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- High-frequency data (4)
- Specification test (4)
- Bias (3)
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- Asian stock markets (2)
- Binding function (2)
- Clustering (2)
- Double asymptotics (2)
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- Inconsistency (2)
- Indirect Inference (2)
- Moderate deviations from unity (2)
- Partial identification (2)
- Real estate market (2)
- Return volatility (2)
- Semiparametric efficiency (2)
- Spatial autoregression (2)
- Strong consistency (2)
- Unidentified structure (2)
- Volatility spillovers (2)
- AIC (1)
- Adaptive estimation (1)
- Asymmetry (1)
- Asymptotic distribution (1)
- Autoregression (1)
- Autoregressive conditional duration model (1)
- Autoregressive regressions (1)
- Average treatment effect (1)
Articles 1 - 30 of 43
Full-Text Articles in Social and Behavioral Sciences
Identifying Latent Group Structures In Nonlinear Panels, Wuyi Wang, Liangjun Su
Identifying Latent Group Structures In Nonlinear Panels, Wuyi Wang, Liangjun Su
Research Collection School Of Economics
We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we extend the sequential binary segmentation algorithm of Bai (1997) for break detection from the time series setup to the panel data framework. We demonstrate that our method is able to identify …
Inference In Continuous Systems With Mildly Explosive Regressors, Ye Chen, Peter C. B. Phillips, Jun Yu
Inference In Continuous Systems With Mildly Explosive Regressors, Ye Chen, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
New limit theory is developed for co-moving systems with explosive processes, connecting continuous and discrete time formulations. The theory uses double asymptotics with infill (as the sampling interval tends to zero) and large time span asymptotics. The limit theory explicitly involves initial conditions, allows for drift in the system, is provided for single and multiple explosive regressors, and is feasible to implement in practice. Simulations show that double asymptotics deliver a good approximation to the finite sample distribution, with both finite sample and asymptotic distributions showing sensitivity to initial conditions. The methods are implemented in the US real estate market …
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error.
Bayesian Analysis Of Bubbles In Asset Prices, Andras Fulop, Jun Yu
Bayesian Analysis Of Bubbles In Asset Prices, Andras Fulop, Jun Yu
Research Collection School Of Economics
We develop a new asset price model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. This latter is allowed to account for possible smooth structural change. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly …
Inference In Continuous Systems With Mildly Explosive Regressors, Ye Chen, Peter C. B. Phillips, Jun Yu
Inference In Continuous Systems With Mildly Explosive Regressors, Ye Chen, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
New limit theory is developed for co-moving systems with explosive processes, connecting continuous and discrete time formulations. The theory uses double asymptotics with infill (as the sampling interval tends to zero) and large time span asymptotics. The limit theory explicitly involves initial conditions, allows for drift in the system, is provided for single and multiple explosive regressors, and is feasible to implement in practice. Simulations show that double asymptotics deliver a good approximation to the finite sample distribution, with both finite sample and asymptotic distributions showing sensitivity to initial conditions. The methods are implemented in the US real estate market …
Volatility Spillovers And Linkages In Asian Stock Markets, Hwee Kwan Chow
Volatility Spillovers And Linkages In Asian Stock Markets, Hwee Kwan Chow
Research Collection School Of Economics
Diebold–Yilmaz spillover indexes are computed for weekly return volatilities based on daily benchmark stock indexes of the US, the UK, and 10 Asian countries. We found (i) the strengthening of overall volatility spillovers is not a temporary surge but persisted after the crisis; (ii) the susceptibility of individual Asian stock markets to inward volatility transfers is linked to its degree of openness; and (iii) the Asian bourses are becoming more important emitters of financial shocks since the crisis. Rolling regressions on volatility linkages reveal the relative dominance of the US over the Japanese and Chinese bourses, and the level of …
Determining The Number Of Groups In Latent Panel Structures With An Application To Income And Democracy, Xun Lu, Liangjun Su
Determining The Number Of Groups In Latent Panel Structures With An Application To Income And Democracy, Xun Lu, Liangjun Su
Research Collection School Of Economics
We consider a latent group panel structure as recently studied by Su, Shi, and Phillips (2014), where the number of groups is unknown and has to be determined empirically. We propose a testing procedure to determine the number of roups. Our test is a residualbased LM-type test. We show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis of a given number of groups and has power to detect deviations from the null. Monte Carlo simulations show that our test performs remarkably well in finite samples. We apply our method to study the effect …
Volatility Spillovers And Linkages In Asian Stock Markets, Hwee Kwan Chow-Tan
Volatility Spillovers And Linkages In Asian Stock Markets, Hwee Kwan Chow-Tan
Research Collection School Of Economics
Diebold-Yilmaz spilloverindexes are computed for weekly return volatilities based on daily benchmarkstock indexes of US, UK and ten Asian countries. We found (i) the strengthening ofoverall volatility spillovers is not a temporary surge but persisted after thecrisis; (ii) the susceptibility ofindividual Asian stock markets to inward volatility transfers is linked to itsdegree of openness; and (iii) the Asian bourses are becoming more importantemitters of financial shocks since the crisis. Rolling regressions on volatilitylinkages reveal the relative dominance of the US over the Japanese and Chinesebourses, and the level of influence on Asian stock markets from the Chinesebourse has risen to …
Estimating Finite-Horizon Life-Cycle Models: A Quasi-Bayesian Approach, Xiaobin Liu
Estimating Finite-Horizon Life-Cycle Models: A Quasi-Bayesian Approach, Xiaobin Liu
Research Collection School Of Economics
