Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sophisticated and small versus simple and sizeable: When does it pay off
  to introduce drifting coefficients in Bayesian VARs?   We assess the relationship between model size and complexity in the
time-varying parameter VAR framework via thorough predictive exercises for the
Euro Area, the United Kingdom and the United States. It turns out that
sophisticated dynamics through drifting coefficients are important in small
data sets while simpler models tend to perform better in sizeable data sets. To
combine best of both worlds, novel shrinkage priors help to mitigate the curse
of dimensionality, resulting in competitive forecasts for all scenarios
considered. Furthermore, we discuss dynamic model selection to improve upon the
best performing individual model for each point in time.

2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga