Econometrics Commons^™

Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yui 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, Jun Yu, Chen Zhang

Research Collection School Of Economics

This paper investigates the performance of different forecasting formulas with fractional Brownian motion based on discrete and finite samples. Existing literature presents two formulas for generating optimal forecasts when continuous records are available. One formula relies on a history over an infinite past, while the other is designed for a record limited to a finite past. In reality, only observations at discrete time points over a finite past are available. In this case, the forecasting formula, which has been widely used in the literature, is the one obtained by Gatheral et al. (2018) that truncates and discretizes the formula based …

A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang

Research Collection School Of Economics

We consider a linear combination of jackknife Anderson-Rubin (AR) and orthogonalized Lagrangian multiplier (LM) tests for inference in IV regressions with many weak instruments and heteroskedasticity. We choose the weight in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed linear combination test controls asymptotic size and is admissible. Under strong identification, we further show that our linear combination test is the uniformly most powerful test against local alternatives among all tests that are constructed based on the jackknife AR and LM tests only and invariant …

Optimal Nonparametric Range-Based Volatility Estimation, Tim Bollerslev, Jia Li, Qiyuan Li

Research Collection School Of Economics

We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time-interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency gains compared to existing commonly used range-based procedures.

Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover, they generate sizable economic values, and their predictability is not spanned by the yield curve. We also observe that the forecasted bond returns are countercyclical, and the magnitude of predictability is stronger during …

Dynamic Factor Copula Models With Estimated Cluster Assignments, Dong Hwan Oh, Andrew John Patton

Research Collection School Of Economics

This paper proposes a dynamic multi-factor copula for use in high-dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five …

Estimating And Applying Autoregression Models Via Their Eigensystem Representation, Leo Krippner

Sim Kee Boon Institute for Financial Economics

This article introduces the eigensystem autoregression (EAR) framework, which allows an AR model to be specified, estimated, and applied directly in terms of its eigenvalues and eigenvectors. An EAR estimation can therefore impose various constraints on AR dynamics that would not be possible within standard linear estimation. Examples are restricting eigenvalue magnitudes to control the rate of mean reversion, additionally imposing that eigenvalues be real and positive to avoid pronounced oscillatory behavior, and eliminating the possibility of explosive episodes in a time-varying AR. The EAR framework also produces closed-form AR forecasts and associated variances, and forecasts and data may be …

Customer Capital And Trade Intermediaries: Evidence From China, Jungho Lee, Jianhuan Xu

Research Collection School Of Economics

Using a unique dataset that links the production and sales of Chinese exporting firms, we document that the value of export goods a firm produces often differs from the value of export goods that the firm sells in foreign markets. We show that this empirical pattern reflects that some exporters act as trade intermediaries, which we refer to as producer intermediaries. We further show that firms with higher accumulated marketing expenditures are more likely to become producer intermediaries. To understand the implications of our empirical findings, we develop a theoretical framework in which firms can lend and borrow customer capital …

Limit Theory For Locally Flat Functional Coefficient Regression, Peter C. B. Phillips, Ying Wang

Research Collection School Of Economics

Functional coefficient (FC) regressions allow for systematic flexibility in the responsiveness of a dependent variable to movements in the regressors, making them attractive in applications where marginal effects may depend on covariates. Such models are commonly estimated by local kernel regression methods. This paper explores situations where responsiveness to covariates is locally flat or fixed. The paper develops new asymptotics that take account of shape characteristics of the function in the locality of the point of estimation. Both stationary and integrated regressor cases are examined. The limit theory of FC kernel regression is shown to depend intimately on functional shape …

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.

Self-Financing, Parental Transfer, And College Education, Jungho Lee, Sunha Myong

Research Collection School Of Economics

We show that financial constraints can affect the human capital accumulation of college students by influencing students’ labor supply. We document that many college students work a substantial number of hours at low-skill jobs, and students who have fewer financial resources (in particular, parental transfer) tend to work more. We develop a model that incorporates college students’ labor supply and its interaction with parental transfer in the presence of financial constraints. By estimating the model, we quantify the trade-off between self-financing and human capital accumulation and discuss the implications of a wage subsidy policy.

Spatial Disaggregation Of Poverty And Disability: Application To Tanzania, Tomoki Fujii

Research Collection School Of Economics

Estimating poverty measures for disabled people in developing countries is often difficult, partly because relevant data are not readily available. We extend the small-area estimation developed by Elbers, Lanjouw and Lanjouw (2002, 2003) to estimate poverty by the disability status of the household head, when the disability status is unavailable in the survey. We propose two alternative approaches to this extension: Aggregation and Instrumental Variables Approaches. We apply these approaches to data from Tanzania and show that both approaches work. Our estimation results show that disability is indeed positively associated with poverty in every region of mainland Tanzania.