This paper proposes a quasi-Bayesian approach for structural parameters in finite-horizon life-cycle models. This approach circumvents the numerical evaluation of the gradient of the objective function and alleviates the local optimum problem. The asymptotic normality of the estimators with and without approximation errors is derived. The proposed estimators reach the semiparametric eciency bound in the general methods of moment (GMM) framework. Both the estimators and the corresponding asymptotic covariance are readily computable. The estimation procedure is easy to parallel so that the graphic processing unit (GPU) can be used to enhance the computational speed. The estimation procedure is illustrated using …
Random Coefficient Continuous Systems: Testing For Extreme Sample Path Behaviour, Yubo Tao, Peter C. B. Phillips, Jun Yu
Random Coefficient Continuous Systems: Testing For Extreme Sample Path Behaviour, Yubo Tao, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behaviour according to specific regions of the parameter space that open up the potential for testing these forms of extreme behaviour. A two-stage approach that employs realized volatility is proposed for the continuous system estimation, asymptotic theory is developed, and test statistics to identify the different forms of extreme sample path behaviour are proposed. Simulations show that the …
Strong Consistency Of Spectral Clustering For Stochastic Block Models, Liangjun Su, Wuyi Wang, Yichong Zhang
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 thespectral clustering techniques that are widely used to study the communitydetection problem in stochastic block models (SBMs). We show that under someweak conditions on the minimal degree, the number of communities, and theeigenvalues of the probability block matrix, the K-means algorithm applied tothe Eigenvectors of the graph Laplacian associated with its first few largesteigenvalues can classify all individuals into the true community uniformlycorrectly almost surely. Extensions to both regularized spectral clustering anddegree-corrected SBMs are also considered. We illustrate the performance ofdifferent methods on simulated networks.
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Specification Test For Spatial Autoregressive Models, Liangjun Su, Xi Qu
Research Collection School Of Economics
This article considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix Wn is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove the consistency of our test. To improve the finite sample performance of our test, we also propose a residual-based wild bootstrap and justify its asymptotic validity. We conduct a small set of Monte Carlo simulations to investigate …
Non-Separable Models With High-Dimensional Data, Liangjun Su, Takuya Ura, Yichong Zhang
Non-Separable Models With High-Dimensional Data, Liangjun Su, Takuya Ura, Yichong 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 penalized conditional density estimation, respectively, where control variables are selected via a localized method of L1-penalization at each value of the continuous treatment. In the second stage we estimate the average and the marginal distribution …
Bubble Testing Under Deterministic Trends, Xiaohu Wang, Jun Yu
Bubble Testing Under Deterministic Trends, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
This paper develops the asymptotic theory of the ordinary least squares estimator of the autoregressive (AR) coefficient in various AR models, when data is generated from trend-stationary models in different forms. It is shown that, depending on how the autoregression is specified, the commonly used right-tailed unit root tests may tend to reject the null hypothesis of unit root in favor of the explosive alternative. A new procedure to implement the right-tailed unit root tests is proposed. It is shown that when the data generating process is trend-stationary, the test statistics based on the proposed procedure cannot find evidence of …
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We derive the asymptotic efficiency bound for regular estimates of the slope coefficient in a linear continuous-time regression model for the continuous martingale parts of two Itô semimartingales observed on a fixed time interval with asymptotically shrinking mesh of the observation grid. We further construct an estimator from high-frequency data that achieves this efficiency bound and, indeed, is adaptive to the presence of infinite-dimensional nuisance components. The estimator is formed by taking optimal weighted average of local nonparametric volatility estimates that are constructed over blocks of high-frequency observations. The asymptotic efficiency bound is derived under a Markov assumption for the …
Domestic Liquidity Conditions And Monetary Policy In Singapore, Hwee Kwan Chow-Tan
Domestic Liquidity Conditions And Monetary Policy In Singapore, Hwee Kwan Chow-Tan
Research Collection School Of Economics
Singapore has an unusual exchange rate-centered monetary policy framework that has served the economy well over the past decades. Monetary policy operations are carried out by the central bank through the management of the Singapore dollar against a currency basket. As is well recognized, such foreign exchange interventions do have an impact on domestic liquidity conditions. However, in the case of Singapore, this tends to be counteracted by the liquidity impact of public sector operations related to the fiscal position and the national pension scheme. The central bank takes into account the net liquidity impact these and other autonomous money …
Structural Inference From Reduced Forms With Many Instruments, Peter C. B. Phillips, Wayne Yuan Gao
Structural Inference From Reduced Forms With Many Instruments, Peter C. B. Phillips, Wayne Yuan Gao
Research Collection School Of Economics
This paper develops exact finite sample and asymptotic distributions for structural equation tests based on partially restricted reduced form estimates. Particular attention is given to models with large numbers of instruments, wherein the use of partially restricted reduced form estimates is shown to be especially advantageous in statistical testing even in cases of uniformly weak instruments. Comparisons are made with methods based on unrestricted reduced forms, and numerical computations showing finite sample performance of the tests are reported. Some new results are obtained on inequalities between noncentral chi-squared distributions with different degrees of freedom that assist in analytic power comparisons.
