Econometrics Commons^™

Two Suggestions To Wp Mp Jamus Lim, Zhengxiao Wu

Research Collection School Of Economics

In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao discussed the arguments presented by Workers’ Party’s candidate Associate Professor Jamus Lim during the live broadcast of a political debate, and put forth two suggestions for Assoc Prof Lim.

In-Fill Asymptotic Theory For Structural Break Point In Autoregressions, Liang Jiang, Xiaohu Wang, Jun Yu

Research Collection School Of Economics

This article obtains the exact distribution of the maximum likelihood estimator of structural break point in the Ornstein-Uhlenbeck process when a continuous record is available. The exact distribution is asymmetric, tri-modal, dependent on the initial condition. These three properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in autoregressive (AR) models. Motivated by these observations, the article then develops an in-fill asymptotic theory for the LS estimator of structural break point in the AR(1) coefficient. The in-fill asymptotic distribution is also asymmetric, tri-modal, dependent on the initial condition, and delivers …

Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng

Research Collection School Of Economics

In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard …

Forecasting Singapore Gdp Using The Spf Data, Tian Xie, Jun Yu

Research Collection School Of Economics

In this article, we use econometric methods, machine learning methods, and a hybrid method to forecast the GDP growth rate in Singapore based on the Survey of Professional Forecasters (SPF). We compare the performance of these methods with the sample median used by the Monetary Authority of Singapore (MAS). It is shown that the relationship between the actual GDP growth rates and the forecasts from individual professionals is highly nonlinear and non-additive, making it hard for all linear methods and the sample median to perform well. It is found that the hybrid method performs the best, reducing the mean squared …

Realized Semicovariances, Tim Bollerslev, Jia Li, Andrew J. Patton, Rogier Quaedvlieg

Research Collection School Of Economics

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high-frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first-order asymptotic results highlight how the same-sign and mixed-sign components load differently on economic information related to stochastic correlation and jumps. The second-order asymptotic results reveal the structure underlying the same-sign semicovariances, as manifested in the form of co-drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross-section of individual stocks to empirically …

Essays On Time Series And Financial Econometrics, Yijie Fei

Dissertations and Theses Collection (Open Access)

This dissertation contains four essays in financial econometrics. In the first essay, some asymptotic results are derived for first-order autoregression with a root moderately deviating from unity and a nonzero drift. It is shown that the drift changes drastically the large sample properties of the least-squares (LS) estimator. The second essay is concerned with the joint test of predictability and stability in the context of predictive regression. The null hypothesis under investigation is that the potential predictors exhibit no predictability and incur no structural break during the sample period. We first show that the IVX estimator provides better finite sample …

Deviance Information Criterion For Latent Variable Models And Misspecified Models, Yong Li, Jun Yu, Tao Zeng

Research Collection School Of Economics

Deviance information criterion (DIC) has been widely used for Bayesian model comparison, especially after Markov chain Monte Carlo (MCMC) is used to estimate candidate models. This paper first studies the problem of using DIC to compare latent variable models when DIC is calculated from the conditional likelihood. In particular, it is shown that the conditional likelihood approach undermines theoretical underpinnings of DIC. A new version of DIC, namely DICL, is proposed to compare latent variable models. The large sample properties of DICL are studied. A frequentist justification of DICL is provided. Like AIC, DICL provides an asymptotically unbiased estimator to …

Identifying Latent Grouped Patterns In Conintegrated 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, Econometrica 84(6), 2215-2264) 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 …

Estimating Selection Models Without Instrument With Stata, Xavier D’Haultfœuille, Arnaud Maurel, Xiaoyun Qiu, Yichong Zhang

Research Collection School Of Economics

This article presents the eqregsel command for implementing the estimationand bootstrap inference of sample selection models via extremal quantile regression. The command estimates a semiparametric sample selection model withoutinstrument or large support regressor, and outputs the point estimates of the ho-mogenous linear coefficients, their bootstrap standard errors, as well as the p-valuefor a specification test.

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, Econometrica 84(6), 2215-2264) 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 …

Econometric Methods And Data Science Techniques: A Review Of Two Strands Of Literature And An Introduction To Hybrid Methods, Tian Xie, Jun Yu, Tao Zeng

Research Collection School Of Economics

The data market has been growing at an exceptional pace. Consequently, more sophisticated strategies to conduct economic forecasts have been introduced with machine learning techniques. Does machine learning pose a threat to conventional econometric methods in terms of forecasting? Moreover, does machine learning present great opportunities to cross-fertilize the field of econometric forecasting? In this report, we develop a pedagogical framework that identifies complementarity and bridges between the two strands of literature. Existing econometric methods and machine learning techniques for economic forecasting are reviewed and compared. The advantages and disadvantages of these two classes of methods are discussed. A class …

Detecting Latent Communities In Network Formation Models, Shujie Ma, Liangjun Su, Yichong Zhang

Research Collection School Of Economics

This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects. We model the coefficients of observed characteristics to have a latent community structure and the edge-wise fixed effects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number …

