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Articles 211 - 240 of 771
Full-Text Articles in Econometrics
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Research Collection School Of Economics
We propose semiparametrically efficient estimators for general integrated volatility functionals of multivariate semimartingale processes. A plug-in method that uses nonparametric estimates of spot volatilities is known to induce high-order biases that need to be corrected to obey a central limit theorem. Such bias terms arise from boundary effects, the diffusive and jump movements of stochastic volatility and the sampling error from the nonparametric spot volatility estimation. We propose a novel jackknife method for bias correction. The jackknife estimator is simply formed as a linear combination of a few uncorrected estimators associated with different local window sizes used in the estimation …
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Research Collection School Of Economics
Non-response is a commonly encountered problem in many population-based surveys. Broadly speaking, non-response can be due to refusal or failure to contact the sample units. Although both types of non-response may lead to bias, there is much evidence to indicate that it is much easier to reduce the proportion of non-contacts than to do the same with refusals. In this article, we use data collected from a nationally-representative survey under the Demographic and Health Surveys program to study non-response due to refusals to HIV testing in Malawi. We review existing estimation methods and propose novel approaches to the estimation of …
Asymptotic Theory For Estimating Drift Parameters In The Fractional Vasicek Model, Weilin Xiao, Jun Yu
Asymptotic Theory For Estimating Drift Parameters In The Fractional Vasicek Model, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This article develops an asymptotic theory for estimators of two parameters in the drift function in the fractional Vasicek model when a continuous record of observations is available. The fractional Vasicek model with long-range dependence is assumed to be driven by a fractional Brownian motion with the Hurst parameter greater than or equal to one half. It is shown that, when the Hurst parameter is known, the asymptotic theory for the persistence parameter depends critically on its sign, corresponding asymptotically to the stationary case, the explosive case, and the null recurrent case. In all three cases, the least squares method …
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Liangjun Su, Ke Miao, Sainan Jin
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Liangjun Su, Ke Miao, Sainan Jin
Research Collection School Of Economics
We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross validation-based method to determine the number of factors in factor models with or without missing values and justify its consistency. Simulations demonstrate that our cross validation method is robust to fat tails in the error distribution and …
Econometric Estimates Of Earth's Transient Climate Sensitivity, Peter C. B. Phillips, Thomas Leirvik, Trude Storelvmo
Econometric Estimates Of Earth's Transient Climate Sensitivity, Peter C. B. Phillips, Thomas Leirvik, Trude Storelvmo
Research Collection School Of Economics
How sensitive is Earth's climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing question in climate science was recently analyzed by dynamic panel data methods using extensive spatio-temporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). Those methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides regularity conditions and asymptotic theory justifying the use of time series …
Specification Tests For Temporal Heterogeneity In Spatial Panel Models With Fixed Effects, Yuhong Xu, Zhenlin Yang
Specification Tests For Temporal Heterogeneity In Spatial Panel Models With Fixed Effects, Yuhong Xu, Zhenlin Yang
Research Collection School Of Economics
We propose score type tests for testing the existence of temporal heterogeneity in slope and spatial parameters in spatial panel data (SPD) models, allowing for the presence of individual-specific and/or time-specific fixed effects (or in general intercept heterogeneity). The SPD model with spatial lag effect is treated in detail by first considering the model with individual-specific effects only, and then extending it to the model with both individual and time specific effects. Two types of tests (naive and robust) are proposed, and their asymptotic properties are presented. These tests are then fully extended to an SPD model with both spatial …
Nonstationary Panel Models With Latent Group Structures And Cross-Section Dependence, Wenxin Huang, Sainan Jin, Peter C. B. Phillips, Liangjun Su
Nonstationary Panel Models With Latent Group Structures And Cross-Section Dependence, Wenxin Huang, Sainan Jin, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an asymptotically non-negligible bias from correlations between the nonstationary regressors and unobserved stationary common factors and/or the equation errors. To remedy these shortcomings we provide three bias-correction procedures under which the estimators are re-centered about zero as both …
Mild-Explosive And Local-To-Mild-Explosive Autoregressions With Serially Correlated Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu
Mild-Explosive And Local-To-Mild-Explosive Autoregressions With Serially Correlated Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper firstly extends the results of Phillips and Magdalinos (2007a) by allowing for anti-persistent errors in mildly explosive autoregressive models. It is shown that the Cauchy asymptotic theory remains valid for the least squares (LS) estimator. The paper then extends the results of Phillips, Magdalinos and Giraitis (2010) by allowing for serially correlated errors of various forms in local-to-mild-explosive autoregressive models. It is shown that the result of smooth transition in the limit theory between local-to-unity and mild-explosiveness remains valid for the LS estimator. Finally, the limit theory for autoregression with intercept is developed.
Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions, Lili Tan, Yichong Zhang
Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions, Lili Tan, Yichong Zhang
Research Collection School Of Economics
The intercept of the binary response model is irregularly identified when the supports of both the special regressor V and the error term ε are the whole real line. This leads to the estimator of the intercept having potentially a slower than √n convergence rate, which can result in a large estimation error in practice. This paper imposes addition tail restrictions which guarantee the regular identification of the intercept and thus the √n-consistency of its estimator. We then propose an estimator that achieves the √n rate. Finally, we extend our tail restrictions to a full-blown model with endogenous regressors.
Semiparametric Maximum Likelihood Inference For Nonignorable Nonresponse With Callbacks, Zhong Guan, Denis H. Y. Leung, Jing Qin
Semiparametric Maximum Likelihood Inference For Nonignorable Nonresponse With Callbacks, Zhong Guan, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
We model the nonresponse probabilities as logistic functions ofthe outcome variable and other covariates in the survey sampling study withcallback. The identification aspect of this callback model is investigated. Semiparametricmaximum likelihood estimators of the parameters in the responseprobabilities are proposed and studied. As a result, an efficient estimator ofthe mean of the outcome variable is constructed using the estimated responseprobabilities. Moreover, if a regression model for conditional mean of the outcomevariable given some covariate is available, then we can obtain an evenmore efficient estimate of the mean of the outcome variable by fitting the regressionmodel using an adjusted least squares …
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Research Collection School Of Economics
This paper studies the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose three estimation methods: (1) the simple quantile regression (QR), (2) the QR with strata fixed effects, and (3) the inverse propensity score weighted QR. For the three estimators, we derive their asymptotic distributions uniformly over a set of quantile indexes and show that the estimator obtained from inverse propensity score weighted QR weakly dominates the other two in terms of efficiency, for a wide range of randomization schemes. For inference, we show that the weighted bootstrap tends to be conservative for methods (1) …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a factor correlation matrix approach to forecast large covariance matrix of asset returns using high-frequency data. We apply shrinkage method to estimate large correlation matrix and adopt principal component method to model the underlying latent factors. A vector autoregressive model is used to forecast the latent factors and hence the large correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. We conduct Monte Carlo studies to compare the finite sample performance of several methods of forecasting large covariance matrix. Our proposed …
Threshold Regression Asymptotics: From The Compound Poisson Process To Two-Sided Brownian Motion, Ping Yu, Peter C. B. Phillips
Threshold Regression Asymptotics: From The Compound Poisson Process To Two-Sided Brownian Motion, Ping Yu, Peter C. B. Phillips
Research Collection School Of Economics
The asymptotic distribution of the least squares estimator in threshold regression is expressed in terms of a compound Poisson process when the threshold effect is fixed and as a functional of two-sided Brownian motion when the threshold effect shrinks to zero. This paper explains the relationship between this dual limit theory by showing how the asymptotic forms are linked in terms of joint and sequential limits. In one case, joint asymptotics apply when both the sample size diverges and the threshold effect shrinks to zero, whereas sequential asymptotics operate in the other case in which the sample size diverges first …
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Research Collection School Of Economics
We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips’ (2016) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals’ group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of both the C-Lasso …
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper considers the grid bootstrap for constructing confidence intervals for the persistence parameter in a class of continuous time models driven by a Levy process. Its asymptotic validity is established by assuming the sampling interval (h) shrinks to zero. Its improvement over the in-fill asymptotic theory is achieved by expanding the coefficient-based statistic around its in fill asymptotic distribution which is non-pivotal and depends on the initial condition. Monte Carlo studies show that the gird bootstrap method performs better than the in-fill asymptotic theory and much better than the long-span theory. Empirical applications to U.S. interest rate data highlight …
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
