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Advances in Forecasting

Paper Session

Friday, Jan. 3, 2025 8:00 AM - 10:00 AM (PST)

San Francisco Marriott Marquis, Foothill B
Hosted By: International Association of Applied Econometrics
  • Chair: Sílvia Gonçalves, McGill University

Does Noise Hurt Economic Forecasts?

Yuan Liao
,
Rutgers University
Xinjie Ma
,
National University of Singapore
Andreas Neuhierl
,
Washington University-St. Louis
Zhentao Shi
,
Chinese University of Hong Kong

Abstract

No, it can help. To support our argument, we show that economic forecast models driven by latent factors are not sparse. This fact allows us to establish a compelling result that including noise in predictions yields greater benefits than excluding it, which contradicts the common practice of removing noise from predictors via variable selection techniques. Empirically, we apply a pseudo-OLS approach to four real-data applications including forecasting the U.S. inflation rate. The performance of our method that interpolates the in-sample data surpasses many commonly used models that rely on dimension reduction.

Forecasted Treatment Effects with Short Panels

Irene Botosaru
,
McMaster University
Raffaella Giacomini
,
University College London
Martin Weidner
,
University of Oxford

Abstract

N/A

Testing Forecast Accuracy With Penalised Regression Models

Valentina Corradi
,
University of Surrey
Jack Fosten
,
King's College London
Daniel Gutknecht
,
Goethe University Frankfurt

Abstract

N/A

Bootstrapping Out-of-Sample Predictability Tests with Real-Time Data

Sílvia Gonçalves
,
McGill University
Michael McCracken
,
Federal Reserve of St. Louis
Yongxu Yao
,
McGill University

Abstract

N/A
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General
  • C5 - Econometric Modeling