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- Panel data (5)
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Articles 1 - 30 of 34
Full-Text Articles in Social and Behavioral Sciences
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhai Qian, Liangjun Su
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhai Qian, Liangjun Su
Research Collection School Of Economics
In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our method can correctly determine the unknown number of breaks, and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method …
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …
Weak Convergence To Stochastic Integrals For Econometric Applications, Hanying Liang, Peter C. B. Phillips, Hanchao Wang, Qiying Wang
Weak Convergence To Stochastic Integrals For Econometric Applications, Hanying Liang, Peter C. B. Phillips, Hanchao Wang, Qiying Wang
Research Collection School Of Economics
Limit theory involving stochastic integrals is now widespread in time series econometrics and relies on a few key results on functional weak convergence. In establishing such convergence, the literature commonly uses martingale and semimartingale structures. While these structures have wide relevance, many applications involve a cointegration framework where endogeneity and nonlinearity play major roles and complicate the limit theory. This paper explores weak convergence limit theory to stochastic integral functionals in such settings. We use a novel decomposition of sample covariances of functions of I (1) and I (0) time series that simplifies the asymptotics and our limit results for …
Bias Correction And Refined Inferences For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
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 …
Asymptotically Refined Score And Gof Tests For Inverse Gaussian Models, Anthony F. Desmond, Zhenlin Yang
Asymptotically Refined Score And Gof Tests For Inverse Gaussian Models, Anthony F. Desmond, Zhenlin Yang
Research Collection School Of Economics
The score test and the GOF test for the inverse Gaussian distribution, in particular the latter, are known to have large size distortion and hence unreliable power when referring to the asymptotic critical values. We show in this paper that with the appropriately bootstrapped critical values, these tests become second-order accurate, with size distortion being essentially eliminated and power more reliable. Two major generalizations of the score test are made: one is to allow the data to be right-censored, and the other is to allow the existence of covariate effects. A data mapping method is introduced for the bootstrap to …
Shrinkage Estimation Of Covariance Matrix For Portfolio Choice With High Frequency Data, Cheng Liu, Ningning Xia, Jun Yu
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 …
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 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 …
Homogeneity Pursuit In Panel Data Models: Theory And Applications, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
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 …
Semiparametric Single Index Panel Data Models With Interactive Fixed Effects: Theory And Practice, Guohua Feng, Bin Peng, Liangjun Su, Thomas Tao Yang
Semiparametric Single Index Panel Data Models With Interactive Fixed Effects: Theory And Practice, Guohua Feng, Bin Peng, Liangjun Su, Thomas Tao Yang
Research Collection School Of Economics
In this paper, we propose a single-index panel data model with unobserved multiple interactive fixed effects. This model has the advantages of being flexible and of being able to allow for common shocks and their heterogeneous impacts on cross sections, thus making it suitable for the investigation of many economic issues. We derive asymptotic theories for both the case where the link function is integrable and the case where the link function is non-integrable. Our Monte Carlo simulations show that our methodology works well for large N and T cases. In our empirical application, we illustrate our model by analyzing …
Robust Forecast Comparison, Sainan Jin, Valentina Corradi, Norman R. Swanson
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 …
Estimating The Volatility Occupation Time Via Regularized Laplace Inversion, Jia Li, Viktor Todorov, Tauchen
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 …
A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei
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 …
Asymptotic Theory For Estimating The Persistent Parameter In The Fractional Vasicek Model, Weilin Xiao, Jun Yu
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.
Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips
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 …
Modeling Speculative Bubbles With Diverse Investor Expectations, Peter C. B. Phillips
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 …
Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips
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 …
Double Asymptotics For Explosive Continuous Time Models, Xiaohu Wang, Jun Yu
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 …
Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang
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 …
Generalized Method Of Integrated Moments For High-Frequency Data, Jia Li, Dacheng Xiu
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 …
Robust Econometric Inference With Mixed Integrated And Mildly Explosive Regressors, Peter C. B. Phillips, Ji Hyung Lee
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 …
On Refined And Robust Inferences For Spatial Econometric Models, Shew Fan Liu
On Refined And Robust Inferences For Spatial Econometric Models, Shew Fan Liu
Dissertations and Theses Collection (Open Access)
Asymptotically refined and heteroskedasticity robust inferences are considered for spatial linear and panel regression models, based on the quasi maximum likelihood (QML) or the adjusted concentrated quasi score (ACQS) approaches. Refined inferences are achieved through bias correcting the QML estimators, bias correcting the t-ratios for covariate effects, and improving tests for spatial effects; heteroskedasticity-robust inferences are achieved through adjusting the quasi score functions. Several popular spatial linear and panel regression models are considered including the linear regression models with either spatial error dependence (SED), or spatial lag dependence (SLD), or both SED and SLD (SARAR), the linear regression models with …
Nonparametric Cointegrating Regression With Endogeneity And Long Memory, Qiying Wang, Peter C. B. Phillips
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 …
Disentangling Greenhouse Warming And Aerosol Cooling To Reveal Earth's Climate Sensitivity, T. Storelvmo, T. Leirvik, U. Lohmann, Peter C. B. Phillips, M. Wild
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 …
Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su
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 …
Model Selection For Explosive Models, Yubo Tao, Jun Yu
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 …
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino
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.
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
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 …
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
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 …
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju
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 …
Testing For Monotonicity In Unobservables Under Unconfoundedness, Stefan Hoderlein, Liangjun Su, Halbert White, Thomas Tao Yang
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 …