About

Pierre Perron is a Professor in the Department of Economics at Boston University. The page primarily lists his PhD students, their advisors, and committee members, but does not provide specific details about his research focus, background, or key contributions. Therefore, there is no additional biographical or research information available on this page.

Research topics

• Computer Science
• Oceanography
• Economics
• Environmental science
• Climatology
• Atmospheric sciences
• Geology
• Statistics
• Geography
• Econometrics
• Accounting
• Mathematics

Selected publications

• An Improved Procedure for Retrospectively Dating the Emergence and Collapse of Bubbles

  Journal of Time Series Analysis · 2025-01-01

  articleOpen accessSenior author

  ABSTRACT This article proposes a new ordinary least squares (OLS)‐based procedure for retrospectively dating the emergence and collapse of bubbles. We first consider a data generating process that entails a switch from a unit root regime to an explosive regime followed by a collapse and subsequent return to unit root behavior. We demonstrate analytically that the standard OLS estimates are inconsistent and date both the origination and implosion points with a delay in large samples. A simple modification that involves omitting the residual corresponding to the implosion date is shown to yield consistent estimates. We also develop an efficient dating algorithm that can accommodate a framework with multiple bubbles. The algorithm exploits the explicit form of the unit root restrictions to directly embed them into the recursive optimization problem which obviates the need to rely on an iterative scheme that requires initial values. Extensive simulation experiments indicate that our proposed procedure typically delivers estimates with lower bias and root mean squared error relative to competing alternatives. An empirical illustration is included.

  PublisherOA PDFDOI

• Continuous Record Asymptotics for Change‐Point Models

  Journal of Time Series Analysis · 2025-02-13 · 7 citations

  articleSenior author

  ABSTRACT In the context of a linear regression model with a single break point, we develop a continuous record asymptotic framework to build inference methods for the break date. We have observations with a sampling frequency over a fixed‐time horizon and let with while keeping the time span fixed. We consider the least‐squares estimate of the break date and establish consistency and convergence rate. We provide a limit theory for shrinking magnitudes of shifts and locally increasing variances. The asymptotic distribution corresponds to the location of the extremum of a function of the quadratic variation of the regressors and of a Gaussian‐centered martingale process over a certain time interval. We can account for the asymmetric informational content provided by the pre‐ and post‐break regimes and show how the location of the break and shift magnitude are key ingredients in shaping the distribution. We consider a feasible version based on plug‐in estimates, which provides a very good approximation to the finite sample distribution. We use the concept of the Highest Density Region to construct confidence sets. Overall, our method is reliable and delivers accurate coverage probabilities and the relatively short average length of the confidence sets. Importantly, it does so irrespective of the size of the break.

  PublisherDOI

• Synergies Between Observed Warming and ENSO Episodes on Extreme Events

  Annals of the New York Academy of Sciences · 2025-11-04 · 1 citations

  articleOpen access

  El Niño/Southern Oscillation (ENSO) is the dominant interannual variability mode of the global climate system with significant effects on a variety of weather conditions, including extremes. Past events illustrate the severe societal consequences this phenomenon has through weather disasters, food security, health, economic growth, migration, and conflict. ENSO's interactions with global warming are not well understood, although they can lead to significant changes in the characteristics of extreme events. Climate conditions in 2024/2025 may favor widespread severe extreme events with global temperature anomalies nearing or surpassing 1.5°C and a transition from strong El Niño to La Niña conditions. Here, we show that current warming has amplified the effects of ENSO on temperature and precipitation extremes worldwide. Results show that warming has produced a considerable amplification of the effects of ENSO episodes over such extremes, as well as extensively modified spatial patterns. We show that considerable shares of the population, gross domestic product, agriculture, and ecosystems now face a higher risk from extreme events due to the interactions between increased anthropogenic forcing and ENSO.

  PublisherOA PDFDOI

• THEORY OF LOW FREQUENCY CONTAMINATION FROM NONSTATIONARITY AND MISSPECIFICATION: CONSEQUENCES FOR HAR INFERENCE

  Econometric Theory · 2024-12-27 · 11 citations

  articleOpen accessSenior authorCorresponding

  We establish theoretical results about the low frequency contamination (i.e., long memory effects) induced by general nonstationarity for estimates such as the sample autocovariance and the periodogram, and deduce consequences for heteroskedasticity and autocorrelation robust (HAR) inference. We present explicit expressions for the asymptotic bias of these estimates. We show theoretically that nonparametric smoothing over time is robust to low frequency contamination. Nonstationarity can have consequences for both the size and power of HAR tests. Under the null hypothesis there are larger size distortions than when data are stationary. Under the alternative hypothesis, existing LRV estimators tend to be inflated and HAR tests can exhibit dramatic power losses. Our theory indicates that long bandwidths or fixed- b HAR tests suffer more from low frequency contamination relative to HAR tests based on HAC estimators, whereas recently introduced double kernel HAC estimators do not suffer from this problem. We present second-order Edgeworth expansions under nonstationarity about the distribution of HAC and DK-HAC estimators and about the corresponding t -test in the regression model. The results show that the distortions in the rejection rates can be induced by time variation in the second moments even when there is no break in the mean.

  PublisherOA PDFDOI

• Gls-Iv for Time Series Regressions with Application To the "New Keynesian Phillips Curve"

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Econometrics Volume 2

  WORLD SCIENTIFIC eBooks · 2024-12-29

  book1st authorCorresponding
  PublisherDOI

• Change-point analysis of time series with evolutionary spectra

  Journal of Econometrics · 2024-06-01 · 9 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Econometrics Volume 1

  WORLD SCIENTIFIC eBooks · 2024-12-29

  book1st authorCorresponding
  PublisherDOI

• Prewhitened long-run variance estimation robust to nonstationarity

  Journal of Econometrics · 2024-05-01 · 6 citations

  articleSenior author
  PublisherDOI

• Author response for "On the persistence of near‐surface temperature dynamics in a warming world"

  2023-09-13

  peer-review
  PublisherDOI

Recent grants

• Structural Changes, Level Shifts in Variance and the Frequency of Permanent Shocks

  NSF · $216k · 2007–2011

Frequent coauthors

• Francisco Estrada

  Vrije Universiteit Amsterdam

  41 shared

• Alessandro Casini

  University of Rome Tor Vergata

  32 shared

• Zhongjun Qu

  31 shared

• Yohei Yamamoto

  Hitotsubashi University

  29 shared

• Serena Ng

  Columbia University

  24 shared

• Mohitosh Kejriwal

  22 shared

• Dukpa Kim

  16 shared

• Tomoyoshi Yabu

  Keio University

  14 shared

Labs

• Pierre Perron LabPI

Education

• Ph.D.

  Yale University

Awards & honors

• Fellow of the Econometric Society
• Fellow of the Journal of Econometrics
• Fellow of the International Association for Applied Economet…