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Articles 1 - 30 of 40
Full-Text Articles in Economics
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Research Collection School Of Economics
This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized regression techniques. We focus on linear models where the slope parameters are heterogeneous across groups but homogenous within a group and the group membership is unknown. Two approaches are considered -- penalized least squares (PLS) for models without endogenous regressors, and penalized GMM (PGMM) for models with endogeneity. In both cases we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single …
A State Space Model Approach To Integrated Covariance Matrix Estimation With High Frequency Data, Cheng Liu, Cheng Yong Tang
A State Space Model Approach To Integrated Covariance Matrix Estimation With High Frequency Data, Cheng Liu, Cheng Yong Tang
Research Collection Lee Kong Chian School Of Business
We consider a state space model approach forhigh frequency financial data analysis. An expectationmaximization(EM) algorithm is developed for estimatingthe integrated covariance matrix of the assets. The statespace model with the EM algorithm can handle noisy financialdata with correlated microstructure noises. Difficultydue to asynchronous and irregularly spaced trading data ofmultiple assets can be naturally overcome by consideringthe problem in a scenario with missing data. Since the statespace model approach requires no data synchronization, norecord in the financial data is deleted so that it efficientlyincorporates information from all observations. Empiricaldata analysis supports the general specification of the statespace model, and simulations confirm …
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Qihui Chen
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Qihui Chen
Research Collection School Of Economics
This paper proposes a residual-based LM test for slope homogeneity in large dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals, and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions which allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional …
Quasi-Maximum Likelihood Estimation For Spatial Panel Data Regressions, Zhenlin Yang
Quasi-Maximum Likelihood Estimation For Spatial Panel Data Regressions, Zhenlin Yang
Research Collection School Of Economics
This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize the data, thus the transformed models are more robust with respect to the normality assumption compared with the standard ones. QMLE method provides additional protection against violation of normality assumption. Asymptotic properties of the QMLEs are investigated. Numerical illustrations are provided.
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Research Collection School Of Economics
The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robtist chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon …
Estimation Of Time-Varying Adjusted Probability Of Informed Trading And Probability Of Symmetric Order-Flow Shock, Daniel Preve, Yiu Kuen Tse
Estimation Of Time-Varying Adjusted Probability Of Informed Trading And Probability Of Symmetric Order-Flow Shock, Daniel Preve, Yiu Kuen Tse
Research Collection School Of Economics
Recently Duarte and Young (2009) study the probability of informed trading (PIN) proposed by Easley et al. (2002) and decompose it into two parts: the adjusted PIN (APIN) as a measure of asymmetric information and the probability of symmetric order-flow shock (PSOS) as a measure of illiquidity. They provide some cross-section estimates of these measures using daily data over annual periods. In this paper we propose a method to estimate daily APIN and PSOS by extending the method in Tay et al. (2009) using high-frequency transaction data. Our empirical results show that while PIN is positively contemporaneously correlated with variance, …
Limit Theory For An Explosive Autoregressive Process, Xiaohu Wang, Jun Yu
Limit Theory For An Explosive Autoregressive Process, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Large sample properties are studied for a first-order autoregression (AR(1)) with a root greater than unity. It is shown that, contrary to the AR coefficient, the least-squares (LS) estimator of the intercept and its t-statistic are asymptotically normal without requiring the Gaussian error distribution, and hence an invariance principle applies. The coefficient based test and the t test have better power for testing the hypothesis of zero intercept in the explosive process than in the stationary process.
