ORCID Profile
0000-0003-2341-0451
Current Organisations
Yale University
,
The University of Auckland
,
Singapore Management University School of Economics
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Publisher: Elsevier BV
Date: 11-2018
Publisher: Cambridge University Press (CUP)
Date: 18-12-2014
DOI: 10.1017/S0266466614000917
Abstract: This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a,b, Econometric Theory 25, 710–738, Econometrica 77, 1901–1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory.
Publisher: The Econometric Society
Date: 2009
DOI: 10.3982/ECTA7732
Publisher: Wiley
Date: 12-06-2019
DOI: 10.1111/OBES.12307
Publisher: Elsevier BV
Date: 03-2018
Publisher: Cambridge University Press (CUP)
Date: 07-06-2018
DOI: 10.1017/S0266466617000202
Abstract: Expansion and collapse are two key features of a financial asset bubble. Bubble expansion may be modeled using a mildly explosive process. Bubble implosion may take several different forms depending on the nature of the collapse and therefore requires some flexibility in modeling. This paper first strengthens the theoretical foundation of the real time bubble monitoring strategy proposed in Phillips, Shi and Yu (2015a,b, PSY) by developing analytics and studying the performance characteristics of the testing algorithm under alternative forms of bubble implosion which capture various return paths to market normalcy. Second, we propose a new reverse s le use of the PSY procedure for detecting crises and estimating the date of market recovery. Consistency of the dating estimators is established and the limit theory addresses new complications arising from the alternative forms of bubble implosion and the endogeneity effects present in the reverse regression. A real-time version of the strategy is provided that is suited for practical implementation. Simulations explore the finite s le performance of the strategy for dating market recovery. The use of the PSY strategy for bubble monitoring and the new procedure for crisis detection are illustrated with an application to the Nasdaq stock market.
Publisher: Wiley
Date: 09-09-2018
DOI: 10.1111/JTSA.12427
Publisher: Wiley
Date: 07-2023
DOI: 10.1111/JOES.12430
Abstract: Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the U.S. housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative “thermometer” for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long‐term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy‐to‐implement reduced‐form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the endogenous instrumental variable based method (IVX) to estimate the reduced‐form model to reduce the finite s le bias which arises from highly persistent regressors and endogeneity. The recursive evolving test proposed by Phillips, Shi, and Yu (PSY) is applied to the estimated nonfundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY‐IVX. An empirical application to the eight Australian capital city housing markets over the period 1999–2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Singapore
No related grants have been discovered for Peter C B Phillips.