Abstract: | Forecasting
firms' earnings has long been an interest of market participants and
academics. Traditional forecasting studies in a multivariate time series
setting do not take into account that the timing of market data release
for a specific 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 coefficients 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
efficiency 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. |
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