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Articles 1 - 24 of 24
Full-Text Articles in Econometrics
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a nonparametric test for general functional inequalities that include conditional moment inequalities as a special case. It is shown that the test controls size uniformly over a large class of distributions for observed data, importantly allowing for general forms of time series dependence. New results on uniform growing dimensional Gaussian coupling for general mixingale processes are developed for this purpose, which readily accommodate most applications in economics and finance. The proposed method is applied in a portfolio evaluation context to test for “all-weather” portfolios with uniformly superior conditional Sharpe ratio functions.
Finite Sample Comparison Of Alternative Estimators For Fractional Gaussian Noise, Shuping Shi, Jun Yu, Chen Zhang
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 …
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
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 …
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu
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 …
Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang
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
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
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, …
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Research Collection School Of Economics
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns' stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix, and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulting from the Federal Reserve's monetary policy shocks prior to the 2007-2009 financial crisis. The dimensionality of policy shocks increased afterwards due to the use of unconventional monetary policy tools.
A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
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
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
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
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 …
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 article proposes the new grid bootstrap to construct confidence intervals (CI) for the persistence parameter in a class of continuous-time models. It is different from the standard grid bootstrap of Hansen in dealing with the initial condition. The asymptotic validity of the CI is discussed under the in-fill scheme. The modified grid bootstrap leads to uniform inferences on the persistence parameter. Its improvement over in-fill asymptotics is achieved by expanding the coefficient-based statistic around its in-fill asymptotic distribution that is non-pivotal and depends on the initial condition. Monte Carlo studies show that the modified grid bootstrap performs better than …
A Posterior-Based Wald-Type Statistic For Hypothesis Testing, Yong Li, Xiaobin Liu, Tao Zeng, Jun Yu
A Posterior-Based Wald-Type Statistic For Hypothesis Testing, Yong Li, Xiaobin Liu, Tao Zeng, Jun Yu
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 χ2" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">χ2 …
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Research Collection School Of Economics
This paper studies the nonparametric estimation of occupation densities for semimartingale processes observed with noise. As leading examples we consider the stochastic volatility of a latent efficient price process, the volatility of the latent noise that separates the efficient price from the actually observed price, and nonlinear transformations of these processes. Our estimation methods are decidedly nonparametric and consist of two steps: the estimation of the spot price and noise volatility processes based on pre-averaging techniques and in-fill asymptotic arguments, followed by a kernel-type estimation of the occupation densities. Our spot volatility estimates attain the optimal rate of convergence, and …
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Research Collection School Of Economics
This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method …
Nonignorable Missing Data, Single Index Propensity Score And Profile Synthetic Distribution Function, Xuerong Chen, Denis H. Y. Leung, Jing Qin
Nonignorable Missing Data, Single Index Propensity Score And Profile Synthetic Distribution Function, Xuerong Chen, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favorably with existing methods.
A Panel Clustering Approach To Analyzing Bubble Behavior, Yanbo Liu, Peter C. B. Phillips, Jun Yu
A Panel Clustering Approach To Analyzing Bubble Behavior, Yanbo Liu, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This study provides new mechanisms for identifying and estimating explosive bubbles in mixed-root panel autoregressions with a latent group structure. A post-clustering approach is employed that combines a recursive k-means clustering al-gorithm with panel-data test statistics for testing the presence of explosive roots in time series trajectories. Uniform consistency of the k-means clustering algorithm is established, showing that the post-clustering estimate is asymptotically equivalent to the oracle counterpart that uses the true group identities. Based on the estimated group membership, right-tailed self-normalized t-tests and coefficient-based J-tests, each with pivotal limit distributions, are introduced to detect the explosive roots. The usual …
Permutation-Based Tests For Discontinuities In Event Studies, Federico Bugni, Jia Li
Permutation-Based Tests For Discontinuities In Event Studies, Federico Bugni, Jia 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 …
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi. Zhang
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi. Zhang
Research Collection School Of Economics
Academic research on nonparametric “spot” volatility inference often relies on high-quality transaction data that are not available to an average investor. Most investors, however, have free access to intraday candlestick charts through their online trading applications. Based on such data, we propose an Optimal candlesticK (OK) estimator for the spot volatility at a given time point. Under a standard infill asymptotic setting for Itˆo semimartingale price process, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. In addition, its estimation error can be coupled by a Brownian functional, whose …
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
In this article, we introduce a command, xtnpsreg, that implements a uniform nonparametric inference procedure for possibly unbalanced panel datasets with general forms of spatio-temporal dependence. We demonstrate how to apply this command in several use cases, including (i) the nonparametric estimation of conditional mean function and its marginal response; (ii) the construction of uniform confidence bands for these nonparametric functional parameters; (iii) specification tests for parametric model restrictions; and (iv) the estimation and uniform inference for functional coefficients in semi-nonparametric models.
Forecasting Equity Index Volatility By Measuring The Linkage Among Component Stocks, Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou
Forecasting Equity Index Volatility By Measuring The Linkage Among Component Stocks, Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou
Research Collection School Of Economics
The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises …
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a new test for conditional moment restrictions via nonparametric series regression, with approximating series terms selected by Lasso. Machine-learning the main features of the unknown conditional expectation function beforehand enables the test to seek power in a targeted fashion. The data-driven selection, however, also tends to distort the test’s size nontrivially, because it restricts the (growing-dimensional) score vector in the series regression on a random polytope, and hence, effectively alters the score’s asymptotic normality. A novel critical value is proposed to account for this truncation effect. We establish the size and local power properties of the proposed …
Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
Research Collection School Of Economics
We study how to improve efficiency via regression adjustments with additional covariates under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We first establish the semiparametric efficiency bound for the local average treatment effect (LATE) under CARs. Second, we develop a general regression-adjusted LATE estimator which allows for parametric, nonparametric, and regularized adjustments. Even when the adjustments are misspecified, our proposed estimator is still consistent and asymptotically normal, and their inference method still achieves the exact asymptotic size under the null. When the adjustments are correctly specified, our estimator achieves the semiparametric efficiency bound. Third, we derive the optimal linear …