Three Essays on Updating Forecasts in Vector Autoregression Models
Date
2010-04
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Queen's University, Department of Economics
Abstract
Forecasting firms' earnings has long been an interest of market participants and aca-
demics. Traditional forecasting studies in a multivariate time series setting do not
take into account that the timing of market data release for a speci¯c time period
of observation is often spread over several days or weeks. This thesis focuses on the
separation of announcement timing or data release and the use of econometric real-
time methods, which we refer to as an updated vector autoregression (VAR) forecast,
to predict data that have yet to be released. In comparison to standard time series
forecasting, we show that the updated forecasts will be more accurate the higher the
correlation coe±cients among the standard VAR innovations are. Forecasting with
the sequential release of information has not been studied in the VAR framework, and
our approach to U.S. nonfarm payroll employment and the six Canadian banks shows
its value. By using the updated VAR forecast, we conclude that there are relative ef-
¯ciency gains in the one-step-ahead forecast compared to the ordinary VAR forecast,
and compared to professional consensus forecasts. Thought experiments emphasize
that the release ordering is crucial in determining forecast accuracy.
Description
Keywords
Citation
Zhu, Hui. "Three Essays on Updating Forecasts in Vector Autoregression Models". Thesis, Queen's University, Department of Economics (2010).