Common Bubble Detection In Large Dimensional Financial Systems, Ye Chen, Peter C. B. Phillips, Shuping Shi

Research Collection School Of Economics

Price bubbles in multiple assets are sometimes nearly coincident in occurrence. Such near-coincidence is strongly suggestive of co-movement in the associated asset prices and is likely driven by certain factors that are latent in the financial or economic system with common effects across several markets. Can we detect the presence of such common factors at the early stages of their emergence? To answer this question, we build a factor model that includes I(1), mildly explosive, and stationary factors to capture normal, exuberant, and collapsing phases in such phenomena. The I(1) factor models the primary driving force of market fundamentals. The …

Volatility Puzzle: Long Memory Or Anti-Persistency, Shuping Shi, Jun Yu

Research Collection School Of Economics

The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1,d,01,d,0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d α close to 0 and d close to 0.5) from Model 2Model 2 (ARFIMA(1,d,01,d,0) with α close to unity and d close to –0.5). An intuitive explanation is given. For the 10 financial assets considered, despite that no definitive conclusions …

Multivariate Stochastic Volatility Models Based On Generalized Fisher Transformation, Han Chen, Yijie Fei, Jun Yu

Research Collection School Of Economics

Modeling multivariate stochastic volatility (MSV) can be challenging, particularly when both variances and covariances are time-varying. In this paper, we address these challenges by introducing a new MSV model based on the generalized Fisher transformation of Archakov and Hansen (2021). Our model is highly exible and ensures that the variance-covariance matrix is always positive-definite. Moreover, our approach separates the driving factors of volatilities and correlations. To conduct Bayesian analysis of the model, we use a Particle Gibbs Ancestor Sampling (PGAS) method, which facilitates Bayesian model comparison. We also extend our MSV model to cover the leverage effect in volatilities and …

The Impact Of Upzoning On Housing Construction In Auckland*, Ryan Greenaway-Mcgrevy, Peter C. B. Phillips

Research Collection School Of Economics

There is a growing debate about whether upzoning is an effective policy response to housing shortages and unaffordable housing. This paper provides empirical evidence to further inform debate by examining the various impacts of recently implemented zoning reforms on housing construction in Auckland, the largest metropolitan area in New Zealand. In 2016, the city upzoned approximately three quarters of its residential land to facilitate construction of more intensive housing. We use a quasi-experimental approach to analyze the short-run impacts of the reform on construction, allowing for potential shifts in construction from non-upzoned to upzoned areas (displacement effects) that would, if …

Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators Of Time Series Models, Sílvia Gonçalves, Ulrich Hounyo, Andrew John Patton, Kevin Sheppard

Research Collection School Of Economics

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard …

Regression-Adjusted Estimation Of Quantile Treatment Effects Under Covariate-Adaptive Randomizations, Liang Jiang, Peter C. B. Phillips, Yubo Tao, Yichong Zhang

Research Collection School Of Economics

Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these additional covariates via auxiliary regressions in the estimation and inference of unconditional quantile treatment effects (QTEs) under CARs. We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability …

Essays On Culture, Institutions, And Development, Meng Liu

Dissertations and Theses Collection (Open Access)

This dissertation consists of three chapters that study culture, institutions, and economic development. In the first chapter, we study the impact of Confucianism on long-run development in northern Vietnam. Using the variation in Confucian literati across 217 historical districts between the Primitive Le and Nguyen dynasties (1426 CE - 1919 CE), we find that districts with greater exposure to Confucianism have experienced better economic outcomes over the past century. The result is robust to using the distance to exogenously located hermit Confucian as an instrument and accounting for a battery of confounders. We show that the positive effects of Confucianism …

Disagreement In Market Index Options, Guilherme Salome, George Tauchen, Jia Li

Research Collection School Of Economics

We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the …

Economic Forecasting In A Pandemic: Some Evidence From Singapore, Hwee Kwan Chow-Tan, Keen Meng Choy

Research Collection School Of Economics

This paper aims to investigate whether the predictive performance and behaviour of professional forecasters are different during the COVID-19 pandemic as compared with the global financial crisis of 2008 and normal times. To this end, we use a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting records for GDP growth and CPI inflation for the period 2000Q1–2021Q4. We first examine the point forecasts to document the extent of forecast failure duringthe two crises and explore various explanations for it, such as leader-following and herding behaviour. Then, using percentile-based summary measures of probability distribution …

Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu

Research Collection School Of Economics

Power posteriors have become popular in estimating the marginal likelihood of a Bayesian model. A power posterior is referred to as the posterior distribution that is proportional to the likelihood raised to a power b∈[0,1]. Important power-posterior-based algorithms include thermodynamic integration (TI) of Friel and Pettitt (2008) and steppingstone sampling (SS) of Xie et al. (2011). In this paper, it is shown that the Bernstein–von Mises (BvM) theorem holds for power posteriors under regularity conditions. Due to the BvM theorem, power posteriors, when adjusted by the square root of the auxiliary constant, have the same limit distribution as the original …

Efficient Estimation Of Generalized Nonparametric Model Under Additive Structure, Ying Xia

Dissertations and Theses Collection (Open Access)

In this thesis, we develop novel nonparametric estimation techniques for two distinct classes of models: (1) Generalized Additive Models with Unknown Link Functions (GAMULF) and (2) Generalized Panel Data Transformation Models with Fixed Effects. Both models avoid parametric assumptions on their respective link or transformation functions, as well as the distribution of the idiosyncratic error terms.

