Econometrics Commons^™

Boosting: Why You Can Use The Hp Filter, Peter C. B. Phillips, Zhentao Shi

Research Collection School Of Economics

We propose a procedure of iterating the HP filter to produce a smarter smoothing device, called the boosted HP (bHP) filter, based on L2-boosting in machine learning. Limit theory shows that the bHP filter asymptotically recovers trend mechanisms that involve integrated processes, deterministic drifts, and structural breaks, covering the most common trends that appear in current modeling methodology. A stopping criterion automates the algorithm, giving a data-determined method for data-rich environments. The methodology is illustrated in simulations and with three real data examples that highlight the differences between simple HP filtering, the bHP filter, and an alternative autoregressive approach.

Business Cycles, Trend Elimination, And The Hp Filter, Peter C. B. Phillips, Sainan Jin

Research Collection School Of Economics

Trend elimination and business cycle estimation are analyzed by finite sample and asymptotic methods. An overview history is provided, operator theory is developed, limit theory as the sample size n → ∞ is derived, and filtered series properties are studied relative to smoothing parameter (λ) behavior. Simulations reveal that limit theory with λ =O(n4) delivers excellent approximations to the HP filter for common sample sizes but fails to remove stochastic trends, contrary to standard thinking in macroeconomics and thereby explaining ‘spurious cycle’ effects of the HP filter. The findings are related to the long run effects of the GFC.

A Hybrid Equity Release Plan For Retirement Financing, Koon Shing Kwong, Yiu Kuen Tse, Junxing Chay

Research Collection School Of Economics

There are two main equity release plans for retirement financing: reverse mortgage plan and home reversion plan. Both plans entitle the homeowners not only to release cash from their properties but also to allow them living there for life. In the lease buyback scheme (LBS) recently introduced in Singapore, the home owner sells the tail-end of the property lease to the government in exchange for a cash payment upfront. Unlike the two main equity release plans, the LBS only allows the owner to stay in the property for the front part of the lease but not for life.

In this …

Spatial Dynamic Panel Data Models With Correlated Random Effects, Liyao Li, Zhenlin Yang

Research Collection School Of Economics

In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions …

Mildly Explosive Autoregression With Anti-Persistent Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu

Research Collection School Of Economics

An asymptotic distribution is derived for the least squares (LS) estimate of a first‐order autoregression with a mildly explosive root and anti‐persistent errors. While the sample moments depend on the Hurst parameter asymptotically, the Cauchy limiting distribution theory remains valid for the LS estimates in the model without intercept and a model with an asymptotically negligible intercept. Monte Carlo studies are designed to check the precision of the Cauchy distribution in finite samples. An empirical study based on the monthly NASDAQ index highlights the usefulness of the model and the new limiting distribution.

Determining The Number Of Communities In Degree-Corrected Stochastic Block Models, Shujie Ma, Liangjun Su, Yichong Zhang

Research Collection School Of Economics

We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio. For estimation, we consider a spectral clustering together with binary segmentation method. This approach guarantees an upper bound for the pseudo likelihood ratio statistic when the model is over-fitted. We also derive its limiting distribution when the model is under-fitted. Based on these properties, we establish the consistency of our estimator for the true number of communities. Developing these theoretical properties require a mild condition on the average degree: growing at a rate faster than log(n), where n is the number …

Mildly Explosive Autoregression With Anti-Persistent Errors, Yui Lim Lui, Jun Yu, Jun Yu

Research Collection School Of Economics

An asymptotic distribution is derived for the least squares (LS) estimate of a first-order autoregression with a mildly explosive root and anti-persistent errors. While the sample moments depend on the Hurst parameter asymptotically, the Cauchy limiting distribution theory remains valid for the LS estimates in the model without intercept and a model with an asymptotically negligible intercept. Monte Carlo studies are designed to check the precision of the Cauchy distribution in finite samples. An empirical study based on the monthly NASDAQ index highlights the usefulness of the model and the new limiting distribution.

Change In Outbreak Epicentre And Its Impact On The Importation Risks Of Covid-19 Progression: A Modelling Study, Oyelola A. Adegboye, Adeshina I. Adekunle, Anton Pak, Ezra Gayawan, Denis H. Y. Leung, Diana P. Rojas, Emma S. Mcbryde, Damon P. Eisen

Research Collection School Of Economics

Background: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. Methods: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to …

