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Articles 1 - 30 of 771
Full-Text Articles in Econometrics
Threshold Spatial Panel Regression With Fixed Effects, Xiaoyu Meng, Zhenlin Yang
Threshold Spatial Panel Regression With Fixed Effects, Xiaoyu Meng, Zhenlin Yang
Research Collection School Of Economics
We introduce general estimation and inference methods for threshold spatial panel regression with two-way fixed effects in a diminishing-threshold-effects framework. A valid objective function is obtained through a simple adjustment on the concentrated quasi loglikelihood with fixed effects being concentrated out, which leads to a consistent estimation of all common parameters. We show that the estimation of threshold parameter has a negligible effect on the asymptotic distribution of the main parameter estimators and thereby regular inference methods apply, though a bias correction may be necessary. The limiting distribution of the threshold parameter estimator is shown to be non-regular and infeasible, …
Semiparametric Cointegrating Rank Selection For Curved Cross-Section Time Series, Peter C. B. Phillips
Semiparametric Cointegrating Rank Selection For Curved Cross-Section Time Series, Peter C. B. Phillips
Research Collection School Of Economics
Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross-section curves. Consistent cointegrating rank estimation is developed using information criteria extended to curve time series environments. The asymptotic theory involves two-parameter Gaussian processes that generalise the standard limit processes involved in cointegrating regressions. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as in standard lag order selection in autoregression, as well as in reduced rank regression with multiple time series.
Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
Research Collection School Of Economics
We study how to improve efficiency via regression adjustments with additional covariates under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We first establish the semiparametric efficiency bound for the local average treatment effect (LATE) under CARs. Second, we develop a general regression-adjusted LATE estimator which allows for parametric, nonparametric, and regularized adjustments. Even when the adjustments are misspecified, our proposed estimator is still consistent and asymptotically normal, and their inference method still achieves the exact asymptotic size under the null. When the adjustments are correctly specified, our estimator achieves the semiparametric efficiency bound. Third, we derive the optimal linear …
Fixed Effects Estimation Of Spatial Panel Model With Missing Responses: An Application To Us State Tax Competition, Xiaoyu Meng, Zhenlin Yang
Fixed Effects Estimation Of Spatial Panel Model With Missing Responses: An Application To Us State Tax Competition, Xiaoyu Meng, Zhenlin Yang
Research Collection School Of Economics
We consider estimation and inferences for general spatial panel data models with randomly missing observations on responses. It allows for unobserved spatiotemporal heterogeneity, time-varying endogenous and contextual spatial interactions, time-varying cross-sectional error dependence, and serial correlation. A general M-estimation method is proposed for model estimation and a novel corrected plug-in method is proposed for model inference. Both take into account the estimation of fixed effects. Asymptotic properties of the proposed methods are studied, and finite sample properties are investigated. An empirical application is given using U.S. state tax competition data. The proposed methods apply to matrix exponential spatial specification and …
Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang
Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang
Research Collection School Of Economics
We consider spatial panel data models with genuine unbalancedness arising from the non-presence of some spatial units in certain time periods. General M-estimation methods are proposed for model estimation, which take into account the estimation of the incidental fixed effects parameters and allow for spatiotemporal heteroskedasticity and high-order time-varying spatial effects. Corrected plug-in methods are proposed for standard error estimation. The proposed estimation and inference methods are rigorously studied for their asymptotic properties and finite sample performance. An application to China’s provincial FDI inflows shows that properly accounting for genuine unbalancedness uncovers significant positive spatial spillovers that are masked when …
High Dimensional Regression Coefficient Test With High Frequency Data, Dachuan Chen, Long Feng, Per A. Myklang, Lan Zhang
High Dimensional Regression Coefficient Test With High Frequency Data, Dachuan Chen, Long Feng, Per A. Myklang, Lan Zhang
Research Collection School Of Economics
This paper presents the first study on high-dimensional regression coefficient tests with high-frequency financial data. These tests allow the number of regressors to be larger than the number of observations within each estimation block and can grow to infinity in asymptotics. In this paper, the sum-type test and max-type test have been proposed, where the former is suitable for the dense alternative (many small betas) and the latter is suitable for the sparse alternative (a very small number of large betas). By showing the asymptotic independence between the sum-type test and max-type test, the paper proposes a third test – …
