Econometrics Commons^™

Cities In A Pandemic: Evidence From China, Badi H. Baltagi, Ying Deng, Li Jing, Zhenlin Yang

Research Collection School Of Economics

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on …

Hypothesis Testing Via Posterior-Test-Based Bayes Factors, Yong Li, Nianling Wang, Jun Yu, Yonghui Zhang

Research Collection School Of Economics

Hypothesis testing via p-value has been criticized in recent years. Bayes factors (BFs) have been tipped as a possible replacement of p-value for hypothesis testing. However, the standard BFs suffer from some theoretical and practical difficulties. For example, they are not well defined under improper priors and are subject to Jeffreys-Lindley-Bartlett’s paradox under vague priors. Moreover, they are difficult to compute for many models. In this paper, we propose to compare sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based BFs are constructed from the posterior version of the likelihood ratio test and the Wald test, respectively. Under …

Asymptotic Theory For Explosive Fractional Ornstein–Uhlenbeck Processes, Hui Jiang, Yajuan Pan, Weilin Liao, Qingshan Yang, Jun Yu

Research Collection School Of Economics

This paper proposes estimators for the parameters of an explosive fractional Ornstein-Uhlenbeck process. The asymptotic properties for the diffusion estimators are developed under the in-fill asymptotic scheme, while the asymptotic properties for the drift estimators are developed under the double asymptotic scheme for the full range of the Hurst parameter. Simulation results demonstrate the effectiveness of the proposed estimators, and the asymptotic distributions provide a good approximation in finite samples. Empirical applications are presented to demonstrate the model’s usefulness and the practical value of the asymptotic theory.

Fully Modified Least Squares Cointegrating Parameter Estimation In Multicointegrated Systems, Igor L. Kheifets, Peter C. B. Phillips

Research Collection School Of Economics

Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The semiparametric framework has the advantage that short run dynamics do not need to be modeled and estimation by standard techniques such as fully modified least squares (FM-OLS) on the original system is straightforward. The paper derives FM-OLS …

Modeling And Forecasting Realized Volatility With The Fractional Ornstein-Uhlenbeck Process, Xiaohu Wang, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper proposes to model and forecast realized volatility (RV) using the fractional Ornstein-Uhlenbeck (fO-U) process with a general Hurst parameter, H. A two-stage method is introduced for estimating parameters in the fO-U process based on discrete-sampled observations. In the first stage, H is estimated based on the ratio of two second-order differences of observations from different frequencies. In the second stage, with the estimated , the other parameters of the model are estimated by the method of moments. All estimators have closed-form expressions and are easy to implement. A large sample theory of the proposed estimators is derived. Extensive …

Covariate Adjustment In Experiments With Matched Pairs, Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem M. Shaikh, Yichong Zhang

Research Collection School Of Economics

This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to “matched pairs” and it is additionally desired to adjust for observed, baseline covariates to gain further precision. By a “matched pairs” design, we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Importantly, we presume that not all observed, baseline covariates are used in determining treatment assignment. We study a broad class of estimators based on a “doubly robust” moment …

Diagnosing Housing Fever With An Econometric Thermometer, Shuping Shi, Peter C. B. Phillips

Research Collection School Of Economics

Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the U.S. housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative “thermometer” for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long-term damage to the real economy. This paper provides a selective …

When Bias Contributes To Variance: True Limit Theory In Functional Coefficient Cointegrating Regression, Peter C. B. Phillips, Ying Wang

Research Collection School Of Economics

Limit distribution theory in the econometric literature for functional coefficient cointegrating regression is incorrect in important ways, influencing rates of convergence, distributional properties, and practical work. The correct limit theory reveals that components from both bias and variance terms contribute to variability in the asymptotics. The errors in the literature arise because random variability in the bias term has been neglected in earlier research. In stationary regression this random variability is of smaller order and can be ignored in asymptotic analysis but not without consequences for finite sample performance. Implications of the findings for rate efficient estimation are discussed. Simulations …

Permutation-Based Tests For Discontinuities In Event Studies, Federico Bugni, Jia Li, Qiyuan Li

Research Collection School Of Economics

We propose using a permutation test to detect discontinuities in an underlying economic model at a cutoff point. Relative to the existing literature, we show that this test is well suited for event studies based on time-series data. The test statistic measures the distance between the empirical distribution functions of observed data in two local subsamples on the two sides of the cutoff. Critical values are computed via a standard permutation algorithm. Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing …

Can Digital Finance Promote Low-Carbon Transition? Evidence From China, Xing Ge, Tomoki Fujii

Research Collection School Of Economics

Using panel data of Chinese cities from 2011 to 2019, this paper analyzes the impact of digital finance on low-carbon transition derived from a super-efficiency slacks-based measure data envelopment analysis. We find that digital finance promotes low-carbon transition, and this finding is robust with respect to the choice of sample, potential presence of measurement issue, choice of study period, presence of other policies, and potential endogeneity, among others. This impact is at least in part goes through increased green innovations. We also find evidence for impact heterogeneity across locations and by the level of low-carbon transition.

