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Articles 481 - 510 of 828
Full-Text Articles in Econometrics
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 …
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 …
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 …
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 …
Bias In The Mean Reversion Estimator In Continuous-Time Gaussian And Lévy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Bias In The Mean Reversion Estimator In Continuous-Time Gaussian And Lévy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Research Collection School Of Economics
Continuous-time Levy processes have become increasingly popular in the asset pricing literature and estimation of the mean reversion parameter has attracted attention recently. This paper develops the approximate nite-sample bias of the ordinary least squares or quasi maximum likelihood estimator of the mean reversion parameter in continuous-time Levy processes. Simulations show that in general the approximate bias works well in capturing the true bias of the mean reversion estimator under difference scenarios. However, when the time span is small and the mean reversion parameter is approaching its lower bound, we find it more difficult to approximate well the nite-sample bias.
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The robustness of the Lagrange Multiplier (LM) tests for spatial error dependence of Burridge (1980) and Born and Breitung (2011) for the linear regression model, and Anselin (1988) and Debarsy and Etur (2010) for the panel regression model with random or fixed effects are examined. While all tests are asymptotically robust against distributional mis‐specification, their finite sample behaviour may be sensitive to the spatial layout. To overcome this shortcoming, standardized LM tests are suggested. Monte Carlo results show that the new tests possess good finite sample properties. An important observation made throughout this study is that the LM tests for …
A Nonparametric Goodness-Of-Fit-Based Test For Conditional Heteroskedasticity, Liangjun Su, Aman Ullah
A Nonparametric Goodness-Of-Fit-Based Test For Conditional Heteroskedasticity, Liangjun Su, Aman Ullah
Research Collection School Of Economics
In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R2) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p-values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in …
Estimation Of Monthly Volatility: An Empirical Comparison Of Realized Volatility, Garch And Acd-Icv Methods, Shouwei Liu, Yiu Kuen Tse
Estimation Of Monthly Volatility: An Empirical Comparison Of Realized Volatility, Garch And Acd-Icv Methods, Shouwei Liu, Yiu Kuen Tse
Research Collection School Of Economics
We apply the ACD-ICV method proposed by Tse and Yang (2011) for the estimation of intraday volatility to estimate monthly volatility, and empirically compare this method against the realized volatility (RV) and generalized autoregressive conditional heteroskedasticity (GARCH) methods. Our Monte Carlo results show that the ACD-ICV method performs well against the other two methods. Evidence on the Chicago Board Options Exchange volatility index (VIX) shows that it predicts the ACD-ICV volatility estimates better than it predicts the RV estimates. While the RV method is popular for the estimation of monthly volatility, its performance is inferior to the GARCH method.
Three Essays On Large Panel Data Models With Cross-Sectional Dependence, Yonghui Zhang
Three Essays On Large Panel Data Models With Cross-Sectional Dependence, Yonghui Zhang
Dissertations and Theses Collection (Open Access)
My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects. Chapter 2 introduces a nonparametric test for cross-sectional contemporaneous dependence in large dimensional panel data models based on the squared distance between the pair-wise joint density and the product of the marginals. The test can be applied to either raw observable data …
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Research Collection School Of Economics
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time …
Qml Estimation Of Dynamic Panel Data Models With Spatial Errors, Liangjun Su, Zhenlin Yang
Qml Estimation Of Dynamic Panel Data Models With Spatial Errors, Liangjun Su, Zhenlin Yang
Research Collection School Of Economics
We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when …
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 exogeous 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 …
Variable Selection In Nonparametric And Semiparametric Regression Models, Liangjun Su, Yonghui Zhang
Variable Selection In Nonparametric And Semiparametric Regression Models, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
This chapter reviews the literature on variable selection in nonparametric and semiparametric regression models via shrinkage. We highlight recent developments on simultaneous variable selection and estimation through the methods of least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD) or their variants, but restrict our attention to nonparametric and semiparametric regression models. In particular, we consider variable selection in additive models, partially linear models, functional/varying coefficient models, single index models, general nonparametric regression models, and semiparametric/nonparametric quantile regression models.
Non-Normality And Heteroscedasticity Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Non-Normality And Heteroscedasticity 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 …
Testing Whether The Underlying Continuous-Time Process Follows A Diffusion: An Infinitesimal Operator-Based Approach, Bin Chen, Zhaogang Song
Testing Whether The Underlying Continuous-Time Process Follows A Diffusion: An Infinitesimal Operator-Based Approach, Bin Chen, Zhaogang Song
Research Collection Lee Kong Chian School Of Business
We develop a nonparametric test to check whether a process can be represented by a stochastic differential equation driven only by a Brownian motion. Our testing procedure utilizes the infinitesimal operator-based martingale characterization combined with a generalized spectral approach. Such a testing procedure is feasible and convenient because the infinitesimal operator of the diffusion process has a closed-form expression. The proposed test is applicable to both univariate and multivariate processes and has an limit distribution under the diffusion hypothesis. Simulation and empirical studies show that the proposed test has reasonable performance in small samples.
Optimal Design Of P-Value Consistent Step-Up Procedures For Multiple Comparisons With A Control In Direction-Mixed Families, Koon Shing Kwong, Siu Hung Cheung
Optimal Design Of P-Value Consistent Step-Up Procedures For Multiple Comparisons With A Control In Direction-Mixed Families, Koon Shing Kwong, Siu Hung Cheung
Research Collection School Of Economics
It is common in clinical studies for several treatments to be compared to a control. Most of the related statistical techniques have been developed to accommodate inferential families in which all hypotheses are either one- or two-sided such that the familywise error rate is controlled at a specified level. Several multiple testing procedures were recently proposed to perform multiple comparisons with a control in direction-mixed families that contain a mixture of one- and two-sided inferences. Of these procedures, the p-value consistent step-up procedure is found to be superior in terms of its power and p-value consistent property. In this paper, …
Singapore Inflation Expectations: Expecting The Unexpected, Aurobindo Ghosh, Jun Yu
Singapore Inflation Expectations: Expecting The Unexpected, Aurobindo Ghosh, Jun Yu
Research Collection School Of Economics
The study of inflation expectations of Singapore house-holds is a multi-disciplinary industry-relevant research that comes out of a partnership between Singapore Management University (SMU) and MasterCard. The research team for this MasterCard-SKBI Singapore Index of Inflation Expectations (SInDEx) project applied rigorous methods using current internet-based marketing survey tools for data-collection and advanced econometric techniques to analyse the data. The updates from the quarterly waves are keenly followed by policymakers, market watchers and the media because of the enormous importance of cost of living to individuals and businesses alike.
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Approach, Yiu Kuen Tse, Thomas Tao Yang
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Approach, Yiu Kuen Tse, Thomas Tao Yang
Research Collection School Of Economics
We propose a method to estimate the intraday volatility of a stock by integrating the instantaneous conditional return variance per unit time obtained from the autoregressive conditional duration (ACD) model, called the ACD-ICV method. We compare the daily volatility estimated using the ACD-ICV method against several versions of the realized volatility (RV) method, including the bipower variation RV with subsampling, the realized kernel estimate, and the duration-based RV. Our Monte Carlo results show that the ACD-ICV method has lower root mean-squared error than the RV methods in almost all cases considered. This article has online supplementary material.
Optimal Estimation Under Nonstandard Conditions, Werner Ploberger, Peter C. B. Phillips
Optimal Estimation Under Nonstandard Conditions, Werner Ploberger, Peter C. B. Phillips
Research Collection School Of Economics
We analyze optimality properties of maximum likelihood (ML) and other estimators when the problem does not necessarily fall within the locally asymptotically normal (LAN) class, therefore covering cases that are excluded from conventional LAN theory such as unit root nonstationary time series. The classical Hajek-Le Cam optimality theory is adapted to cover this situation. We show that the expectation of certain monotone "bowl-shaped" functions of the squared estimation error are minimized by the ML estimator in locally asymptotically quadratic situations, which often occur in nonstationary time series analysis when the LAN property fails. Moreover, we demonstrate a direct connection between …
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, 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, the method finds a …
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C. B. Phillips
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C. B. Phillips
Research Collection School Of Economics
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation for the corresponding estimators is determined both by the sample size n and a parameter measuring the proximity of the model to the unit root boundary. (C) 2012 Elsevier B.V. All rights reserved.
