TITLE:
Selection of Macroeconomic Forecasting Models: One Size Fits All?
AUTHORS:
Yunyun Lv
KEYWORDS:
Variable Selection Specific to the Horizon, Sample Mean, Principal Components, Out-of-Sample Forecasting
JOURNAL NAME:
Theoretical Economics Letters,
Vol.7 No.4,
May
8,
2017
ABSTRACT: The
main distinction between this paper and traditional approach is the assumption
that variables affect the economy through different horizons. Under this alternative
hypothesis, a variable considered as an unimportant detail from a short-horizon
perspective may become an essential factor in a long-horizon standpoint, this
paper, therefore, suggests selecting variables specific to the horizon. My
findings confirm that a model that allows the variables particular to the
horizon has a lower Schwarz Bayesian Information Criterion (SBIC) value than a
model that does not. My outcomes also show that the vector autoregression (VAR)
model in general forecasts poorly compared with my approach. Likewise, I
contribute to the literature by setting predictions equal to the sample mean as
a benchmark and showing that the out-of-sample forecasts of the VAR model with
lag length higher than one fail to outperform the sample mean. Additionally, I
select principal components derived from 190 different time series to forecast
a time series as the time horizon varies. Again, the results show that some of
the principal components may be more important at some horizons than at others,
thus I suggest selecting the principal components in a factor-augmented VAR
(FAVAR) model specific to the horizon. According to above results, I conclude
that long-horizon and deep-rooted economic problems cannot be fixed with
short-horizon and surface-level interventions. I also reach my argument via simulation.