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 …

House Prices And Affordability, R. Greenaway-Mcgrevy, Peter C. B. Phillips

Research Collection School Of Economics

The decade following the global financial crisis (GFC) has witnessed rampant house price appreciation in many cities of the developed world. The metropolitan centres of New Zealand showcase this phenomenon with house price appreciation persistently outpacing income growth. In Auckland, the ratio of prevailing median house prices to median household income rose from 6.4 in 2010 to 10.0 in 2016 (Demographia, Citation2011, Citation2017), before declining to 8.6 by 2019 as house prices flat-lined while household incomes increased (Demographia, Citation2020). However, a strong resurgence in house prices during 2020 means that this ratio has resumed its upward trajectory, with an increase …

Joint Tests For Dynamic And Spatial Effects In Short Dynamic Panel Data Models With Fixed Effects And Heteroskedasticity, Zhenlin Yang

Research Collection School Of Economics

Simple and reliable tests are proposed for testing the existence of dynamic and/or spatial effects in fixed-effects panel data models with small T and possibly heteroskedastic errors. The tests are constructed based on the adjusted quasi scores (AQS), which correct the conditional quasi scores given the initial differences to account for the effect of initial values. To improve the finite sample performance, standardized AQS tests are also derived, which are shown to have much improved finite sample properties. All the proposed tests are robust against nonnormality, but some are not robust against cross-sectional heteroskedasticity (CH). A different type of adjustments …

Causal Change Detection In Possibly Integrated Systems: Revisiting The Money-Income Relationship, Shuping Shi, Stan Hurn, Peter C. B. Phillips

Research Collection School Of Economics

This paper re-examines changes in the causal link between money and income in the United States over the past half century (1959-2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a rolling window algorithm, and a recursive evolving algorithm all of which utilize subsample tests of Granger causality within a lagaugmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. Bootstrap methods are developed to control family-wise size in the implementation …

Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang

Research Collection School Of Economics

We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …

Quasi-Bayesian Inference For Production Frontiers, Xiaobin Liu, Thomas Tao Yang, Yichong Zhang

Research Collection School Of Economics

We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian method to produce the point estimate and confidence interval for the production frontier. We show the asymptotic properties of the proposed estimator and the validity of the inference procedure. The finite sample performance of our method is illustrated through simulations and an empirical application.

Point Optimal Testing With Roots That Are Functionally Local To Unity, Anna Bykhovskaya, Peter C. B. Phillips

Research Collection School Of Economics

Limit theory for regressions involving local to unit roots (LURs) is now used extensively in time series econometric work, establishing power properties for unit root and cointegration tests, assisting the construction of uniform confidence intervals for autoregressive coefficients, and enabling the development of methods robust to departures from unit roots. The present paper shows how to generalize LUR asymptotics to cases where the localized departure from unity is a time varying function rather than a constant. Such a functional local unit root (FLUR) model has much greater generality and encompasses many cases of additional interest that appear in practical work, …

Asymptotic Properties Of Least Squares Estimator In Local To Unity Processes With Fractional Gaussian Noises, Xiaohu Wang, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper derives asymptotic properties of the least squares estimator of the autoregressive parameter in local to unity processes with errors being fractional Gaussian noises with the Hurst parameter H. It is shown that the estimator is consistent when H ∈ (0, 1). Moreover, the rate of convergence is n when H ∈ [0.5, 1). The rate of convergence is n^2H when H ∈ (0, 0.5). Furthermore, the limit distribution of the centered least squares estimator depends on H. When H = 0.5, the limit distribution is the same as that obtained in Phillips (1987a) for the local to …

Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi K. Baltagi, Alain Pirotte, Zhenlin Yang

Research Collection School Of Economics

We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS-OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS-OPMD method provides feasible solutions. The AQS tests are formally derived and …

Data Driven Value-At-Risk Forecasting Using A Svr-Garch-Kde Hybrid, Marius Lux, Wolfgang Karl Hardle, Stefan Lessmann

Sim Kee Boon Institute for Financial Economics

Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is value-at-risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated …

Persistent And Rough Volatility, Xiaobin Liu, Shuping Shi, Jun Yu

Research Collection School Of Economics

This paper contributes to an ongoing debate on volatility dynamics. We introduce a discrete-time fractional stochastic volatility (FSV) model based on the fractional Gaussian noise. The new model has the same limit as the fractional integrated stochastic volatility (FISV) model under the in-fill asymptotic scheme. We study the theoretical properties of both models and introduce a memory signature plot for a model-free initial assessment. A simulated maximum likelihood (SML) method, which maximizes the time-domain log-likelihoods obtained by the importance sampling technique, is employed to estimate the model parameters. Simulation studies suggest that the SML method can accurately estimate both models. …

Uniform Nonparametric Inference For Time Series, Jia Li, Zhipeng Liao

Research Collection School Of Economics

This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of heteroskedasticity and autocorrelation consistent (HAC) estimators with increasing dimension are established for making feasible inference. An empirical application on the unemployment volatility …

Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse

Research Collection School Of Economics

We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum …

Unconditional Quantile Regression With High-Dimensional Data, Yuya Sasaki, Takuya Ura, Yichong Zhang

Research Collection School Of Economics

Credible counterfactual analysis requires high-dimensional controls. This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel doubly robust score for double/debiased estimation and inference for the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference for the Lasso double/debiased estimator, and develop asymptotic theories to guarantee that the bootstrap works. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that i) marginal effects of counterfactually extending the duration of the exposure to the …

Universal Minimum Wage Is Not Suitable For Singapore, Zhengxiao Wu

Research Collection School Of Economics

In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao examined the concept of a universal minimum wage, and discussed how it is not suitable for Singapore.