Asymptotic Theory For Near Integrated Processes Driven By Tempered Linear Processes, Farzad Sabzikar, Qiying Wang, Peter C. B. Phillips

Research Collection School Of Economics

In an early article on near-unit root autoregression, Ahtola and Tiao (1984) studied the behavior of the score function in a stationary first order autoregression driven by independent Gaussian innovations as the autoregressive coefficient approached unity from below. The present paper develops asymptotic theory for near-integrated random processes and associated regressions including the score function in more general settings where the errors are tempered linear processes. Tempered processes are stationary time series that have a semi-long memory property in the sense that the autocovariogram of the process resembles that of a long memory model for moderate lags but eventually diminishes …

Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu

Research Collection School Of Economics

This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and …

Essays On Nonstationary Econometrics, Yanbo Liu

Dissertations and Theses Collection (Open Access)

My dissertation consists of three essays that contribute new theoretical results to robust inference procedures and machine learning algorithms in nonstationary models.

Chapter 2 compares OLS and GLS in autoregressions with integrated noise terms. Grenander and Rosenblatt (2008) gave sufficient conditions for the asymptotic equivalence of GLS and OLS in deterministic trend extraction. However when extending to univariate autoregression model yt = ρnyt−1 + ut , ρn = 1 + c nα , ut = ut−1 + t , and t is one iid disturbance term with zero expectation and σ 2 variance, the asymptotic equivalence no longer holds. Under …

Essays On A Mechanism Design Approach To The Problem Of Bilateral Trade And Public Good Provision, Cuiling Zhang

Dissertations and Theses Collection (Open Access)

The dissertation consists of three chapters which studies a mechanism design approach to the problem of bilateral trade and public good provision.

Chapter 1 characterizes mechanisms satisfying Bayesian incentive compatibility (BIC) and interim individual rationality (IIR) in the classical public good provision problem. We propose a stress test for the results in the standard continuum type space by subject- ing them to a finite type space. The main contribution of this paper is to propose a set of techniques that allow us to characterize the efficient and optimal mechanisms in a discrete setup. Using these techniques, we conclude that many …

Three Essays On Nonstationary Time Series Econometrics, Yiu Lim Lui

Dissertations and Theses Collection (Open Access)

This dissertation comprises three papers that separately study different nonstationary time series models.

The first paper, titled as "The Grid Bootstrap for Continuous Time Models", is a joint work with Professor Jun Yu and Professor Weilin Xiao. It considers the grid bootstrap for constructing confidence intervals for the persistence parameter in a class of continuous-time models driven by a Lévy process. Its asymptotic validity is discussed under the assumption that the sampling interval (h) shrinks to zero, the time span (N) goes to infinity or both. Its improvement over the in-fill asymptotic theory is achieved by expanding the coefficient-based statistic …

Essays On Empirical Asset Pricing, Liyao Wang

Dissertations and Theses Collection (Open Access)

The dissertation consists of four chapters on empirical asset pricing. The first chapter reexamines the existence of time-series momentum. Time-series momentum (TSM) refers to the predictability of the past 12-month return on the next one month return. Using the same data set as Moskowitz, Ooi, and Pedersen (2012) (MOP, henceforth), we show that asset-by-asset time-series regressions reveal little evidence of TSM, both in- and out-of-sample. While the t -statistic in a pooled regression appears large, it is not statistically reliable as it is less than the critical values of parametric and nonparametric bootstraps. From an investment perspective, the performance of …

Robust Estimation And Inference Of Spatial Panel Data Models With Fixed Effects, Shew Fan Liu, Zhenlin Yang

Research Collection School Of Economics

It is well established that the quasi maximum likelihood (QML) estimation of the spatial regression models is generally inconsistent under unknown cross-sectional heteroskedasticity (CH) and the CH-robust methods have been developed. The same issue remains for the spatial panel data (SPD) models but the similar studies based on QML approach do not seem to have been carried out. This paper focuses on the SPD model with fixed effects (FE). We argue that under unknown CH the QML estimator for the SPD-FE model is inconsistent in general, but there are ‘special cases’ where it may remain consistent although the exact conditions …

Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression, Degui Li, Peter C. B. Phillips, Jiti Gao

Research Collection School Of Economics

This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of the data, we allow the regressors to be cointegrated and to embody a mixture of stochastic and deterministic trends, complications which result in asymptotic degeneracy of the kernel-weighted signal matrix. To address these complications new local and global rotation techniques are introduced to transform the covariate space to accommodate multiple scenarios of induced degeneracy. Under regularity conditions we derive asymptotic results that differ …

Estimation Of Fixed Effects Spatial Dynamic Panel Data Models With Small T And Unknown Heteroskedasticity, Liyao Li, Zhenlin Yang

Research Collection School Of Economics

We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtained by adjusting the conditional quasi-score functions given the initial observations, leading to M-estimators that are free from the initial conditions and robust against unknown cross-sectional heteroskedasticity. Consistency and asymptotic normality of the proposed M-estimator are established. The standard errors are obtained by representing the estimating equations as sums of martingale differences. Monte Carlo results show that the proposed …