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Research Collection School Of Economics
Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias …
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Research Collection School Of Economics
Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias …
In-Fill Asymptotic Theory For Structural Break Point In Autoregression: A Unified Theory, Liang Jiang, Xiaohu Wang, Jun Yu
In-Fill Asymptotic Theory For Structural Break Point In Autoregression: A Unified Theory, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
This paper obtains the exact distribution of the maximum likelihood estimatorof structural break point in the OrnsteinñUhlenbeck process when a continuousrecord is available. The exact distribution is asymmetric, tri-modal, dependenton the initial condition. These three properties are also found in the önite sampledistribution of the least squares (LS) estimator of structural break point inautoregressive (AR) models. Motivated by these observations, the paper then developsan in-öll asymptotic theory for the LS estimator of structural break point inthe AR(1) coe¢ cient. The in-öll asymptotic distribution is also asymmetric, trimodal,dependent on the initial condition, and delivers excellent approximationsto the önite sample distribution. Unlike …
Singapore’S Life Program: Actuarial Framework, Longevity Risk And Impact Of Annuity Fund Return, Koon Shing Kwong, Yiu Kuen Tse, Wai-Sum Chan
Singapore’S Life Program: Actuarial Framework, Longevity Risk And Impact Of Annuity Fund Return, Koon Shing Kwong, Yiu Kuen Tse, Wai-Sum Chan
Research Collection School Of Economics
The Central Provident Fund (CPF) is a defined-contribution savings plan forming the key pillar of the pension system in Singapore. The CPF Lifelong Income For the Elderly (LIFE) program, which provides lifetime income for retirees, is a mandatory pension scheme for all Singapore residents. In this paper we construct an actuarial framework to analyze the LIFE program. We use this framework to study the plan payout outcomes with respect to changes in mortality and annuity fund return assumptions. We also examine the effects of some possible changes in the program on the payouts and bequests.
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 …
A Martingale Difference-Divergence-Based Test For Specification, Liangjun Su, Xin Zheng
A Martingale Difference-Divergence-Based Test For Specification, Liangjun Su, Xin Zheng
Research Collection School Of Economics
In this paper we propose a novel consistent model specification test based on the martingale difference divergence (MDD) of the error term given the covariates. The MDD equals zero if and only if error term is conditionally mean independent of the covariates. Our MDD test does not require any nonparametric estimation under the alternative and it is applicable even if we have many covariates in the regression model. We establish the asymptotic distributions of our test statistic under the null and a sequence of Pitman local alternatives converging to the null at the usual parametric rate. Simulations suggest that our …
Improved Likelihood Inferences For Weibull Regression Model, Yan Shen, Zhenlin Yang
Improved Likelihood Inferences For Weibull Regression Model, Yan Shen, Zhenlin Yang
Research Collection School Of Economics
A general procedure is developed for bias-correcting the maximum likelihood estimators (MLEs) of the parameters of Weibull regression model with either complete or right-censored data. Following the bias correction, variance corrections and hence improved t-ratios for model parameters are presented. Potentially improved t-ratios for other reliability-related quantities are also discussed. Simulation results show that the proposed method is effective in correcting the bias of the MLEs, and the resulted t-ratios generally improve over the regular t-ratios.
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …
A Specification Test Based On The Mcmc Output, Yong Li, Jun Yu, Tao Zeng
A Specification Test Based On The Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
A test statistic is proposed to assess themodel specification after the model is estimated by Bayesian MCMC methods. Thenew test is motivated from the power enhancement technique of Fan, Liao and Yao(2015). It combines a component (J1) that tests anull point hypothesis in an expanded model and a power enhancement component (J0) obtained from the null model. It is shown that J0 converges to zero when the null model is correctly specified anddiverges when the null model is misspecified. Also shown is that J1 is asymptotically X2-distributed, suggesting that theproposed test is asymptotically pivotal, when the null model is correctlyspecified. …
Asymptotic Theory For Estimating Drift Parameters In The Fractional Vasicek Model, Weilin Xiao, Jun Yu
Asymptotic Theory For Estimating Drift Parameters In The Fractional Vasicek Model, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper develops the asymptotic theory for estimators of two parameters in the drift function in the fractional Vasicek model when a continuous record of observations is available. The fractional Vasicek model is assumed to be driven by the fractional Brownian motion with a known Hurst parameter greater than or equal to one half. It is shown that the asymptotic theory for the persistent parameter depends critically on its sign, corresponding asymptotically to the stationary case, the explosive case, and the null recurrent case. In all three cases, the least squares method is considered. When the persistent parameter is positive, …
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Research Collection School Of Economics
Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G-non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G-non-causality to test …
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 …
Lag Length Selection In Panel Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Lag Length Selection In Panel Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples.