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Full-Text Articles in Econometrics

Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips Nov 2016

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 techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are consideredpenalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear 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 …

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Shrinkage Estimation Of Covariance Matrix For Portfolio Choice With High Frequency Data, Cheng Liu, Ningning Xia, Jun Yu Nov 2016

Shrinkage Estimation Of Covariance Matrix For Portfolio Choice With High Frequency Data, Cheng Liu, Ningning Xia, Jun Yu

Research Collection School Of Economics

This paper examines the usefulness of high frequency data in estimating the covariancematrix for portfolio choice when the portfolio size is large. A computationally convenientnonlinear shrinkage estimator for the integrated covariance (ICV) matrix of financial as-sets is developed in two steps. The eigenvectors of the ICV are first constructed from adesigned time variation adjusted realized covariance matrix of noise-free log-returns of rel-atively low frequency data. Then the regularized eigenvalues of the ICV are estimated byquasi-maximum likelihood based on high frequency data. The estimator is always positivedefinite and its inverse is the estimator of the inverse of ICV. It minimizes the …

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Bias Correction And Refined Inferences For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu Nov 2016

Bias Correction And Refined Inferences For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu

Research Collection School Of Economics

This paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the …

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Homogeneity Pursuit In Panel Data Models: Theory And Applications, Wuyi Wang, Peter C. B. Phillips, Liangjun Su Nov 2016

Homogeneity Pursuit In Panel Data Models: Theory And Applications, Wuyi Wang, Peter C. B. Phillips, Liangjun Su

Research Collection School Of Economics

This paper studies estimation of a panel data model with latent structures where individuals can be classified into different groups where slope parameters are homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS which is a systematic extension of the CARDS procedure proposed by Ke, Fan, and Wu (2015) in a cross section framework. The extension addresses the problem of comparing vector coefficients in a panel model for homogeneity and introduces a new concept of controlled classification of multidimensional quantities called the segmentation net. We …

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Robust Forecast Comparison, Sainan Jin, Valentina Corradi, Norman R. Swanson Oct 2016

Robust Forecast Comparison, Sainan Jin, Valentina Corradi, Norman R. Swanson

Research Collection School Of Economics

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and we develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton, Maasoumi, and Whang (2005, Review of Economic Studies …

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Estimating The Volatility Occupation Time Via Regularized Laplace Inversion, Jia Li, Viktor Todorov, Tauchen Oct 2016

Estimating The Volatility Occupation Time Via Regularized Laplace Inversion, Jia Li, Viktor Todorov, Tauchen

Research Collection School Of Economics

We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far …

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A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei Oct 2016

A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei

Research Collection School Of Economics

This paper provides a practical test for strict exogeneity in linear panel data models with fixed effects when the number of individuals N goes to infinity while the number of time periods T is fixed. The test is based on the supremum of a sequence of Wald test statistics. Under suitable conditions, we establish the asymptotic distribution of the test statistic and consistency of the test. A bootstrap procedure is proposed to improve the finite sample performance and the validity of the procedure is justified. We investigate the finite sample performance of the test via a small set of Monte …

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Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips Sep 2016

Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips

Research Collection School Of Economics

This paper characterizes systematic risk stemming from the possible occurrence of price bubbles and measures the impact of this additional risk factor on asset prices. Historical stock market behavior and recent empirical experience have led economists and policy makers to acknowledge that price bubbles in financial markets do occur and need to be accounted for in risk analysis. New econometric tools for analyzing mildly explosive behavior (Phillips and Magdalinos, 2007; Phillips et al., 2011) have made it possible to detect the presence of bubbles in data and to date stamp their origination and collapse, providing empirical confirmation of such episodes …

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Modeling Speculative Bubbles With Diverse Investor Expectations, Peter C. B. Phillips Sep 2016

Modeling Speculative Bubbles With Diverse Investor Expectations, Peter C. B. Phillips

Research Collection School Of Economics

We construct a model of asset market exuberance, collapse and recovery using subjective investor-based rational expectations about the impact of fundamentals on the market price. Investors are assumed to have heterogeneous market sentiments, allowing them to be exuberant, cautious, or fundamentalist via boundary conditions that describe their respective views of the market impact of the same economic fundamentals. Equilibrium solution paths of the model take varying forms, depending on the parameter settings that reflect the importance of each type of market participant. This rational expectations model of asset pricing is shown to be consistent with a simple explosive continuous time …

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Asymptotic Theory For Estimating The Persistent Parameter In The Fractional Vasicek Model, Weilin Xiao, Jun Yu Sep 2016

Asymptotic Theory For Estimating The Persistent Parameter In The Fractional Vasicek Model, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper develops the asymptotic theory for the least squares (LS) estimator of the persistent parameter in the fractional Vasicek model when a continuous record of observations is available. The fractional Vasicek model is assumed to be driven by the fractional Brownian motion with a known Hurst parameter greater than or equal to one half. It is shown that the asymptotic properties depend on the sign of the persistent parameter, corresponding to the stationary case, the explosive case and the null recurrent case. The strong consistency and the asymptotic distribution are obtained in all three cases.