Nonparametric Regression Estimation With General Parametric Error Covariance: A More Efficient Two-Step Estimator, Liangjun Su, Aman Ullah, Yun Wang
Nonparametric Regression Estimation With General Parametric Error Covariance: A More Efficient Two-Step Estimator, Liangjun Su, Aman Ullah, Yun Wang
Research Collection School Of Economics
Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the asymptotic theorem for the slope estimators; second, we propose a more efficient two-step estimator for nonparametric regression function with general parametric error covariance, and develop the corresponding asymptotic theorems. Monte Carlo study shows the relative efficiency loss of MY’s estimator in …
Detecting Bubbles In Hong Kong Residential Property Market, Matthew S. Yiu, Jun Yu, Lu Jin
Detecting Bubbles In Hong Kong Residential Property Market, Matthew S. Yiu, Jun Yu, Lu Jin
Research Collection School Of Economics
This study uses a newly developed bubble detection method (Phillips, Shi, and Yu, 2011) to identify real estate bubbles in the Hong Kong residential property market. Our empirical results reveal several positive bubbles in the Hong Kong residential property market, including one in 1995, a stronger one in 1997, yet another one in 2004, and a more recent one in 2008. In addition, the method identifies two negative bubbles in the data, one in 2000 and the other one in 2001. These empirical results continue to be valid for the mass segment and the luxury segment. However, this method has …
Nonparametric Dynamic Panel Data Models: Kernel Estimation And Specification Testing, Liangjun Su, Xun Lu
Nonparametric Dynamic Panel Data Models: Kernel Estimation And Specification Testing, Liangjun Su, Xun Lu
Research Collection School Of Economics
Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced model—the fact that the two additive components have the same functional form, and the unknown function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity in typical dynamic panel data …
Modeling Myopia: Application To Non-Renewable Resource Extraction, Tomoki Fujii
Modeling Myopia: Application To Non-Renewable Resource Extraction, Tomoki Fujii
Research Collection School Of Economics
We develop a parsimonious model of myopia with an infinitesimal period of commitment as an extension to a standard dynamic optimization in a continuous-time environment. We clearly distinguish the processes of planning future controls and choosing the current control, which makes the model both analytically and numerically convenient. In its application to a simple non-renewable resource extraction problem, we show that whether the terminal time is free or fixed determines the appropriateness of the approximation to myopic agents by constant discounting. We also show that the expiry of extraction permits may be useful in the presence of myopia.
Shrinkage Empirical Likelihood Estimator In Longitudinal Analysis With Time-Dependent Covariates: Application To Modeling The Health Of Filipino Children, Denis H. Y. Leung, Dylan S. Small, Jing Qin, Min Zhu
Shrinkage Empirical Likelihood Estimator In Longitudinal Analysis With Time-Dependent Covariates: Application To Modeling The Health Of Filipino Children, Denis H. Y. Leung, Dylan S. Small, Jing Qin, Min Zhu
Research Collection School Of Economics
The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may …
Semiparametric Estimation In Triangular System Equations With Nonstationarity, Jiti Gao, Peter C. B. Phillips
Semiparametric Estimation In Triangular System Equations With Nonstationarity, Jiti Gao, Peter C. B. Phillips
Research Collection School Of Economics
A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary integrated time series. Semiparametric least squares (SLS) estimation is considered and its asymptotic properties are derived. Due to endogeneity in the parametric regressors, SLS is not consistent for the parametric component and a semiparametric instrumental variable (SIV) method is proposed instead. Under certain regularity conditions, the SIV …
Heteroskedasticity And Non-Normality Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Heteroskedasticity And Non-Normality Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung (2011), we introduce general methods to modify the standard LM tests so that they become robust against heteroskedasticity and non-normality. The idea behind the robustification is to decompose the concentrated score function into a sum of uncorrelated terms so that the outer product of gradient (OPG) can be used to estimate its variance. We also provide methods for improving …
Robust Bayesian Model Selection, Yong Li, Jun Yu
Robust Bayesian Model Selection, Yong Li, Jun Yu
Research Collection School Of Economics
This paper extends the robust Bayesian inference in misspecified models of Müller (2013, Econometrica) to Bayesian model selection of a set of misspecified models. It is shown that when a model is misspecified, under the Kullback-Leibler loss function, the risk associated with Müller's posterior is less (weakly) than that with the original posterior distribution asymptotically. Based on this new result, two new information criteria are proposed for model selection under model misspecification. Sufficient conditions are provided for the risk associated with Müller's posterior to be strictly smaller.
Testing For Multiple Bubbles 1: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Testing For Multiple Bubbles 1: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Research Collection School Of Economics
Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple bubble phenomena within the same sample period. To meet this challenge the present paper develops a new recursive flexible window method that is better suited for practical implementation with long …
Testing For Multiple Bubbles 2: Limit Theory Of Real Time Detectors, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Testing For Multiple Bubbles 2: Limit Theory Of Real Time Detectors, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Research Collection School Of Economics
This paper provides the limit theory of real time dating algorithms for bubble detection that were suggested in Phillips, Wu and Yu (2011, PWY) and Phillips, Shi and Yu (2013b, PSY). Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolves as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right tailed unit root tests (each with a di§erent recursive algorithm) that may be used in real time to locate the origination and collapse dates of bubbles. …
Inconsistent Var Regression With Common Explosive Roots, Peter C. B. Phillips, Tassos Magdalinos
Inconsistent Var Regression With Common Explosive Roots, Peter C. B. Phillips, Tassos Magdalinos
Research Collection School Of Economics
Nielsen (Working paper, University of Oxford, 2009) shows that vector autoregression is inconsistent when there are common explosive roots with geometric multiplicity greater than unity. This paper discusses that result, provides a coexplosive system extension and an illustrative example that helps to explain the finding, gives a consistent instrumental variable procedure, and reports some simulations. Some exact limit distribution theory is derived and a useful new reverse martingale central limit theorem is proved.
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We propose nonparametric estimators of the occupation measure and the occupation density of the diffusion coefficient (stochastic volatility) of a discretely observed Itô semimartingale on a fixed interval when the mesh of the observation grid shrinks to zero asymptotically. In a first step we estimate the volatility locally over blocks of shrinking length, and then in a second step we use these estimates to construct a sample analogue of the volatility occupation time and a kernel-based estimator of its density. We prove the consistency of our estimators and further derive bounds for their rates of convergence. We use these results …
Trial And Error In Influential Social Networks, Xiaohui Bei, Ning Chen, Liyu Dou, Xiangru Huang, Ruixin Qiang
Trial And Error In Influential Social Networks, Xiaohui Bei, Ning Chen, Liyu Dou, Xiangru Huang, Ruixin Qiang
Research Collection School Of Economics
In this paper, we introduce a trial-And-error model to study information diffusion in a social network. Specifically, in every discrete period, all individuals in the network concurrently try a new technology or product with certain respective probabilities. If it turns out that an individual observes a better utility, he will then adopt the trial; otherwise, the individual continues to choose his prior selection. We first demonstrate that the trial and error behavior of individuals characterizes certain global community structures of a social network, from which we are able to detect macro-communities through the observation of microbehavior of individuals. We run …
Economic Indices: Managing By The Numbers, Singapore Management University
Economic Indices: Managing By The Numbers, Singapore Management University
Perspectives@SMU
Understanding indices is not just about crunching numbers, but appreciating how it is constructed
Robust Estimation And Inference For Jumps In Noisy High Frequency Data: A Local-To-Continuity Theory For The Pre-Averaging Method, Jia Li
Research Collection School Of Economics
We develop an asymptotic theory for the pre-averaging estimator when asset price jumps are weakly identified, here modeled as local to zero. The theory unifies the conventional asymptotic theory for continuous and discontinuous semimartingales as two polar cases with a continuum of local asymptotics, and explains the breakdown of the conventional procedures under weak identification. We propose simple bias-corrected estimators for jump power variations, and construct robust confidence sets with valid asymptotic size in a uniform sense. The method is also robust to certain forms of microstructure noise.