Two test statistics are proposed to determine model specification after a model is estimated by an MCMC method. The first test is the MCMC version of IOSA test and its asymptotic null distribution is normal. The second test is motivated from the power enhancement technique of Fan et al. (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the first test. It is shown that J0 converges to zero when the null model is correctly specified and diverges when the null model is misspecified. Also …
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 loadingsexhibit 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 interval. …
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
Research Collection School Of Economics
We examine the effect of investor attention spillover on stock return predictability. Using a novel measure, the News Network Triggered Attention index (NNTA), we find that NNTA negatively predicts market returns with a monthly in(out)-of-sample R-square of 5.97% (5.80%). In the cross-section, a long-short portfolio based on news co-occurrence generates a significant monthly alpha of 68 basis points. The results are robust to the inclusion of alternative attention proxies, sentiment measures, other news- and information-based predictors, across recession and expansion periods. We further validate the attention spillover effect by showing that news co-mentioning leads to greater increases in Google and …
Change Detection And The Causal Impact Of The Yield Curve, Shuping Shi, Peter C. B. Phillips, Stan Hurn
Change Detection And The Causal Impact Of The Yield Curve, Shuping Shi, Peter C. B. Phillips, Stan Hurn
Research Collection School Of Economics
Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an …
Identifying Latent Grouped Patterns In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Gaosheng Ju
Identifying Latent Grouped Patterns In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Gaosheng Ju
Research Collection School Of Economics
We consider the estimation of latent grouped patterns in dynamic panel data models with interactive fixed effects. We assume that the individual slope coefficients are homogeneous within a group and heterogeneous across groups but each individual’s group membership is unknown to the researcher. We consider penalized principal component (PPC) estimation by extending the penalized-profile-likelihood-based C-Lasso of Su, Shi, and Phillips (2016) to panel data models with cross section dependence. Given the correct number of groups, we show that the C-Lasso can achieve simultaneous classification and estimation in a single step and exhibit the desirable property of uniform classification consistency. The …
A Frequentist Approach To Bayesian Asymptotics, Tingting Cheng, Jiti Gao, Peter C. B. Phillips
A Frequentist Approach To Bayesian Asymptotics, Tingting Cheng, Jiti Gao, Peter C. B. Phillips
Research Collection School Of Economics
Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature also shows that the posterior distribution is asymptotically normal when the sample size of the original data considered goes to infinity. To the best of our knowledge, there is little discussion on the large sample behaviour of the posterior mean. In this paper, we aim to fill this gap. In particular, we extend the posterior mean idea to the conditional mean case, which is conditioning on a given vector of summary statistics of the original data. We …
Volume, Volatility, And Public News Announcements, Tim Bollerslev, Jia Li, Yuan Xue
Volume, Volatility, And Public News Announcements, Tim Bollerslev, Jia Li, Yuan Xue
Research Collection School Of Economics
We provide new empirical evidence for the way in which financial markets process information. Our results rely critically on high-frequency intraday price and volume data for the S&P 500 equity portfolio and U.S. Treasury bonds, along with new econometric techniques, for making inference on the relationship between trading intensity and spot volatility around public news announcements. Consistent with the predictions derived from a theoretical model in which investors agree to disagree, our estimates for the intraday volume-volatility elasticity around important news announcements are systematically below unity. Our elasticity estimates also decrease significantly with measures of disagreements in beliefs, economic uncertainty, …
Editorial For The Special Issue Entitled: New Advances In Spatial Econometrics: Interactions Matter, Nicolas Debarsy, Zhenlin Yang
Editorial For The Special Issue Entitled: New Advances In Spatial Econometrics: Interactions Matter, Nicolas Debarsy, Zhenlin Yang
Research Collection School Of Economics
This Regional Science and Urban Economics special issue collects together a subset of contributions presented in the 15th edition of the International Workshop in Spatial Econometrics and Statistics, which was organized by the Department of Economics of the University of Orléans (Laboratoire d’Economie d’Orléans – UMR CNRS 7322) on May 26-27 2016
Homogeneity Pursuit In Panel Data Models: Theory And Application, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
Homogeneity Pursuit In Panel Data Models: Theory And Application, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being 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. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.
Aging Suppresses Skin-Derived Circulating Sdf1 To Promote Full-Thickness Tissue Regeneration, Mailyn A. Nishiguchi, Casey A. Spencer, Denis H. Y. Leung, Thomas H. Leung
Aging Suppresses Skin-Derived Circulating Sdf1 To Promote Full-Thickness Tissue Regeneration, Mailyn A. Nishiguchi, Casey A. Spencer, Denis H. Y. Leung, Thomas H. Leung
Research Collection School Of Economics
Physicians have observed that surgical wounds in the elderly heal with thinner scars than wounds in young patients. Understanding this phenomenon may reveal strategies for promoting scarless wound repair. We show that full-thickness skin wounds in aged but not young mice fully regenerate. Exposure of aged animals to blood from young mice by parabiosis counteracts this regenerative capacity. The secreted factor, stromal-derived factor 1 (SDF1), is expressed at higher levels in wounded skin of young mice. Genetic deletion of SDF1 in young skin enhanced tissue regeneration. In aged mice, enhancer of zeste homolog 2 (EZH2) and histone H3 lysine 27 …
Comment On: Limit Of Random Measures Associated With The Increments Of A Brownian Semimartingale, Jia Li, Dacheng Xiu
Comment On: Limit Of Random Measures Associated With The Increments Of A Brownian Semimartingale, Jia Li, Dacheng Xiu
Research Collection School Of Economics
We thank the Editors’ invitation for the opportunity of contributing to this special issue as a celebration of Professor Jean Jacod’s seminal work originally written in 1994 (Jacod, 1994). This paper established general limit theorems for integrated volatility functionals, and provided theoretical tools that eventually changed the landscape of theoretical research concerning high-frequency data. This impact is also largely due to Professor Jacod’s continuous contribution to a broad variety of challenging issues in the area of high-frequency financial econometrics, including volatility estimation, jumps, and microstructure noise, as well as a large body of mathematical results collected in Jacod and Shiryaev …
Spatial Dynamic Panel Data Models With Correlated Random Effects, Liyao Li, Zhenlin Yang
Spatial Dynamic Panel Data Models With Correlated Random Effects, Liyao Li, Zhenlin Yang
Research Collection School Of Economics
In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums …
Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang
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 …
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Research Collection School Of Economics
This paper first extends the methodology of Yang (J Econom 185:33-59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the model and the model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order …
Financial Bubble Implosion And Reverse Regression, Peter C. B. Phillips, Shu-Ping Shi
Financial Bubble Implosion And Reverse Regression, Peter C. B. Phillips, Shu-Ping Shi
Research Collection School Of Economics
Expansion and collapse are two key features of a financial asset bubble. Bubble expansionmay be modeled using a mildly explosive process. Bubble implosion may take several differentforms depending on the nature of the collapse and therefore requires some flexibility in modeling.This paper first strengthens the theoretical foundation of the real time bubble monitoringstrategy proposed in Phillips, Shi and Yu (2015a,b, PSY) by developing analytics and studyingthe performance characteristics of the testing algorithm under alternative forms of bubbleimplosion which capture various return paths to market normalcy. Second, we propose a newreverse sample use of the PSY procedure for detecting crises and …