Maximum Likelihood Estimation For The Fractional Vasicek Model, Katsuto Tanaka, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper estimates the drift parameters in the fractional Vasicek model from a continuous record of observations via maximum likelihood (ML). The asymptotic theory for the ML estimates (MLE) is established in the stationary case, the explosive case, and the boundary case for the entire range of the Hurst parameter, providing a complete treatment of asymptotic analysis. It is shown that changing the sign of the persistence parameter changes the asymptotic theory for the MLE, including the rate of convergence and the limiting distribution. It is also found that the asymptotic theory depends on the value of the Hurst parameter.

Estimation Of Conditional Average Treatment Effects With High-Dimensional Data, Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang

Research Collection School Of Economics

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. This is a key feature since identification is generally more credible if the full vector of conditioning variables, including possible transformations, is high-dimensional. The second stage consists of a low-dimensional kernel regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator …

Activation Of Trpa1 Nociceptor Promotes Systemic Adult Mammalian Skin Regeneration, Jenny J. Wei, Hali S. Kim, Casey A. Spencer, Donna Brennan-Crispi, Ying Zheng, Nicolette M. Johnson, Misha Rosenbach, Christopher Miller, Denis H. Y. Leung, George Cotsarelis, Thomas H. Leung

Research Collection School Of Economics

Adult mammalian wounds, with rare exception, heal with fibrotic scars that severely disrupt tissue architecture and function. Regenerative medicine seeks methods to avoid scar formation and restore the original tissue structures. We show in three adult mouse models that pharmacologic activation of the nociceptor TRPA1 on cutaneous sensory neurons reduces scar formation and can also promote tissue regeneration. Local activation of TRPA1 induces tissue regeneration on distant untreated areas of injury, demonstrating a systemic effect. Activated TRPA1 stimulates local production of interleukin-23 (IL-23) by dermal dendritic cells, leading to activation of circulating dermal IL-17–producing γδ T cells. Genetic ablation of …

Spatial Panel Data Models With Temporal Heterogeneity, Yuhong Xu

Dissertations and Theses Collection (Open Access)

This dissertation studies the fixed effects (FE) spatial panel data (SPD) models with temporal heterogeneity (TH), where the regression coefficients and spatial coefficients are allowed to change with time. The FE-SPD model with time-varying coefficients renders the usual transformation method in dealing with the fixed effects inapplicable, and an adjusted quasi score (AQS) method is proposed, which adjusts the concentrated quasi score function with the fixed effects being concentrated out. AQS tests for the lack of temporal heterogeneity (TH) in slope and spatial parameters are first proposed. Then, a set of AQS estimation and inference methods for the FE-SPD model …

Redundancy Insurance Is Not Unemployment Insurance, Zhengxiao Wu

Research Collection School Of Economics

In a commentary, SMU Senior Lecturer of Statistics Wu Zhengxiao discussed the difference between redundancy insurance and unemployment insurance. He shared Japan's example, where the cost for unemployment insurance is higher than redundancy insurance, and added that being an unprecedented policy, more care should be taken when implementing it.

Essays On Multivariate Stochastic Volatility Models, Han Chen

Dissertations and Theses Collection (Open Access)

In this dissertation, I have made several contributions to the literature on the multivariate stochastic volatility model. First, I have considered a new multivariate stochastic volatility (MSV) model based on a recently proposed novel parameterization of the correlation matrix. This modeling design is a generalization of Fisher's z-transformation to the high-dimensional case. It is fully flexible as the validity of the resulting correlation matrix is guaranteed automatically. It allows me to completely separate the driving factors of volatilities and correlations. To conduct an econometric analysis of the proposed model, I develop a new Bayesian method that relies on the Markov …

Three Essays On Econometrics, Xin Zheng

Dissertations and Theses Collection (Open Access)

The dissertation includes three chapters on econometrics. The first chapter is about treatment effects and its application in randomized control trial. The second chapter is about specification test. The third chapter is about panel data model with fixed effects.

In the first chapter, we study the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment …