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Articles 1 - 27 of 27
Full-Text Articles in Economic Theory
Equal Predictive Ability Tests Based On Panel Data With Applications To Oecd And Imf Forecasts, Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang
Equal Predictive Ability Tests Based On Panel Data With Applications To Oecd And Imf Forecasts, Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang
Research Collection School Of Economics
We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, …
High-Dimensional Iv Cointegration Estimation And Inference, Peter C. B. Phillips, Igor L. Kheifets
High-Dimensional Iv Cointegration Estimation And Inference, Peter C. B. Phillips, Igor L. Kheifets
Research Collection School Of Economics
A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those …
Connecting The (Dirty) Dots: Current Account Surplus And Polluting Production, Jungho Lee, Shang-Jin Wei, Jianhuan Xu
Connecting The (Dirty) Dots: Current Account Surplus And Polluting Production, Jungho Lee, Shang-Jin Wei, Jianhuan Xu
Research Collection School Of Economics
According to the existing open-economy macroeconomics literature, a current account surplus is associated with a welfare loss only when distortions exist in either savings or investment. We propose a new welfare effect even in the absence of such distortions. In our theory, a trade imbalance − the largest component of a current account imbalance − interacts with a country’s pollution control (“cleanness”) regime to generate welfare effects outside the standard channels. In particular, a trade surplus alters the shipping costs and composition of a country’s imports, producing a welfare loss associated with greater pollution.
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.
Non-Separable Models With High-Dimensional Data, Liangjun Su, T Ura, Yc Zhang
Non-Separable Models With High-Dimensional Data, Liangjun Su, T Ura, Yc Zhang
Research Collection School Of Economics
This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. In the first stage we estimate the conditional mean, distribution, and density objects by penalized local least squares, penalized local maximum likelihood estimation, and numerical differentiation, respectively, where control variables are selected via a localized method of L-1-penalization at each value of the continuous treatment. In the second stage we estimate the average and marginal distribution of the potential …
Limit Theory For Moderate Deviation From Integrated Garch Processes, Yubo Tao
Limit Theory For Moderate Deviation From Integrated Garch Processes, Yubo Tao
Research Collection School Of Economics
This paper develops the limit theory of the GARCH(1,1) process that moderately deviates from IGARCH process towards both stationary and explosive regimes. The asymptotic theory extends Berkes et al. (2005) by allowing the parameters to have a slower rate of convergence. The results can be applied to unit root test for processes with mildly-integrated GARCH innovations (e.g. Boswijk (2001), Cavaliere and Taylor (2007, 2009)) and deriving limit theory of estimators for models involving mildly-integrated GARCH processes (e.g. Jensen and Rahbek (2004), Francq and Zakoïan (2012, 2013).
The Revealed Preference Theory Of Stable Matchings With One-Sided Preferences, Gaoji Hu, Jiangtao Li, Rui Tang
The Revealed Preference Theory Of Stable Matchings With One-Sided Preferences, Gaoji Hu, Jiangtao Li, Rui Tang
Research Collection School Of Economics
This note studies the testable implications of the theory of stable matchings intwo-sided matching markets with one-sided preferences. Our main result connects therevealed preference analysis to the well-known lattice structure of the set of stablematchings, and tests the rationalizability of a data set by analyzing the joins andmeets of matchings.
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 …
Robust Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Robust Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency. Unlike classical linear regressions, jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times. The latter are the continuous martingale parts of the processes as well as observation noise. By sampling more frequently the role of these components, which are hidden in the observed price, shrinks asymptotically. The robustness of our inference procedure is with respect to outliers, which are of particular …
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Research Collection School Of Economics
This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced …
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We study the market microstructure noise-variance estimation of high-frequency stock prices. Based on the Hansen and Lunde (2006) approach, we propose estimates using subsampling method at different time scales. We conduct a Monte Carlo study to compare our method against others in the literature. Our results show that our proposed estimates have lower (absolute) mean error and root mean-squared error, and their performance is quite stable at different time scales.
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the …
Model Selection For Explosive Models, Yubo Tao, Jun Yu
Model Selection For Explosive Models, Yubo Tao, Jun Yu
Research Collection School Of Economics
This paper examines the limit properties of information criteria for distinguishing between the unit root model and the various kinds of explosive models. The information criteria include AIC, BIC, HQIC. The explosive models include the local-to-unit-root model, the mildly explosive model and the regular explosive model. Initial conditions with different order of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the unit root model when data come from the unit root model. When data come from the local-to-unit-root model, both BIC and …
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture the anomaly effects of some asset-specific characteristics and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known "error-in-variable" problem associated with the commonly used …
Econometric Analysis Of Continuous Time Models: A Survey Of Peter Phillips' Work And Some New Results, Jun Yu
Research Collection School Of Economics
Econometric analysis of continuous time models has drawn the attention of Peter Phillips for 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and the associated econometric problems. He has investigated problems from univariate equations to systems of equations, from asymptotic theory to finite sample issues, from parametric models to nonparametric models, from identification problems to estimation and inference problems, from stationary models to nonstationary and nearly nonstationary models. This paper provides an overview of Peter Phillips' contributions in the continuous time econometrics literature. We review …
A State Space Model Approach To Integrated Covariance Matrix Estimation With High Frequency Data, Cheng Liu, Cheng Yong Tang
A State Space Model Approach To Integrated Covariance Matrix Estimation With High Frequency Data, Cheng Liu, Cheng Yong Tang
Research Collection Lee Kong Chian School Of Business
We consider a state space model approach forhigh frequency financial data analysis. An expectationmaximization(EM) algorithm is developed for estimatingthe integrated covariance matrix of the assets. The statespace model with the EM algorithm can handle noisy financialdata with correlated microstructure noises. Difficultydue to asynchronous and irregularly spaced trading data ofmultiple assets can be naturally overcome by consideringthe problem in a scenario with missing data. Since the statespace model approach requires no data synchronization, norecord in the financial data is deleted so that it efficientlyincorporates information from all observations. Empiricaldata analysis supports the general specification of the statespace model, and simulations confirm …
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We propose nonparametric estimators of the occupation measure and the occupation density of the diffusion coefficient (stochastic volatility) of a discretely observed Itô semimartingale on a fixed interval when the mesh of the observation grid shrinks to zero asymptotically. In a first step we estimate the volatility locally over blocks of shrinking length, and then in a second step we use these estimates to construct a sample analogue of the volatility occupation time and a kernel-based estimator of its density. We prove the consistency of our estimators and further derive bounds for their rates of convergence. We use these results …
Economic Indices: Managing By The Numbers, Singapore Management University
Economic Indices: Managing By The Numbers, Singapore Management University
Perspectives@SMU
Understanding indices is not just about crunching numbers, but appreciating how it is constructed
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Research Collection School Of Economics
An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the finite-sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the finite-sample distributions of the estimators uniformly well irrespective of the strength of the identification. An important technical component of …
Power Maximization And Size Control Of Heteroscedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control Of Heteroscedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (ρ). We show that the nonstandard fixed-ρ limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-ρ limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the nonstandard limit distribution is also second-order correct under the large-ρ asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central …
Corrigendum To "A Gaussian Approach For Continuous Time Models Of The Short Term Interest Rate", Peter C. B. Phillips, Jun Yu
Corrigendum To "A Gaussian Approach For Continuous Time Models Of The Short Term Interest Rate", Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
An error is corrected in Yu and Phillips (2001) (Econometrics Journal, 4, 210-224) where a time transformation was used to induce Gaussian disturbances in the discrete time equivalent model. It is shown that the error process in this model is not a martingale and the Dambis, Dubins-Schwarz (DDS) theorem is not directly applicable. However, a detrended error process is a martingale, the DDS theorem is applicable, and the corresponding stopping time correctly induces Gaussianity. We show that the two stopping time sequences differ by O(a2), where a is the pre-specified normalized timing constant.
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Research Collection School Of Economics
This paper overviews some recent advances on simulatio n-based methods of estimating time series models and asset pricing models that are widely used in finance. The simulation based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms and hence the maximum likelihood method (MLE) and the generalized method of moments (GMM) are difficult to use. They can also be useful for improving the finite sample performance of the traditional methods when financial time series are highly persistent and when the quantity of interest is a highly nonlinear function of system parameters.The …
A New Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
A New Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
Research Collection School Of Economics
A new posterior odds analysis is proposed to test for a unit root in volatility dynamics in the context of stochastic volatility models. This analysis extends the Bayesian unit root test of So and Li (1999, Journal of Business Economic Statistics) in two important ways. First, a numerically more stable algorithm is introduced to compute the Bayes factor, taking into account the special structure of the competing models. Owing to its numerical stability, the algorithm overcomes the problem of diverged “size” in the marginal likelihood approach. Second, to improve the “power” of the unit root test, a mixed prior specification …
More Efficient Estimation In Nonparametric Regression With Nonparametric Autocorrelated Errors, Liangjun Su, Aman Ullah
More Efficient Estimation In Nonparametric Regression With Nonparametric Autocorrelated Errors, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial estimator. Furthermore, we consider criterion functions based on the linear exponential family, which include the local polynomial least squares criterion as a special case. Simulation evidence suggests that significant gains can be …
A New Approach To Robust Inference In Cointegration, Sainan Jin, Peter Phillips, Yixiao Sun
A New Approach To Robust Inference In Cointegration, Sainan Jin, Peter Phillips, Yixiao Sun
Research Collection School Of Economics
A new approach to robust testing in cointegrated systems is proposed using non-parametric HAC estimators without truncation. While such HAC estimates are inconsistent, they still produce asymptotically pivotal tests and, as in conventional regression settings, can improve testing and inference.
L S Penrose's Limit Theorem: Tests By Simulation, Pao-Li Chang, Vincent Chua, Moshe Machover
L S Penrose's Limit Theorem: Tests By Simulation, Pao-Li Chang, Vincent Chua, Moshe Machover
Research Collection School Of Economics
LS Penrose’s limit theorem (PLT) – which is implicit in Penrose [5, p. 72] and for which he gave no rigorous proof – says that, in simple weighted voting games, if the number of voters increases indefinitely while existing voters retain their weights and the relative quota is pegged, then – under certain conditions – the ratio between the voting powers of any two voters converges to the ratio between their weights. Lindner and Machover [3] prove some special cases of PLT; and conjecture that the theorem holds, under rather general conditions, for large classes of weighted voting games, various …
Temporary Equilibrium Dynamics With Bayesian Learning, Shurojit Chatterji
Temporary Equilibrium Dynamics With Bayesian Learning, Shurojit Chatterji
Research Collection School Of Economics
This paper examines the stability of deterministic steady states in a class of economies where the state variable is one dimensional and where agents use Bayesian techniques to form expectations. The dynamics with learning are locally convergent if the prior mean is close to a stable perfect-foresight root having modulus less than 1 and if the prior beliefs are held with enough confidence. The dynamics are, however, divergent if the prior mean or the variance of the prior distribution is sufficiently large