Specification Tests For Temporal Heterogeneity In Spatial Panel Data Models With Fixed Effects, Yuhong Xu, Zhenlin Yang

Research Collection School Of Economics

We propose adjusted quasi score (AQS) 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 is treated in detail by first considering the model with individual fixed effects only, and then extending it to the model with both individual and time fixed effects. Two types of AQS tests (naïve and robust) are proposed, and their asymptotic properties are presented. These tests are then fully extended to SPD models with …

Hybrid Stochastic Local Unit Roots, Offer Lieberman, Peter C. B. Phillips

Research Collection School Of Economics

Two approaches have dominated formulations designed to capture small departures from unit root autoregressions. The first involves deterministic departures that include local-to-unity (LUR) and mildly (or moderately) integrated (MI) specifications where departures shrink to zero as the sample size n -> infinity. The second approach allows for stochastic departures from unity, leading to stochastic unit root (STUR) specifications. This paper introduces a hybrid local stochastic unit root (LSTUR) specification that has both LUR and STUR components and allows for endogeneity in the time varying coefficient that introduces structural elements to the autoregression. This hybrid model generates trajectories that, upon normalization, …

Corrigendum To "On Time-Varying Factor Models: Estimation And Testing" [J. Econometrics 198 (2017) 84-101], Liangjun Su, Xia Wang

Research Collection School Of Economics

We note that Su and Wang (2017, On Time-varying Factor Models: Estimation and Testing, Journal of Econometrics 198, 84-101) ignore the bias terms when estimating the time-varying factor models. In this note, we correct the theoretical results on the estimation of time-varying factor models. The asymptotic results for testing the correct specification of time invariant factor loadings are not affected.

Local Powers Of Least-Squares-Based Test For Panel Fractional Ornstein-Uhlenbeck Process, Katsuto Tanaka, Weilin Xiao, Jun Yu

Research Collection School Of Economics

Based on the least squares estimator, this paper proposes a novel method to test the sign of the persistence parameter in a panel fractional Ornstein-Uhlenbeck process with a known Hurst parameter H. Depending on H ∈ (1/2, 1), H = 1/2, or H ∈ (0, 1/2), three test statistics are considered. In the null hypothesis the persistence parameter is zero. Based on a panel of continuous record of observations, the null asymptotic distributions are obtained when T is fixed and N is assumed to go to infinity, where T is the time span of the sample and N is the …

Testing Alphas In Conditional Time-Varying Factor Models With High Dimensional Assets, Shujie Ma, Wei Lan, Liangjun Su, Chih-Ling Tsai

Research Collection School Of Economics

For conditional time-varying factor models with high dimensional assets, this article proposes a high dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model (Fama and French, 1993) is better than CAPM (Sharpe, 1964) …

Strong Consistency Of Spectral Clustering For Stochastic Block Models, Liangjun Su, Wuyi Wang, Yichong Zhang

Research Collection School Of Economics

In this paper we prove the strong consistency of several methods based on the spectral clustering techniques that are widely used to study the community detection problem in stochastic block models (SBMs). We show that under some weak conditions on the minimal degree, the number of communities, and the eigenvalues of the probability block matrix, the K-means algorithm applied to the eigenvectors of the graph Laplacian associated with its first few largest eigenvalues can classify all individuals into the true community uniformly correctly almost surely. Extensions to both regularized spectral clustering and degree-corrected SBMs are also considered. We illustrate the …

Model Selection For Explosive Models, Yubo Tao, Jun Yu

Research Collection School Of Economics

This chapter examines the limit properties of information criteria (such as AIC, BIC, and HQIC) for distinguishing between the unit-root (UR) model and the various kinds of explosive models. The explosive models include the local-to-unit-root model from the explosive side the mildly explosive (ME) model, and the regular explosive model. Initial conditions with different orders 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 UR model when data come from the UR model. When data come from the local-to-unit-root model from …

Finance And Ideology: The Firm-Level Channels, Hao Liang, Rong Wang, Haikun Zhu

Research Collection Lee Kong Chian School Of Business

We provide firm-level evidence on how politicians’ ideologies affect economic outcomes and financial development by exploring a unique setting of ideological discontinuity in China from Maoism to Dengism around 1978. We find the ideological exposure during a politician’s early adulthood has an enduring effect on contemporary firm and city policies. Firms governed by “Mao’s mayors” have more stakeholder spending, lower pay inequality, and less internationalization than those governed by Deng’s. Further evidence suggests politicians’ ideology may affect economic activities through channels other than economic policy. Selection bias, endogenous matching and mayor age effect are unlikely to drive our results.

Uniform Inference In Panel Autoregression, John C. Chao, Peter C. B. Phillips

Research Collection School Of Economics

This paper considers estimation and inference concerning the autoregressive coefficient (rho) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for rho that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson-Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when rho is near unity. But it is not so well known that …