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Articles 391 - 420 of 828
Full-Text Articles in Econometrics
Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang
Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi-maximum likelihood (QML) estimator of the spatial autoregressive (SAR) model can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in certain situations and when it does the regular QML estimator can still be consistent. In cases where this condition is violated, we propose a simple modified QML estimation method robust against unknown heteroskedasticity. In both cases, asymptotic distributions of the estimators are derived, and methods …
Testing Additive Separability Of Error Term In Nonparametric Structural Models, Liangjun Su, Yundong Tu, Aman Ullah
Testing Additive Separability Of Error Term In Nonparametric Structural Models, Liangjun Su, Yundong Tu, Aman Ullah
Research Collection School Of Economics
This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively. We propose a test statistic under a set of identification conditions considered by Hoderlein, Su and White (2012), which require the existence of a control variable such that the regressor is independent of the error term given the control variable. The test statistic is motivated from the observation that, under the additive error structure, the partial derivative of the nonparametric structural function with respect to the error term is one under identification. The asymptotic distribution …
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a consistent nonparametric test for linearity in panel data models with interactive fixed effects. To construct the test statistic, we need to estimate the model under the null hypothesis of linearity and then obtain the restricted residuals. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed both under the null hypothesis and a sequence of Pitman local alternatives. To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap p-values. A small set of Monte Carlo simulations illustrates that our test performs well in …
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
Research Collection School Of Economics
Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated …
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
A Bayesian test statistic is proposed to assess the model specification after the model is estimated by Bayesian MCMC methods. The proposed approach does not require an alternative model to be specified and is applicable to a variety of models, including latent variable models, structural dynamic choice models, and dynamics stochastic general equilibrium (DSGE) models, for which frequentist methods are difficult to use. The properties of the test statistic are established and its implementation is discussed. The test is easy to use and the test statistic can be calculated from MCMC outputs even when there are latent variables. The method …
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 consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon andWeidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to …
Optimal Jackknife For Unit Root Models, Ye Chen, Jun Yu
Optimal Jackknife For Unit Root Models, Ye Chen, Jun Yu
Research Collection School Of Economics
A new jackknife method is introduced to remove the first order bias in unit root models. It is optimal in the sense that it minimizes the variance among all the jackknife estimators of the form considered in Phillips and Yu (2005) and Chambers and Kyriacou (2013) after the number of subsamples is selected. Simulations show that the new jackknife reduces the variance of that of Chambers and Kyriacou by about 10% for any selected number of subsamples without compromising bias reduction. The results continue to hold true in near unit root models. (C) 2014 Elsevier B.V. All rights reserved.
A Combined Approach To The Inference Of Conditional Factor Models, Yan Li, Liangjun Su, Yuewa Xu
A Combined Approach To The Inference Of Conditional Factor Models, Yan Li, Liangjun Su, Yuewa Xu
Research Collection School Of Economics
This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature-the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is …
Nonparametric Predictive Regression, Ioannis Kasparis, Elena Andreou, Peter C. B. Phillips
Nonparametric Predictive Regression, Ioannis Kasparis, Elena Andreou, Peter C. B. Phillips
Research Collection School Of Economics
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The limit distribution of these predictive tests is nuisance parameter free and holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit root processes. Asymptotic theory and simulations show that the proposed tests are more powerful than existing parametric predictability tests when deviations from unity are large or the predictive regression is nonlinear. Empirical illustrations to monthly SP500 stock returns data are provided. …
Lag Length Selection For Unit Root Tests In The Presence Of Nonstationary Volatility, Giuseppe Cavaliere, Peter C. B. Phillips, Stephan Smeekes, A. M. Robert Taylor
Lag Length Selection For Unit Root Tests In The Presence Of Nonstationary Volatility, Giuseppe Cavaliere, Peter C. B. Phillips, Stephan Smeekes, A. M. Robert Taylor
Research Collection School Of Economics
A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency …
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 prove consistency 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 …
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Research Collection School Of Economics
We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved …
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Research Collection School Of Economics
We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved …
Asymptotic Theory For Linear Diffusions Under Alternative Sampling Scheme, Qiankun Zhou, Jun Yu
Asymptotic Theory For Linear Diffusions Under Alternative Sampling Scheme, Qiankun Zhou, Jun Yu
Research Collection School Of Economics
The asymptotic distributions of the maximum likelihood estimator of the persistence parameter are developed in a linear diffusion model under three sampling schemes, long-span, in-fill and double. Simulations suggest that the in-fill asymptotic distribution gives a more accurate approximation to the finite sample distribution than the other two distributions. An empirical application highlights the difference in unit root testing based on the alternative asymptotic distributions.