Conditional Evaluation Of Predictive Models: The Cspa Command, Jia Li, Zhipeng Liao, Rogier Quaedvlieg, Wenyu Zhou

Research Collection School Of Economics

In this article, we introduce a new command, cspa, that implements the conditional superior predictive ability test developed in Li, Liao, and Quaedvlieg (2022, Review of Economic Studies 89: 843–875). With the conditional performance of predictive methods measured nonparametrically by the conditional expectation functions of their predictive losses, we test the null hypothesis that a benchmark model weakly outperforms a collection of competitors uniformly across the conditioning space. The proposed command can implement this test for both independent cross-sectional data and serially dependent time-series data. Confidence sets for the most superior model can be obtained by inverting the test, for …

A Mixture Autoregressive Model Based On Student’S T–Distribution, Mika Meitz, Daniel Preve, Pentti Saikkonen

Research Collection School Of Economics

A new mixture autoregressive model based on Student’s t–distribution is proposed. A key feature of our model is that the conditional t–distributions of the component models are based on autoregressions that have multivariate t–distributions as their (low-dimensional) stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional mean of each component model is a linear function of past observations and the conditional variance is also time-varying. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has …

Bubble Testing Under Polynomial Trends, Xiaohu Wang, Jun Yu

Research Collection School Of Economics

This paper develops the asymptotic theory of the least squares estimator of the autoregressive (AR) coefficient in an AR(1) regression with intercept when data is generated from a polynomial trend model in different forms. It is shown that the commonly used right-tailed unit root tests tend to favor the explosive alternative. A new procedure, which implements the right-tailed unit root tests in an AR(2) regression, is proposed. It is shown that when the data generating process has a polynomial trend, the test statistics based on the new procedure cannot find evidence of explosiveness. Whereas, when the data generating process is …

School Attendance Information Or Conditional Cash Transfer? Evidence From A Randomized Field Experiment In Rural Bangladesh, Tomoki Fujii, Christine Ho, Rohan Ray, Abu S. Shonchoy

Research Collection School Of Economics

Low school attendance remains an important challenge in resource-poor settings with cash and information constraints. We compare conditional cash transfer (CCT) treatments with framing variations (gain and loss) against attendance information treatment as interventions to address these constraints in a unified framework. Our randomized evaluation shows CCT treatments increase attendance by 11 percentage points, about half of which is attributable to attendance information. These treatments improve girls’ academic aspirations and reduce early marriage. Daily CCT set at a quarter of local child wage maximizes attendance impact. We highlight the importance of low-cost information technology to boost attendance sustainably and cost-effectively.

Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu

Research Collection School Of Economics

The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1, d, 0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d

Finite Sample Comparison Of Alternative Estimators For Fractional Gaussian Noise, Shuping Shi, Jun Yu, Chen Zhang

Research Collection School Of Economics

The fractional Brownian motion (fBm) process is a continuous-time Gaussian process with its increment being the fractional Gaussian noise (fGn). It has enjoyed widespread empirical applications across many fields, from science to economics and finance. The dynamics of fBm and fGn are governed by a fractional parameter H ∈ (0, 1). This paper first derives an analytical expression for the spectral density of fGn and investigates the accuracy of various approximation methods for the spectral density. Next, we conduct an extensive Monte Carlo study comparing the finite sample performance and computational cost of alternative estimation methods for H under the …

On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu

Research Collection School Of Economics

This paper examines the performance of alternative forecasting formulae with the fractional Brownian motion based on a discrete and finite sample. One formula gives the optimal forecast when a continuous record over the infinite past is available. Another formula gives the optimal forecast when a continuous record over the finite past is available. Alternative discretiza-tion schemes are proposed to approximate these formulae. These alternative discretization schemes are then compared with the conditional expectation of the target variable on the vector of the discrete and finite sample. It is shown that the conditional expectation delivers more accurate forecasts than the discretization-based …

Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …

Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang

Research Collection School Of Economics

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope …

Bayesian Methods In Economics And Finance: Editor's Introduction, Jun Yu

Research Collection School Of Economics

Modern days, Bayesian methods have gained prominence in theoretical work and applications in economics and finance due to the rapid development of computational technologies and their ability to learn. The special issue intends to examine central aspects in Bayesian analysis and applications, including prior choices, model selection with massive data and latent variables, hypothesis testing, Bayesian learning. In total, this special issue contains ten papers, all subject to the Journal of Econometrics (JOE)’s normal refereeing process. Most of these papers came from a conference held at the ESSEC Singapore campus on 10 December 2018.

