Clearing 2021
Event

Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach by Ulrich Hounyo

Join Dr Ulrich Hounyo for an online event, part of the Econometrics Research Seminar Series, Summer Term 2021

  • Wed 9 Jun 21

    16:00 - 17:30

  • Colchester Campus

    Zoom

  • Event speaker

    Dr Ulrich Hounyo

  • Event type

    Lectures, talks and seminars
    Econometrics Research Seminar Series

  • Event organiser

    Economics, Department of

Join Dr Ulrich Hounyo as they present their research on Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach.

Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach by Ulrich Hounyo

Join us for this Econometrics Research Seminar, Summer Term 2021

Dr Ulrich Hounyo from the Department of Economics, University at Albany, State University of New York, will present their research on Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach.

Abstract

This paper considers bootstrap inference in model averaging for predictive regressions. We first show that a naïve bootstrap approach, which consists of stacking all residuals at time t into a vector, and then resampling these cross-sectional vectors of residuals over time is invalid in the context of model averaging. The naïve approach induces a bias-related term in the bootstrap variance of averaging estimators. The failure of this naive bootstrap is due to the discrepancies between residuals from the full model (where all regressors are included) and residuals from other approximating models (with more omitted variable bias).

We then propose and justify two general fixed-design residual-based bootstrap resampling approaches for model averaging in predicting regressions. In a local asymptotic framework, we show the validity of the bootstrap in estimating the variance of a combined forecast and the asymptotic covariance matrix of a combined parameter vector with fixed weights. Our two proposed methods - the general blocking-based residual resampling and the general dependent wild-based residual resampling - can preserve nonparametrically the cross-sectional dependence between different models and the time series dependence in the errors simultaneously. The finite sample performance of these methods are assessed via Monte Carlo simulation. We illustrate our approach using an empirical study of the Taylor rule equation with 24 alternative specifications. 

This seminar will be held via webinar on Zoom at 4pm on Wednesday 9th June. This event is open to all levels of study and is also open to the public. To register your place and gain access to the webinar, please contact the seminar organisers.

This event is part of the Econometrics Research Seminar Series.

 

Contact the seminar organisers
Contact the seminar organisers
Neslihan Sakarya Department of Economics
University of Essex
Department of Economics University of Essex
Wivenhoe Park, Colchester, Essex, CO4 3SQ
Telephone: 01206 872728

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