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Testing Of Non-Inferiority And Superiority For Three-Arm Clinincal Studies With Multiple Experimental Treatments, Junjiang Zhong, Koon Shing Kwong, Siu Hung Cheung Sep 2016

Testing Of Non-Inferiority And Superiority For Three-Arm Clinincal Studies With Multiple Experimental Treatments, Junjiang Zhong, Koon Shing Kwong, Siu Hung Cheung

Research Collection School Of Economics

The purpose of a non-inferiority trial is to assert the efficacy of an experimental treatment compared with a reference treatment by showing that the experimental treatment retains a substantial proportion of the efficacy of the reference treatment. Statistical methods have been developed to test multiple experimental treatments in three-arm non-inferiority trials. In this paper, we report the development of procedures that simultaneously test the non-inferiority and the superiority of experimental treatments after the assay sensitivity has been established. The advantage of the proposed test procedures is the additional ability to identify superior treatments while retaining an non-inferiority testing power comparable …

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Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips Sep 2016

Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips

Research Collection School Of Economics

This paper characterizes systematic risk stemming from the possible occurrence of price bubbles and measures the impact of this additional risk factor on asset prices. Historical stock market behavior and recent empirical experience have led economists and policy makers to acknowledge that price bubbles in financial markets do occur and need to be accounted for in risk analysis. New econometric tools for analyzing mildly explosive behavior (Phillips and Magdalinos, 2007; Phillips et al., 2011) have made it possible to detect the presence of bubbles in data and to date stamp their origination and collapse, providing empirical confirmation of such episodes …

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Generalized Method Of Integrated Moments For High-Frequency Data, Jia Li, Dacheng Xiu Jul 2016

Generalized Method Of Integrated Moments For High-Frequency Data, Jia Li, Dacheng Xiu

Research Collection School Of Economics

We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second‐step GMM estimation, which requires the correction of a high‐order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and …

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Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang Jul 2016

Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang

Research Collection School Of Economics

Monotonicity in a scalar unobservable is a common assumption when modeling heterogeneity in structural models. Among other things, it allows one to recover the underlying structural function from certain conditional quantiles of observables. Nevertheless, monotonicity is a strong assumption and in some economic applications unlikely to hold, e.g., random coefficient models. Its failure can have substantive adverse consequences, in particular inconsistency of any estimator that is based on it. Having a test for this hypothesis is hence desirable. This paper provides such a test for cross-section data. We show how to exploit an exclusion restriction together With a conditional independence …

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Double Asymptotics For Explosive Continuous Time Models, Xiaohu Wang, Jun Yu Jul 2016

Double Asymptotics For Explosive Continuous Time Models, Xiaohu Wang, Jun Yu

Research Collection School Of Economics

This paper establishes a double asymptotic theory for explosive continuous time Levy-driven processes and the corresponding exact discrete time models. The double asymptotic theory assumes the sample size diverges because the sampling interval (h) shrinks to zero and the time span (N) diverges. Both the simultaneous and sequential double asymptotic distributions are derived. In contrast to the long-time span asymptotics (N -> infinity with fixed h) where no invariance principle applies, the double asymptotic distribution is derived without assuming Gaussian errors, so an invariance principle applies, as the asymptotic theory for the mildly explosive process developed by Phillips and Magdalinos …

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Robust Econometric Inference With Mixed Integrated And Mildly Explosive Regressors, Peter C. B. Phillips, Ji Hyung Lee Jun 2016

Robust Econometric Inference With Mixed Integrated And Mildly Explosive Regressors, Peter C. B. Phillips, Ji Hyung Lee

Research Collection School Of Economics

This paper explores in several prototypical models a convenient inference procedure for nonstationary variable regression that enables robust chi-square testing for a wide class of persistent and endogenous regressors. The approach uses the mechanism of self-generated instruments called IVX instrumentation developed by Magdalinos and Phillips (2009b). We first show that these methods remain valid for regressors with local unit roots in the explosive direction and mildly explosive roots, where the roots are further from unity in the explosive direction than 0 (n(-1)). It is also shown that Wald testing procedures remain robust for multivariate regressors with certain forms of mixed …

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Nonparametric Cointegrating Regression With Endogeneity And Long Memory, Qiying Wang, Peter C. B. Phillips Apr 2016