Robust Deviance Information Criterion For Latent Variable Models, Yong Li, Tao Zeng, Jun Yu
Robust Deviance Information Criterion For Latent Variable Models, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular and hence invalidates the standard asymptotic arguments. A new information criterion, robust DIC (RDIC), is proposed for Bayesian comparison of latent variable models. RDIC is shown to be a good approximation to DIC without data augmentation. While the later quantity is difficult to compute, the expectation { …
Recent Advances In Nonstationary Time Series: A Festschrift In Honor Of Peter C. B. Phillips, Robert S. Mariano, Zhijie Xiao, Jun Yu
Recent Advances In Nonstationary Time Series: A Festschrift In Honor Of Peter C. B. Phillips, Robert S. Mariano, Zhijie Xiao, Jun Yu
Research Collection School Of Economics
On July 14–15, 2008, the School of Economics and the Sim Kee Boon Institute for Financial Economics at Singapore Management University (SMU) co-hosted a conference honoring the contribution of Peter Phillips to econometrics and statistics, in celebration of his 60th birthday. In total, 51 papers were presented by his colleagues and former students, who deeply appreciate and respect Peter as a true scholar and a good friend. These papers mainly cover two areas of Peter’s current research interests—nonstationary time series analysis, and panel, nonlinear and nonparametric models. On the basis of this conference, we have taken the opportunity to edit …
Statistical Tests For Multiple Forecast Comparison, Roberto Mariano, Daniel P. A. Preve
Statistical Tests For Multiple Forecast Comparison, Roberto Mariano, Daniel P. A. Preve
Research Collection School Of Economics
We consider a multivariate version of the Diebold–Mariano test for equal predictive ability of three or more forecasting models. The Wald-type test, S, which has a null distribution that is asymptotically chi-squared, is shown to be generally invariant with respect to the ordering of the models being compared. Finite-sample corrections for the test are also developed. Monte Carlo simulations indicate that S has reasonable size properties in large samples but tends to be oversized in moderate samples. The finite-sample correction succeeds in correcting for size, but only partially. For the size-adjusted tests, power increases with sample size, as expected. It …
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Research Collection School Of Economics
In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.
Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu
Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu
Research Collection School Of Economics
It is well known that for continuous time models with a linear drift standard estimation methods yield biased estimators for the mean reversion parameter both in finite discrete samples and in large in-fill samples. In this paper, we obtain two expressions to approximate the bias of the least squares/maximum likelihood estimator of the mean reversion parameter in the Ornstein-Uhlenbeck process with a known long run mean when discretely sampled data are available. The first expression mimics the bias formula of Marriott and Pope (1954) for the discrete time model. Simulations show that this expression does not work satisfactorily when the …
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Research Collection School Of Economics
An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the finite-sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the finite-sample distributions of the estimators uniformly well irrespective of the strength of the identification. An important technical component of …
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C. B. Phillips
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C. B. Phillips
Research Collection School Of Economics
Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression under misspecified lag structure are derived, showing the NW estimator to be inconsistent, in general, with a "pseudo-true function" limit that is a local average of the true regression function. In this respect nonlinear cointegrating regression differs importantly from conventional linear cointegration which is invariant to time translation. When centred on the pseudo-true function and appropriately scaled, the NW estimator still has a mixed …
Semiparametric Gmm Estimation Of Spatial Autoregressive Models, Liangjun Su
Semiparametric Gmm Estimation Of Spatial Autoregressive Models, Liangjun Su
Research Collection School Of Economics
We propose semiparametric GMM estimation of semiparametric spatial autoregressive (SAR) models under weak moment conditions. In comparison with the quasi-maximum-likelihood-based semiparametric estimator of Su and Jin (2010), we allow for both heteroscedasticity and spatial dependence in the error terms. We derive the limiting distributions of our estimators for both the parametric and nonparametric components in the model and demonstrate the estimator of the parametric component has the usual -asymptotics. When the error term also follows an SAR process, we propose an estimator for the parameter in the SAR error process and derive the joint asymptotic distribution for both spatial parameters. …