The first chapter aims to provide an in-depth and systematic introduction to cross- sectional and panel-data nonparametric transformation models, encompassing practical applications, a diverse range of estimation techniques, and the study of asymptotic properties. We discuss the advantages and limitations of these models and …

Essays On Large Panel Data Models With Two-Way Heterogeneity, Yiren Wang

Dissertations and Theses Collection (Open Access)

This dissertation consists of two papers that contribute to the estimation and inference theory of the panel data models with two-way slope heterogeneity. The first paper considers the panel quantile regression model with slope heterogeneity along both individuals and time. By modelling this two-way heterogeneity with the low-rank slope matrix, the slope coefficient can be estimated via the nuclear norm regularization followed by sample-splitting, row- and column-wise quantile regression, and debiasing. The inferential theory for the final slope estimator along with its factor and factor loading is derived. Two specification tests are proposed: one tests whether the slope coefficient is …

On The Spectral Density Of Fractional Ornstein-Uhlenbeck Process: Approximation, Estimation, And Model Comparison, Shuping Shi, Jun Yu, Chen Zhang

Research Collection School Of Economics

This paper introduces a novel method for accurately approximating the spectral density of the discretely-sampled fractional Ornstein-Uhlenbeck (fOU) process. We utilize this approximated spec-tral density to develop an estimation method called the approximated Whittle maximum likelihood method (AWML) for fOU. Additionally, we develop a likelihood-ratio (LR) test using the approxi-mated spectral densities to distinguish between the fractional Brownian motion (fBm) and fOU pro-cesses, two popular models in the volatility literature. Simulation studies demonstrate that the AWML method improves the estimation speed and accuracy compared to existing ones and that the LR test is effective in distinguishing between the two processes …

Inflation Dynamics And Expectations In Singapore, Hwee Kwan Chow-Tan

Research Collection School Of Economics

Inflation dynamics in Singapore have primarily been shaped by foreign factors, including global inflationary pressures and external macroeconomic shocks. More recently, the normalisation phase of the Covid-19 pandemic crisis has led to domestic price pressures from pent-up demand and supply-chain disruptions. Meanwhile, the war in Ukraine has resulted in a hike in the global prices of food, energy, and industrial commodities. Using inflation forecasts from the MAS Survey of Professional Forecasters as our measure of inflation expectations, we show that short-term inflation expectations have shifted up recently. Moreover, greater disagreement amongst survey respondents in the more recent surveys suggests individual …

Uniform Nonparametric Inference For Time Series Using Stata, Jia Li, Zhipeng Liao, Mengsi Gao

Research Collection School Of Economics

In this article, we introduce a command, tssreg, that conducts nonparametric series estimation and uniform inference for time-series data, including the case with independent data as a special case. This command can be used to nonparametrically estimate the conditional expectation function and the uniform confidence band at a user-specified confidence level, based on an econometric theory that accommodates general time-series dependence. The uniform inference tool can also be used to perform nonparametric specification tests for conditional moment restrictions commonly seen in dynamic equilibrium models.

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 2 (0; 1). It is shown that the estimator is consistent for all values of H 2 (0; 1). Moreover, the rate of convergence is n 1 when H 2 [0:5; 1). The rate of convergence is n 2H when H 2 (0; 0:5). Furthermore, the limiting distribution of the centered least squares estimator depends on H. When H = 0:5, the limiting distribution is the same as that obtained …

Estimation And Inference With Near Unit Roots, Peter C. B. Phillips

Research Collection School Of Economics

New methods are developed for identifying, estimating, and performing inference with nonstationary time series that have autoregressive roots near unity. The approach subsumes unit-root (UR), local unit-root (LUR), mildly integrated (MI), and mildly explosive (ME) specifications in the new model formulation. It is shown how a new parameterization involving a localizing rate sequence that characterizes departures from unity can be consistently estimated in all cases. Simple pivotal limit distributions that enable valid inference about the form and degree of nonstationarity apply for MI and ME specifications and new limit theory holds in UR and LUR cases. Normalizing and variance stabilizing …

Adjustment With Many Regressors Under Covariate-Adaptive Randomizations, Liang Jiang, Liyao Li, Ke Miao, Yichong Zhang

Research Collection School Of Economics

Our paper identifies a trade-off when using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the efficiency of causal estimators by incorporating information from covariates that are not used in the randomization. On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the sample size. Failure to account for the cost of RAs can result in over-rejection of causal inference under the null hypothesis. To address this issue, we develop a unified …