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SUMMARY:Statistics Seminar - Sebastien Laurent
DTSTART:20241115
DTEND:20241115
DESCRIPTION:
Sebastien Laurent

IAE Aix-Marseille

Asymptotics for penalized QMLEs of time series regressions

We examine a linear regression model applied to the components of a time series\, aiming to identify time-varying\, constant as well as zero conditional beta coefficients. To address the non-identifiability of parameters when a conditional beta is constant\, we employ a lasso-type estimator. This penalized estimator simplifies the model by shrinking the estimates when the beta is constant. Given that the model accommodates conditional heteroskedasticity and the relevant regressors are unknown\, the total number of parameters to estimate can be quite large. To manage this complexity\, we propose a multistep estimator that first captures the dynamics of the regressors before estimating the dynamics of the betas. This strategy breaks down a high-dimensional optimization problem into several lower-dimensional ones. Since we avoid making strict parametric assumptions about the innovation distributions\, we use Quasi-Maximum Likelihood (QML) estimators. The non-Markovian nature of the global model means that standard convex optimization results cannot be applied. Nevertheless\, we analyze the asymptotic distribution of the multistep lasso estimator and its adaptive version\, deriving bounds on the maximum value of the penalty term. We also propose a nonlinear coordinate-wise descent algorithm\, which is demonstrated to find stationary points of the objective function. The finite-sample properties of these estimators are further explored through a Monte Carlo simulation and illustrated with an application to financial data.


DTSTAMP:20241110
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SUMMARY: Applied Statistics Workshop
DTSTART:20241122
DTEND:20241122
DESCRIPTION:Upcoming news

DTSTAMP:20241110
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SUMMARY:Statistics Seminar - Thomas Nagler
DTSTART:20241129
DTEND:20241129
DESCRIPTION:
Thomas Nagler

LMU Munich

An new bootstrap for time series
Abstract :
Resampling methods such as the bootstrap have proven invaluable in statistics and machine learning. However\, the applicability of traditional bootstrap methods is limited when dealing with large streams of dependent data\, such as time series or spatially correlated observations. In this paper\, we propose a novel bootstrap method that is designed to account for data dependencies and can be executed online\, making it particularly suitable for real-time applications. This method is based on an autoregressive sequence of increasingly dependent resampling weights. We prove the theoretical validity of the proposed bootstrap scheme under general conditions. We demonstrate the effectiveness of our approach through extensive simulations and show that it provides reliable uncertainty quantification even in the presence of complex data dependencies. Further extensions to nonstationary time series will be discussed.

DTSTAMP:20241110
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BEGIN:VEVENT
SUMMARY:Statistics Seminar - Vlad Stefan Barbu
DTSTART:20241206
DTEND:20241206
DESCRIPTION:
Vlad Stefan Barbu

Université de Rouen

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Upcoming news

DTSTAMP:20241110
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BEGIN:VEVENT
SUMMARY: Applied Statistics Workshop
DTSTART:20241213
DTEND:20241213
DESCRIPTION:


Upcoming news




DTSTAMP:20241110
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