Cost-Effective Estimation Of The Population Mean Using Prediction Estimators, Tomoki Fujii, Roy Van Der Weide
Cost-Effective Estimation Of The Population Mean Using Prediction Estimators, Tomoki Fujii, Roy Van Der Weide
Research Collection School Of Economics
This paper considers the prediction estimator as an efficient estimator for the population mean. The study may be viewed as an earlier study that proved that the prediction estimator based on the iteratively weighted least squares estimator outperforms the sample mean. The analysis finds that a certain moment condition must hold in general for the prediction estimator based on a Generalized-Method-of-Moment estimator to be at least as efficient as the sample mean. In an application to cost-effective double sampling, the authors show how prediction estimators may be adopted to maximize statistical precision (minimize financial costs) under a budget constraint (statistical …
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Research Collection School Of Economics
To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in details under several popular spatial LM …
A Nonparametric Poolability Test For Panel Data Models With Cross Section Dependence, Sainan Jin, Liangjun Su
A Nonparametric Poolability Test For Panel Data Models With Cross Section Dependence, Sainan Jin, Liangjun Su
Research Collection School Of Economics
In this article we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymptotic normal distributions of our test statistic under the null hypothesis of poolability and a sequence of local alternatives, and prove the consistency of our test. We also suggest a bootstrap method as an alternative way to obtain the critical values. A small set of Monte Carlo simulations indicate the test performs reasonably well in finite …
Nonparametric Dynamic Panel Data Models With Interactive Fixed Effects: Sieve Estimation And Specification Testing, Liangjun Su, Yonghui Zhang
Nonparametric Dynamic Panel Data Models With Interactive Fixed Effects: Sieve Estimation And Specification Testing, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper we analyze nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension and the time dimension are large. We propose sieve estimation for the nonparametric function by extending Bai’s (2009) principal component analysis (PCA) to our nonparametric framework. Based on the asymptotic expansion of the Gaussian quasi-log-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. …
Testing Monotonicity In Unobservables With Panel Data, Liangjun Su, Stefan Hoderlein, Halbert White
Testing Monotonicity In Unobservables With Panel Data, Liangjun Su, Stefan Hoderlein, Halbert White
Research Collection School Of Economics
Monotonicity in a scalar unobservable is a crucial identifying assumption for an important class of nonparametric structural models accommodating unobserved heterogeneity. Tests for this monotonicity have previously been unavailable. This paper proposes and analyzes tests for scalar monotonicity using panel data for structures with and without time-varying unobservables, either partially or fully nonseparable between observables and unobservables. Our nonparametric tests are computationally straightforward, have well behaved limiting distributions under the null, are consistent against precisely specified alternatives, and have standard local power properties. We provide straightforward bootstrap methods for inference. Some Monte Carlo experiments show that, for empirically relevant sample …
Local Linear Gmm Estimation Of Functional Coefficient Iv Models With Application To The Estimation Of Rate Of Return To Schooling, Liangjun Su, Irina Murtazashvili, Aman Ullah
Local Linear Gmm Estimation Of Functional Coefficient Iv Models With Application To The Estimation Of Rate Of Return To Schooling, Liangjun Su, Irina Murtazashvili, Aman Ullah
Research Collection School Of Economics
We consider the local linear GMM estimation of functional coe cient 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 allowed for. We propose a nonparametric test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis as well as a sequence of local alternatives and global alternatives, and propose a bootstrap version for …
Nonparametric Testing For Asymmetric Information, Liangjun Su, Martin Spindler
Nonparametric Testing For Asymmetric Information, Liangjun Su, Martin Spindler
Research Collection School Of Economics
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 interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information, which is applicable in a variety of situations. We demonstrate that the test works reasonably well through …
Collusion Set Detection Using A Quasi Hidden Markov Model, Zhengxiao Wu, Xiaoyu Wu
Collusion Set Detection Using A Quasi Hidden Markov Model, Zhengxiao Wu, Xiaoyu Wu
Research Collection School Of Economics
In stock market, a collusion set is defined as a group of individuals or organizations who act cooperatively with an intention of manipulating security price. Collusion-based malpractices impose large costs on the economy, but few techniques have yet been developed for collusion set detection. In this article, we propose a quasi hidden Markov model (QHMM) approach. In particular, we consider the transactions as a marked point process with hidden states, and we calculate the class conditional probabilities to identify the malicious transactions. The detection algorithms associated with the model are recursive, hence suitable for online monitoring and detection. The QHMM …