Nonparametric Cointegrating Regression With Endogeneity And Long Memory, Qiying Wang, Peter C. B. Phillips

Research Collection School Of Economics

This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a, b, Econometric Theory 25, 710-738, Econometrica 77, 1901-1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating …

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Disentangling Greenhouse Warming And Aerosol Cooling To Reveal Earth's Climate Sensitivity, T. Storelvmo, T. Leirvik, U. Lohmann, Peter C. B. Phillips, M. Wild Apr 2016

Disentangling Greenhouse Warming And Aerosol Cooling To Reveal Earth's Climate Sensitivity, T. Storelvmo, T. Leirvik, U. Lohmann, Peter C. B. Phillips, M. Wild

Research Collection School Of Economics

Earth's climate sensitivity has long been subject to heated debate and has spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of its most likely range(1). Recent observational studies have produced estimates of transient climate sensitivity, that is, the global mean surface temperature increase at the time of CO2 doubling, as low as 1.3 K (refs 2,3), well below the best estimate produced by global climate models (1.8 K). Here, we present an observation-based study of the time period 1964 to 2010, which does not rely on climate models. The method incorporates observations of greenhouse gas …

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Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su Mar 2016

Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We establish the asymptotic distributions of the Lasso estimators of the regression coefficients and their post Lasso versions. We also propose and validate a data-driven method to determine the tuning parameter used in …

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Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su Mar 2016

Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su

Research Collection School Of Economics

In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified …

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Model Selection For Explosive Models, Yubo Tao, Jun Yu Mar 2016

Model Selection For Explosive Models, Yubo Tao, Jun Yu

Research Collection School Of Economics

This paper examines the limit properties of information criteria for distinguishing between the unit root model and the various kinds of explosive models. The information criteria include AIC, BIC, HQIC. The explosive models include the local-to-unit-root model, the mildly explosive model and the regular explosive model. Initial conditions with different order of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the unit root model when data come from the unit root model. When data come from the local-to-unit-root model, both BIC and …

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Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino Mar 2016

Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino

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 estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.

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Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang Feb 2016

Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang

Research Collection School Of Economics

Monotonicity in a scalar unobservable is a common assumption when modeling heterogeneity in structural models. Among other things, it allows one to recover the underlying structural function from certain conditional quantiles of observables. Nevertheless, monotonicity is a strong assumption and in some economic applications unlikely to hold, e.g., random coefficient models. Its failure can have substantive adverse consequences, in particular inconsistency of any estimator that is based on it. Having a test for this hypothesis is hence desirable. This paper provides such a test for cross-section data. We show how to exploit an exclusion restriction together with a conditional independence …

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Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White Feb 2016

Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White

Research Collection School Of Economics

Granger non-causality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G- non-causality follows from structural non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G- non-causality …

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Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju Feb 2016

Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju

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 …

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New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu Jan 2016

New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu

Research Collection School Of Economics

Based on the Girsanov theorem, this paper first obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is seriously biased. These two properties are also found in the finite sample distribution of the least squares estimator of structural break point in the discrete time model. The paper then builds a continuous time approximation to the discrete time model and develops an in-fill asymptotic theory for the least squares estimator. The obtained in-fill asymptotic distribution is asymmetric and tri-modal and delivers …

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Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang Jan 2016

Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang

Research Collection School Of Economics

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and …

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Meritocracy Voting: Measuring The Unmeasurable, Peter C. B. Phillips Jan 2016

Meritocracy Voting: Measuring The Unmeasurable, Peter C. B. Phillips

Research Collection School Of Economics

Learned societies commonly carry out selection processes to add new fellows to an existing fellowship. Criteria vary across societies but are typically based on subjective judgments concerning the merit of individuals who are nominated for fellowships. These subjective assessments may be made by existing fellows as they vote in elections to determine the new fellows or they may be decided by a selection committee of fellows and officers of the society who determine merit after reviewing nominations and written assessments. Human judgment inevitably plays a central role in these determinations and, notwithstanding its limitations, is usually regarded as being a …

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Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su Jan 2016

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 (2009) and Moon and Weidner (2015), 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 zero. …

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Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang Dec 2015

Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang

Research Collection School Of Economics

It is well known that quasi maximum likelihood (QML) estimation of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values, and a wrong treatment of them will result in inconsistency and serious bias. The same issues apply to spatial DPD (SDPD) models with short panels. In this paper, a unified Mestimation method is proposed for estimating the fixed-effects SDPD models containing three major types of spatial effects, namely spatial lag, spatial error and space-time lag. The method is free from the specification of the distribution of the initial observations and robust against nonnormality …

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