sustainable cities in the …
An Interview Question That Brought About Some Thoughts, Zhengxiao Wu
An Interview Question That Brought About Some Thoughts, Zhengxiao Wu
Research Collection School Of Economics
We tackle the question posed by journalist Mehdi Hassan – on how many Chinese lives could be lost or would have to be lost to justify a single percentage of economic growth. We considered a utility maximization problem where the utility function is defined to be the sum of the life expectancy at birth for all Chinese nationals.
Estimation And Inference Of Fractional Continuous-Time Model With Discrete-Sampled Data, Xiaohu Wang, Weilin Xiao, Jun Yu
Estimation And Inference Of Fractional Continuous-Time Model With Discrete-Sampled Data, Xiaohu Wang, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper proposes a two-stage method for estimating parameters in a para-metric fractional continuous-time model based on discrete-sampled observations. In the first stage, the Hurst parameter is estimated based on the ratio of two second-order differences of observations from different time scales. In the second stage, the other parameters are estimated by the method of moments. All estimators have closed-form expressions and are easy to obtain. A large sample theory of the pro-posed estimators is derived under either the in-fill asymptotic scheme or the double asymptotic scheme. Extensive simulations show that the proposed theory performs well in finite samples. Two …
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Anders Eriksson, Daniel P. A. Preve, Jun Yu
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Anders Eriksson, Daniel P. A. Preve, Jun Yu
Research Collection School Of Economics
This paper introduces a parsimonious and yet flexible semiparametric model to forecastfinancial volatility. The new model extends a related linear nonnegative autoregressive modelpreviously used in the volatility literature by way of a power transformation. It is semiparametric inthe sense that the distributional and functional form of its error component is partially unspecified.The statistical properties of the model are discussed and a novel estimation method is proposed.Simulation studies validate the new method and suggest that it works reasonably well in finitesamples. The out-of-sample forecasting performance of the proposed model is evaluated against anumber of standard models, using data on S&P 500 …
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 …
Hypothesis Testing, Specification Testing And Model Selection Based On The Mcmc Output Using R, Yong Li, Jun Yu, Tao Zeng
Hypothesis Testing, Specification Testing And Model Selection Based On The Mcmc Output Using R, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
This chapter overviews several MCMC-based test statistics for hypothesis testing andspecification testing and MCMC-based model selection criteria developed in recentyears. The statistics for hypothesis testing can be viewed as the MCMC version ofthe “trinity” of test statistics based in maximum likelihood (ML), namely, the likelihoodratio (LR) test, the Lagrange multiplier (LM) test, and the Wald test. The model selection criteria correspond to two predictive distributions. One of them can be viewed asthe MCMC version of widely used information criterion, AIC. The asymptotic distributions of the test statistics and model selection criteria are discussed. The test statisticsand model selection criteria are …
A Quantile-Based Asset Pricing Model, Tomohiro Ando, Jushan Bai, Mitohide Nishimura, Jun Yu
A Quantile-Based Asset Pricing Model, Tomohiro Ando, Jushan Bai, Mitohide Nishimura, Jun Yu
Research Collection School Of Economics
It is well-known that the standard estimators of the risk premium in asset pricing models are biased when some price factors are omitted. To address this problem, we propose a novel quantile-based asset pricing model and a new estimation method. Our new asset pricing model allows for the risk premium to be quantile-dependent and our estimation method is applicable to models with unobserved factors. It avoids biased estimation results and always ensures a positive risk premium. The method is applied to the U.S., Japan, and U.K. stock markets. The empirical analysis demonstrates the clear benefits of our approach.
Limit Theory For Moderate Deviation From Integrated Garch Processes, Yubo Tao
Limit Theory For Moderate Deviation From Integrated Garch Processes, Yubo Tao
Research Collection School Of Economics
This paper develops the limit theory of the GARCH(1,1) process that moderately deviates from IGARCH process towards both stationary and explosive regimes. The asymptotic theory extends Berkes et al. (2005) by allowing the parameters to have a slower rate of convergence. The results can be applied to unit root test for processes with mildly-integrated GARCH innovations (e.g. Boswijk (2001), Cavaliere and Taylor (2007, 2009)) and deriving limit theory of estimators for models involving mildly-integrated GARCH processes (e.g. Jensen and Rahbek (2004), Francq and Zakoïan (2012, 2013).
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Research Collection School Of Economics
The power-posterior method of Friel and Pettitt (2008) has been used to estimate the marginal likelihoods of competing Bayesian models. In this paper it is shown that the Bernstein-von Mises (BvM) theorem holds for the power posteriors under regularity conditions. Due to the BvM theorem, the power posteriors, when adjusted by the square root of the corresponding grid points, converge to the same normal distribution as the original posterior distribution, facilitating the implementation of importance sampling for the purpose of estimating the marginal likelihood. Unlike the power-posterior method that requires repeated posterior sampling from the power posteriors, the new method …
Panel Threshold Regressions With Latent Group Structures, Ke Miao, Liangjun Su, Wendun Wang
Panel Threshold Regressions With Latent Group Structures, Ke Miao, Liangjun Su, Wendun Wang
Research Collection School Of Economics
In this paper, we consider the least squares estimation of a panel structure threshold re-gression (PSTR) model where both the slope coefficients and threshold parameters may exhibit latent group structures. We study the asymptotic properties of the estimators of the latent group structure and the slope and threshold coefficients. We show that we can estimate the latent group structure correctly with probability approaching 1 and the estimators of the slope and threshold coefficients are asymptotically equivalent to the infeasible estimators that are obtained as if the true group structures were known. We study likelihood-ratio-based inferences on the group-specific threshold parameters …
A Smoothed Q-Learning Algorithm For Estimating Optimal Dynamic Treatment Regime, Yanqin Fan, Ming He, Liangjun Su, Xiao-Hua Zhou
A Smoothed Q-Learning Algorithm For Estimating Optimal Dynamic Treatment Regime, Yanqin Fan, Ming He, Liangjun Su, Xiao-Hua Zhou
Research Collection School Of Economics
In this paper we propose a smoothed Q-learning algorithm for estimating optimal dynamic treatment regimes. In contrast to the Q-learning algorithm in which non-regular inference is involved, we show that under assumptions adopted in this paper, the proposed smoothed Q-learning estimator is asymptotically normally distributed even when the Q-learning estimator is not and its asymptotic variance can be consistently estimated. As a result, inference based on the smoothed Q-learning estimator is standard. We derive the optimal smoothing parameter and propose a data-driven method for estimating it. The finite sample properties of the smoothed Q-learning estimator are studied and compared with …
An Improved Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
An Improved Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
Research Collection School Of Economics
A new posterior odds analysis is developed to test for a unit root in volatilitydynamics in the context of stochastic volatility models. Our analysis extendsthe Bayesian unit root test of So and Li (1999) in two important ways. First,a mixed informative prior distribution with a random weight is introducedfor the Bayesian unit root testing in volatility. Second, a numerically morestable algorithm is introduced to compute Bayes factor, taking into accountthe special structure of the competing models. It can be shown that theapproach introduced overcomes the problem of the diverging “size” in themarginal likelihood approach by So and Li (1999) and …
Editorial Introduction To The Special Issue Entitled: Spatial Econometrics: New Methods And Applications, Zhenlin Yang
Editorial Introduction To The Special Issue Entitled: Spatial Econometrics: New Methods And Applications, Zhenlin Yang
Research Collection School Of Economics
Spatial econometrics is a fast growing research area, with the theories and methods developed over the past five decades or so being applied more and more widely, not only in the specialized fields such as regional science, real estate, economic geography, and urban economics, but also increasingly in the general fields such as economics, finance and social networks, in particular in the recent decade. One of the driving forces is perhaps the formation of the Spatial Econometrics Association (SEA) at Rome in 2006 and its subsequent annul conference started in 2007 at the Cambridge University, which have helped greatly the …
The Revealed Preference Theory Of Stable Matchings With One-Sided Preferences, Gaoji Hu, Jiangtao Li, Rui Tang
The Revealed Preference Theory Of Stable Matchings With One-Sided Preferences, Gaoji Hu, Jiangtao Li, Rui Tang
Research Collection School Of Economics
This note studies the testable implications of the theory of stable matchings intwo-sided matching markets with one-sided preferences. Our main result connects therevealed preference analysis to the well-known lattice structure of the set of stablematchings, and tests the rationalizability of a data set by analyzing the joins andmeets of matchings.
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 …
M-Estimators Of U-Processes With A Change-Point Due To A Covariate Threshold, Lili Tan, Yichong Zhang
M-Estimators Of U-Processes With A Change-Point Due To A Covariate Threshold, Lili Tan, Yichong Zhang
Research Collection School Of Economics
Economic theory often predicts a “tipping point” effect due to multiple equilibria. Linear threshold regressions estimate the “tipping point” by assuming at the same time that the response variable is linear in an index of covariates. However, economic theory rarely imposes a specific functional form, but rather predicts a monotonic relationship between the response variable and the index. We propose new, rank-based, estimators for both the “tipping point” and other regression coefficients, exploiting only the monotonicity condition. We derive the asymptotic properties of these estimators by establishing a more general result for M-estimators of U-processes with a change-point due to …
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 …
Asymptotic Theory For Rough Fractional Vasicek Models, Weilin Xiao, Jun Yu
Asymptotic Theory For Rough Fractional Vasicek Models, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper extends the asymptotic theory for the fractional Vasicek model developed in Xiao and Yu (2018) from the case where H ∈ (1/2, 1) to the case where H ∈ (0, 1/2). It is found that the asymptotic theory of the persistence parameter (k) critically depends on the sign of k. Moreover, if k > 0, the asymptotic distribution for the estimator of k is different when H ∈ (0, 1/2) from that when H ∈ (1/2, 1).
Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang
Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang
Research Collection School Of Economics
This paper develops methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. We consider the case where the number of cross-sectional units (N) is large and the number of time series periods (T).as well as the number of unobserved common factors (R) are fixed. Under some normalization rules, wecan concentrateout thelarge dimen-sional parameter vector of factor loadings and specify a set of conditional moment restriction that are involved with only the finite dimensional factor parameters along with the infinite dimensional nonpara-metric component. For a conjectured restriction on the parameter, we consider …
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin
Research Collection School Of Economics
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 classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …
Weak Σ-Convergence: Theory And Applications, Jianning Kong, Peter C. B. Phillips, Donggyu Sul
Weak Σ-Convergence: Theory And Applications, Jianning Kong, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
The concept of relative convergence, which requires the ratio of two time series to converge to unity in the long run, explains convergent behavior when series share commonly divergent stochastic or deterministic trend components. Relative convergence of this type does not necessarily hold when series share common time decay patterns measured by evaporating rather than divergent trend behavior. To capture convergent behavior in panel data that do not involve stochastic or divergent deterministic trends, we introduce the notion of weak σ-convergence, whereby cross section variation in the panel decreases over time. The paper formalizes this concept and proposes a simple-to-implement …
Random Coefficient Continuous Systems: Testing For Extreme Sample Path Behavior, Yubo Tao, Peter C. B. Phillips, Jun Yu
Random Coefficient Continuous Systems: Testing For Extreme Sample Path Behavior, Yubo Tao, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behavior according to specific regions of the parameter space that open up the potential for testing these forms of extreme behavior. A two-stage approach that employs realized volatility is proposed for the continuous system estimation, asymptotic theory is developed, and test statistics to identify the different forms of extreme sample path behavior are proposed. Simulations show that the …
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Research Collection School Of Economics
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of increments at the identified jump events. The test for rank r is based on the asymptotic behavior of the …
Limit Theory For Moderate Deviations From Integrated Garch Processes, Yubo Tao
Limit Theory For Moderate Deviations From Integrated Garch Processes, Yubo Tao
Research Collection School Of Economics
This paper develops the limit theory of the GARCH(1,1) process that moderately deviates from IGARCH process towards both stationary and explosive regimes. The asymptotic theory extends Berkes et al. (2005) by allowing the parameters to have a slower rate of convergence. The results can be applied to unit root test for processes with mildly-integrated GARCH innovations (e.g. Boswijk (2001), Cavaliere and Taylor (2007, 2009)) and deriving limit theory of estimators for models involving mildly-integrated GARCH processes (e.g. Jensen and Rahbek (2004), Francq and Zakoïan (2012, 2013).