Bespoke Realized Volatility: Tailored Measures Of Risk For Volatility Prediction, Andrew John Patton, Haozhe Zhang
Bespoke Realized Volatility: Tailored Measures Of Risk For Volatility Prediction, Andrew John Patton, Haozhe Zhang
Research Collection School Of Economics
Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data with no reference to how or where the estimate will subsequently be used. This paper presents methods for “tailoring” the estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a specific parametric forecasting model, it may be possible to exploit that knowledge to construct a better measure of volatility. We use methods from machine learning to estimate optimal “bespoke” RVs for heterogeneous autoregressive (HAR) and GARCH-X forecasting applications. We apply …
Dynamic Spatial Panel Data Models With Interactive Fixed Effects: M-Estimation And Inference Under Fixed Or Relatively Small T, Liyao Li, Ke Miao, Zhenlin Yang
Dynamic Spatial Panel Data Models With Interactive Fixed Effects: M-Estimation And Inference Under Fixed Or Relatively Small T, Liyao Li, Ke Miao, Zhenlin Yang
Research Collection School Of Economics
We propose an M-estimation method for dynamic spatial panel data models with interactive fixed effects based on (relatively) short panels. Unbiased estimating functions are constructed by adjusting the concentrated conditional quasi scores, given the initial values and with the factor loadings being concentrated out, to account for the effects of conditioning and concentration. Solving the estimating equations gives the M-estimators of the common parameters and common factors. Under fixed T, n-consistency and joint asymptotic normality of the M-estimators are established. Under T = o(n), the M-estimators of the common parameters are shown to be nT-consistent and asymptotically normal. For inference, …
A General Limit Theory For Nonlinear Functionals Of Nonstationary Time Series, Qiying Wang, Peter C. B. Phillips
A General Limit Theory For Nonlinear Functionals Of Nonstationary Time Series, Qiying Wang, Peter C. B. Phillips
Research Collection School Of Economics
New limit theory is provided for a wide class of sample variance and covariance functionals involving both nonstationary and stationary time series. Sample functionals of this type commonly appear in regression applications and the asymptotics are particularly relevant to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root, or fractional processes. The limit theory is unusually general in that it covers both parametric and nonparametric regressions. Self-normalized versions of these statistics are considered that are useful in inference. Numerical evidence reveals interesting strong bimodality in the finite sample distributions of conventional self-normalized statistics similar to …
New Asymptotics Applied To Functional Coefficient Regression And Climate Sensitivity Analysis, Qiying Wang, Peter C. B. Phillips, Ying Wang
New Asymptotics Applied To Functional Coefficient Regression And Climate Sensitivity Analysis, Qiying Wang, Peter C. B. Phillips, Ying Wang
Research Collection School Of Economics
A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth …
Testing Mean Stability Of Heteroskedastic Time Series, Violetta Dalla, Liudas Giraitis, Peter C. B. Phillips
Testing Mean Stability Of Heteroskedastic Time Series, Violetta Dalla, Liudas Giraitis, Peter C. B. Phillips
Research Collection School Of Economics
Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions about time dependence, causality, and the effects of unanticipated shocks. In spite of what may seem as obvious differences between a time series of independent variates with changing variance and a stationary conditionally heteroskedastic (GARCH) process, such processes may be hard to distinguish in applied work using basic …
Uncovering Mild Drift In Asset Prices With Intraday High-Frequency Data, Shuping Shi, Peter C. B. Phillips
Uncovering Mild Drift In Asset Prices With Intraday High-Frequency Data, Shuping Shi, Peter C. B. Phillips
Research Collection School Of Economics
Asset prices are commonly represented as a drift-diffusion process, wherein the drift component denotes the anticipated return of the asset within some time frame, while the diffusion component accommodates random shocks. The drift component has substantial practical significance but accurate estimation is typically challenging and has met with limited success in the existing literature except over large time spans. This paper explores a comprehensive range of drift-diffusion models that include constant, linear, trending, and bursting drift. Conditions are identified under which realized squared drift is a reliable tool for gauging integrated squared drift when the time span Tn is large …
Gmm Estimation With Brownian Kernels Applied To Income Inequality Measurement, Jin Seo Cho, Peter C. B. Phillips
Gmm Estimation With Brownian Kernels Applied To Income Inequality Measurement, Jin Seo Cho, Peter C. B. Phillips
Research Collection School Of Economics
In GMM estimation, it is well known that if the moment dimension grows with the sample size, the asymptotics of GMM differ from the standard finite dimensional case. The present work examines the asymptotic properties of infinite dimensional GMM estimation when the weight matrix is formed by inverting Brownian motion or Brownian bridge covariance kernels. These kernels arise in econometric work such as minimum Cramér–von Mises distance estimation when testing distributional specification. The properties of GMM estimation are studied under different environments where the moment conditions converge to a smooth Gaussian or non-differentiable Gaussian process. Conditions are also developed for …
Competition Through Entry Fees Between Auctions For Differentiated Objects, Massimiliano Landi, Domenico Menicucci, Domenico Colucci
Competition Through Entry Fees Between Auctions For Differentiated Objects, Massimiliano Landi, Domenico Menicucci, Domenico Colucci
Research Collection School Of Economics
This paper considers two competing auctions with objects differentiated according to the random utility framework introduced in Perloff and Salop (1985): bidders privately observe their values for the two objects, and values are ex ante i.i.d. across objects and across bidders. For the case of uniform distribution of values, we show that competition under entry fees is less intense than competition under reserve prices in the sense that sellers are better off, and bidders are worse off when competition takes place with entry fees rather than under reserve prices. The key difference between the two settings is that undercutting under …
Weak Identification Of Long Memory With Implications For Volatility Modeling;, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Weak Identification Of Long Memory With Implications For Volatility Modeling;, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.
Max-Share Misidentification, Liyu Dou, Paul Ho, Thomas Lubik
Max-Share Misidentification, Liyu Dou, Paul Ho, Thomas Lubik
Research Collection School Of Economics
While max-share identiffcation has become increasingly popular in a wide range of applications, we show that its validity requires necessary and suffffcient conditions that are rarely satisffed in practice—the target variable’s response to the target shock must be (i) orthogonal to its responses to untargeted shocks and (ii) larger than combinations of those responses. Imposing additional restrictions on the target shock weakens but does not fully eliminate these conditions. We show that in practice, the weight max-share places on an identiffed untargeted shock can be obtained by projecting the response to that shock on the max-share response. We also theoretically …
Speculative Bubbles In The Recent Ai Boom: Nasdaq And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi
Speculative Bubbles In The Recent Ai Boom: Nasdaq And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi
Research Collection School Of Economics
The recent artificial intelligence (AI) boom covers a period of rapid innovation and wide adoption of AI intelligence technologies across diverse industries. These developments have fueled an unprecedented frenzy in the Nasdaq, with AI-focused companies experiencing soaring stock prices that raise concerns about speculative bubbles and real-economy consequences. Against this background, this study investigates the formation of speculative bubbles in the Nasdaq stock market with a specific focus on the so-called Magnificent Seven (Mag-7) individual stocks during the AI boom, spanning the period from January 2017 to January 2025. We apply the real-time PSY bubble detection methodology of Phillips et …
Policy Evaluation With Nonlinear Trended Outcomes: Covid-19 Vaccination Rates In The United States, Lynn Bergeland Morgan, Peter C. B. Phillips, Donggyu Sul
Policy Evaluation With Nonlinear Trended Outcomes: Covid-19 Vaccination Rates In The United States, Lynn Bergeland Morgan, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend components. If a policy change shifts trend paths of outcome variables, TWFE estimation can distort results and invalidate inference, especially in a context of evolving policy decisions. A robust solution is proposed by allowing for dynamic club membership empirically using a relative convergence test procedure. The determinants of respective club memberships are assessed by panel ordered logit regressions. The approach allows for policy evolution and shifts in outcomes according to a convergence cluster framework with transitions over time and …
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a nonparametric test for general functional inequalities that include conditional moment inequalities as a special case. It is shown that the test controls size uniformly over a large class of distributions for observed data, importantly allowing for general forms of time series dependence. New results on uniform growing dimensional Gaussian coupling for general mixingale processes are developed for this purpose, which readily accommodate most applications in economics and finance. The proposed method is applied in a portfolio evaluation context to test for “all-weather” portfolios with uniformly superior conditional Sharpe ratio functions.
High Frequency Factor Analysis With Partially Observable Factors, Dachuan Chen, Wenqi Lu, Siyu Xie
High Frequency Factor Analysis With Partially Observable Factors, Dachuan Chen, Wenqi Lu, Siyu Xie
Research Collection School Of Economics
This paper considers a novel factor structure – Partially Observable Factor Model – where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously.When the observable factors are exogenous, we provide the estimation theory for the integrated eigenvalues of the residual covariance matrix, which including the bias-corrected estimator, central limit theorem …
Limit Theory For Local Polynomial Estimation Of Functional Coefficient Models With Possibly Integrated Regressors, Ying Wang, Peter C. B. Phillips
Limit Theory For Local Polynomial Estimation Of Functional Coefficient Models With Possibly Integrated Regressors, Ying Wang, Peter C. B. Phillips
Research Collection School Of Economics
Limit theory for functional coefficient cointegrating regression was recently found to be considerably more complex than earlier understood. The issues were explained and correct limit theory derived for the kernel weighted local level estimator in Phillips and Wang (2023b). The present paper provides complete limit theory for the general kernel weighted local th order polynomial estimators of the functional coefficient and the coefficient derivatives. Both stationary and nonstationary regressors are allowed. Implications for bandwidth selection are discussed. An adaptive procedure to select the fit order is proposed and found to work well. A robust -ratio is constructed following the new …
Valuation Of Crypto Assets On Blockchain With Deep Learning Approach, Xi Zhou, Yin Pang, Esther Ying Yang, Jing Rong Goh, Shaun Shuxun Wang
Valuation Of Crypto Assets On Blockchain With Deep Learning Approach, Xi Zhou, Yin Pang, Esther Ying Yang, Jing Rong Goh, Shaun Shuxun Wang
Research Collection School Of Economics
With the rapid expansion of the blockchain ecosystem, crypto asset valuation has become an essential area of study for investors and institutions. Here, we introduce a deep learning framework that was designed to predict the value index of crypto assets by integrating intrinsic value variables and decomposing market prices into value and sentiment components. The Crypto Asset Value-indexing Model (CAVM) was applied to Ethereum’s cryptocurrency ETH to demonstrate its effectiveness. Four econometric tests were conducted to verify the informativeness, predictiveness, and reasonability of the generated value indices, as well as the efficiency of price decomposition. Our findings suggested that the …
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information, Shujie Ma, Po-Yao Niu, Yichong Zhang, Yinchu Zhu
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information, Shujie Ma, Po-Yao Niu, Yichong Zhang, Yinchu Zhu
Research Collection School Of Economics
This article investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unobserved latent factors; the other part models the effects of the observed covariates through a coefficient matrix which is composed of high-dimensional column vectors. We model the observational pattern of the responses through a logistic regression of the covariates, and allow its probability to go to zero as the sample size increases. We apply an iterative least squares (LS) estimation approach in our considered context. The iterative LS …
Estimation And Inference In A Possibly Multicointegrated System With A Fixed Number Of Instruments, Yixiao Sun, Peter C. B. Phillips, Igor L. Kheifets
Estimation And Inference In A Possibly Multicointegrated System With A Fixed Number Of Instruments, Yixiao Sun, Peter C. B. Phillips, Igor L. Kheifets
Research Collection School Of Economics
This paper shows that the mixed normal asymptotic limit of the trend IV estimator with a fixed number of deterministic instruments (fTIV) holds in both singular (multicointegrated) and nonsingular cointegration systems, thereby relaxing the exogeneity condition in (Phillips and Kheifets, 2024, Theorem 1(ii)). The mixed normality of the limiting distribution of fTIV allows for asymptotically pivotal F and t tests about the cointegration parameters and for simple efficiency comparisons of the estimators for different numbers K of instruments, as well as comparisons with the trend IV estimator when K→∞ with the sample size.
Food Self-Sufficiency And Building-Integrated Urban Agriculture: Lessons From Singapore, Tomoki Fujii, Christoph Waibel, Xinyi Du, Zhongming Shi
Food Self-Sufficiency And Building-Integrated Urban Agriculture: Lessons From Singapore, Tomoki Fujii, Christoph Waibel, Xinyi Du, Zhongming Shi
Research Collection School Of Economics
Singapore aims to achieve 30 percent food self-sufficiency by 2030, known as the 30-by-30 target, and this paper reviews Singapore’s changing landscape for urban agriculture and its recent progress towards the 30-by-30 goal, highlighting key challenges such as land constraints, high production costs, and resource limitations. Building-integrated agriculture (BIA), which utilizes building surfaces such as rooftops, façades, and balconies for food production within urban environments, is examined as a potential way to increase self-sufficiency through a simulation. Despite the BIA’s potential, practical issues—including regulatory concerns and infrastructure limitations—must be addressed before it can be implemented at scale. Insights from Singapore’s …
Fintech’S Rise Reshaping Asean’S Financial Future, Swee Liang Tan
Fintech’S Rise Reshaping Asean’S Financial Future, Swee Liang Tan
Research Collection School Of Economics
Fintech can offset the diminishing effects of bank lending on economic growth. By leveraging innovative technologies, fintech companies can reduce transaction costs, address information asymmetry issues and streamline processes such as customer evaluation and risk assessment. This strategic collaboration between banks and fintech firms can lead to improved efficiency and effectiveness in financial services, such as pressuring traditional banks to innovate in areas such as customer interface and processes, ultimately boosting economic growth.
How Do Insurance Companies Manage Reserves? Evidence From Reserve Errors Across Lines Of Business And Accident Years, Jing Rong Goh, Shinichi Kamiya, Pingyi Lou
How Do Insurance Companies Manage Reserves? Evidence From Reserve Errors Across Lines Of Business And Accident Years, Jing Rong Goh, Shinichi Kamiya, Pingyi Lou
Research Collection School Of Economics
We study the question of how insurance companies manage reserves. Specifically, we investigate how managerial incentives affect insurers’ reserving practice across lines of business (LOBs) and accident years (AYs). Because the tax discount factor the tax authority assigns varies across LOBs and AYs, insurers with stronger tax-saving incentives will be inclined to manage reserves across both LOBs and AYs. In contrast, since the Risk Based Capital (RBC) regime specifies different industry worst-case factors across LOBs, insurers with stronger incentives to increase their RBC ratio will be inclined to manage reserves across LOBs. Regarding income-smoothing incentives, only the overall level (and …
Redesigning Home Reversion Products To Empower Retirement For Singapore's Public Flat Owners, Koon Shing Kwong, Jing Rong Goh, Jordan Jie Xin Lee, Ting Lin Collin Chua
Redesigning Home Reversion Products To Empower Retirement For Singapore's Public Flat Owners, Koon Shing Kwong, Jing Rong Goh, Jordan Jie Xin Lee, Ting Lin Collin Chua
Research Collection School Of Economics
This paper introduces an innovative sell-type home reversion product aimed at monetizing Singapore’s public flats, serving as a new alternative to the existing Singapore Lease Buyback Scheme (LBS). This new product not only retains the LBS’s guaranteed period of residence in the property along with life annuity incomes but also enhances the product features to meet specific homeowner needs, including the ability to age in place, flexibility in retaining part of the property, options for bequests, and guaranteed principal return. By incorporating these additional features, the new product seeks to stimulate greater demand for monetizing public flats among asset-rich but …
Gauging Growth Risk In An International Financial Centre: Some Evidence From Singapore, Hwee Kwan Chow
Gauging Growth Risk In An International Financial Centre: Some Evidence From Singapore, Hwee Kwan Chow
Research Collection School Of Economics
This paper applies the growth-at-risk framework proposed by Adrian et al. (2019) to Singapore, an international financial centre whereby financial shocks are intermediated away quickly. We gauge near-term risks around growth projections taken from the survey of professional forecasters by accounting for financial stress in both local and global financial markets, as well as worldwide economic uncertainty. The conditioning variables are first linked to future growth through quantile regressions, and the estimated quantiles are fitted with skew t-distributions to produce full predictive distributions. Scenario analysis reveals that greater local financial strain tends to widen the uncertainty of growth outlook, higher …
Optimal Har Inference, Liyu Dou
Optimal Har Inference, Liyu Dou
Research Collection School Of Economics
This paper addresses the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference for a scalar parameter of interest, under the assumption of a known upper bound on data persistence. Finite-sample optimal tests are derived within the Gaussian location model, revealing that robustness-efficiency tradeoffs are primarily determined by the maximal persistence. With a suitable adjustment to the critical value, the equal-weighted cosine (EWC) test emerges as nearly optimal, wherein the long-run variance is estimated through projections onto q type II cosines. This approach establishes a direct link between the choice of q and persistence assumptions, accompanied by adjustments to the …