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 detail under several popular spatial LM …
Bias Correction For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Bias Correction For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Research Collection School Of Economics
This paper examines the finite sample properties of the quasi maximum likelihood (QML) estimators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general bias correction methods recently developed by Yang (2015), we derive up to third-order bias corrections for the QML estimators of the FE-SPD model, and propose a simple bootstrap method for their practical implementation. Monte Carlo results reveal that the QML estimators of the spatial parameters can be quite biased and that a second-order bias correction effectively removes the bias. The validity of the bootstrap method is established. Variance corrections …
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture the anomaly effects of some asset-specific characteristics and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known "error-in-variable" problem associated with the commonly used …
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Research Collection School Of Economics
In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure …
Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su
Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su
Research Collection School Of Economics
We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon and Weidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors …
The True Limit Distributions Of The Anderson-Hsiao Iv Estimators In Panel Autoregression, Peter C. B. Phillips, Chirok Han
The True Limit Distributions Of The Anderson-Hsiao Iv Estimators In Panel Autoregression, Peter C. B. Phillips, Chirok Han
Research Collection School Of Economics
This note derives the correct limit distributions of the Anderson-Hsiao (1981) levels and differences instrumental variable estimators, provides comparisons showing that the levels IV estimator has uniformly smaller variance asymptotically as the cross section (n) and time series (T) sample sizes tend to infinity, and compares these results with those of the first difference least squares (FDLS) estimator. (C) 2014 Elsevier B.V. All rights reserved.
Three Essays On Financial Econometrics, Jiang Liang
Three Essays On Financial Econometrics, Jiang Liang
Dissertations and Theses Collection (Open Access)
This dissertation develops several econometric techniques to address three issues in financial economics, namely, constructing a real estate price index, estimating structural break points, and estimating integrated variance in the presence of market microstructure noise and the corresponding microstructure noise function. Chapter 2 develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model …
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 Threshold Regression: Estimation And Inference, Daniel J. Henderson, Christopher F. Parmeter, Liangjun Su
Nonparametric Threshold Regression: Estimation And Inference, Daniel J. Henderson, Christopher F. Parmeter, Liangjun Su
Research Collection School Of Economics
The present work describes a simple approach to estimating the location of a threshold/changepoint in a nonparametric regression. This model has connections both to the time-series and regressiondiscontinuity literatures. The estimator leverages a simple decomposition, giving it the form of asemiparametric smooth coefficient model. Optimal bandwidth selection and a suite of testing facilitiesare also presented. Several empirical examples are provided to illustrate the implementation of themethods discussed here.
Specification Testing For Transformation Models With An Application To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Specification Testing For Transformation Models With An Application To Generalized Accelerated Failure-Time Models, Arthur Lewbel, Xun Lu, Liangjun Su
Research Collection School Of Economics
This paper provides a nonparametric test of the specification of a transformation model. Specifically, we test whether an observable outcome Y is monotonic in the sum of a function of observable covariates X plus an unobservable error U. Transformation models of this form are commonly assumed in economics, including, e.g., standard specifications of duration models and hedonic pricing models. Our test statistic is asymptotically normal under local alternatives and consistent against nonparametric alternatives violating the implied restriction. Monte Carlo experiments show that our test performs well in finite samples. We apply our results to test for specifications of generalized accelerated …
Testing Linearity Using Power Transforms Of Regressors, Yae In Baek, Jin Seo Cho, Peter C. B. Phillips
Testing Linearity Using Power Transforms Of Regressors, Yae In Baek, Jin Seo Cho, Peter C. B. Phillips
Research Collection School Of Economics
We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We call this modeling difficulty the trifold identification problem and show that it may be overcome using a test based on the quasi-likelihood ratio (QLR) statistic. More specifically, the QLR statistic may be approximated under each identification problem and the separate null approximations may be combined to produce a composite approximation that embodies the linear model hypothesis. …
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Based on the Girsanov theorem, this paper obtains the exact Önite sample distribution of the maximum likelihood estimator of structural break points in a continuous time model. The exact Önite sample theory suggests that, in empirically realistic situations, there is a strong Önite sample bias in the estimator of structural break points. This property is shared by least squares estimator of both the absolute structural break point and the fractional structural break point in discrete time models. A simulation-based method based on the indirect estimation approach is proposed to reduce the bias both in continuous time and discrete time models. …
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Research Collection School Of Economics
We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.
Testing The Martingale Hypothesis, Peter C. B. Phillips, Sainan Jin
Testing The Martingale Hypothesis, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide class of nonlinear, nonmartingale processes. Simulations show that the tests have good finite sample properties in comparison with other tests particularly under conditional heteroscedasticity …
Norming Rates And Limit Theory For Some Time-Varying Coefficient Autoregressions, Offer Lieberman, Peter C. B. Phillips
Norming Rates And Limit Theory For Some Time-Varying Coefficient Autoregressions, Offer Lieberman, Peter C. B. Phillips
Research Collection School Of Economics
A time-varying autoregression is considered with a similarity-based coefficient and possible drift. It is shown that the random-walk model has a natural interpretation as the leading term in a small-sigma expansion of a similarity model with an exponential similarity function as its AR coefficient. Consistency of the quasi-maximum likelihood estimator of the parameters in this model is established, the behaviours of the score and Hessian functions are analysed and test statistics are suggested. A complete list is provided of the normalization rates required for the consistency proof and for the score and Hessian function standardization. A large family of unit …