Posterior-Based Wald-Type Statistic For Hypothesis Testing, Xiaobin Liu, Yong Li, Jun Yu, Tao Zeng

Research Collection School Of Economics

A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as MCMC output) is available, …

Efficient Bilateral Trade Via Two-Stage Mechanisms: Comparison Between One-Sided And Two-Sided Asymmetric Information Environments, Takashi Kunimoto, Cuiling Zhang

Research Collection School Of Economics

This paper first considers a bilateral-trade model with one-sided asymmetric information in which one agent (seller) initially owns an indivisible object and is fully informed of its value, while the other agent (buyer) intends to obtain the object whose value is unknown to himself. As no mechanisms can generally result in efficient, voluntary bilateral trades (Jehiel and Pauzner, 2006), we aim to overturn this impossibility result by employing two-stage mechanisms (Mezzetti, 2004) in which first, the outcome (e.g., allocation of the goods) is determined, then the agents observe their own outcome-decision payoffs, and finally, transfers are made. We show that …

Externalities In The Housing Market And Agglomeration Economies, Yifan Wu

Dissertations and Theses Collection (Open Access)

This thesis studies the externalities in the housing market and agglomeration economies. While knowledge-based externalities, or knowledge spillovers are one of the most important micro-foundations of agglomeration economies, the first chapter studies how knowledge spillovers from universities affect local innovation activities. In the second chapter, we propose a high-order spatiotemporal autoregression approach to study the externalities in the housing market. The third chapter studies another important but under explored aspect of the agglomeration economies – the role that marriage market plays in providing incentives to promote urbanization, along with the unique feminization phenomenon during this process.

The first chapter studies …

Spatial Panel Data Models: Unbalanced Panel, Threshold Effect And Network Structure, Xiaoyu Meng

Dissertations and Theses Collection (Open Access)

This thesis studies the estimation and inference problems for spatial panel data models when the panels are unbalanced, when the panels contain threshold effects, or when the panels contain time-varying network structures. These three scenarios divide the thesis naturally into three chapters.

The first chapter considers estimation and inferences for fixed effects spatial panel data models based on unbalanced panels that result from randomly missing spatial units. The unbalanced nature of the panel data renders the standard method of estimation inapplicable. In this chapter, we proposed an M-estimation method where the estimating functions are obtained by adjusting the concentrated quasi …

Bayesian And Machine Learning Methods With Applications In Asset Pricing, Yaohan Chen

Dissertations and Theses Collection (Open Access)

The dissertation consists of three essays on asset pricing by constructing new data set and developing new methodologies. In the first chapter, we conduct empirical studies on the volatility-managed portfolios in the Chinese stock market. Using data from the Chinese stock market, we have found that the main empirical findings in Moreira and Muir (2017) break down. Based on the empirical findings, we exploit a comprehensive set of $99$ equity strategies in the Chinese stock market to analyze the value of managed portfolios. Based on these $99$ equity trading strategies, we find that there exists no systematic gain from scaling …

Essays On Long Memory Time Series And Panel Models, Shuyao Ke

Dissertations and Theses Collection (Open Access)

This dissertation studies different long memory models. The first chapter considers a time series regression model where both the regressors and error term are locally stationary long memory processes with time-varying memory parameters, and the regression coefficients are also allowed to be time-varying. We consider a frequency-domain least squares estimator with kernelized discrete Fourier transform and derive its pointwise asymptotic normality and uniform consistency. A specification test on the constancy of coefficients is provided. The second chapter studies a linear regression panel data model with interactive fixed effects where the regressors, factors and idiosyncratic error terms are all stationary but …

A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton

Research Collection School Of Economics

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value-at-Risk (VaR). We use the proposed new test to study the VaR …

Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root. We develop a data-driven semiparametric and identification-robust approach to inference that reveals such ambiguities and documents the prevalence of weak identification in many realized volatility and trading volume series. The identification-robust empirical evidence generally favors long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.

Win: How Public Entrepreneurship Can Transform The Developing World, Tomoki Fujii

Research Collection School Of Economics

This book provides a story about the Infrastructure Development Company Limited (IDCOL) written from the perspective of its first full-time Chief Executive Officer. For those who have never heard of IDCOL, it was created in 1997 by the Government of Bangladesh as a nonbank financial institution to fill the financial gap for developing medium- to large-scale infrastructure. IDCOL had a very modest start with a nominal paid-up capital of less than US$2,000, but its capital, equity, and reserves increased to US$110 million by 2020. During this massive expansion, IDCOL met various challenges. This book gives an account of these challenges …

Variation And Efficiency Of High-Frequency Betas, Congshan Zhang, Jia Li, Viktor Todorov, George Tauchen

Research Collection School Of Economics

This paper studies the efficient estimation of betas from high-frequency return data on a fixed time interval. Under an assumption of equal diffusive and jump betas, we derive the semiparametric efficiency bound for estimating the common beta and develop an adaptive estimator that attains the efficiency bound. We further propose a Hausman type test for deciding whether the common beta assumption is true from the high-frequency data. In our empirical analysis we provide examples of stocks and time periods for which a common market beta assumption appears true and ones for which this is not the case. We further quantify …