dc.contributor.author |
Gupta, Rangan
|
|
dc.contributor.author |
Kabundi, Alain
|
|
dc.contributor.author |
Miller, Stephen M.
|
|
dc.contributor.author |
Uwilingiye, Josine
|
|
dc.date.accessioned |
2014-07-22T13:08:06Z |
|
dc.date.available |
2014-07-22T13:08:06Z |
|
dc.date.issued |
2014-06 |
|
dc.description.abstract |
We use several models using classical and Bayesian methods to forecast
employment for eight sectors of the US economy. In addition to using standard vectorautoregressive
and Bayesian vector autoregressive models, we also augment these
models to include the information content of 143 additional monthly series in some
models. Several approaches exist for incorporating information from a large number of
series.We consider two multivariate approaches—extracting common factors (principal
components) and Bayesian shrinkage. After extracting the common factors, we use
Bayesian factor-augmented vector autoregressive and vector error-correction models,
as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive models.
For an in-sample period of January 1972 to December 1989 and an out-of-sample
period of January 1990 to March 2010, we compare the forecast performance of the
alternative models. More specifically, we perform ex-post and ex-ante out-of-sample
forecasts from January 1990 through March 2009 and from April 2009 through March
2010, respectively. We find that factor augmented models, especially error-correction
versions, generally prove the best in out-of-sample forecast performance, implying that
in addition to macroeconomic variables, incorporating long-run relationships along
with short-run dynamics play an important role in forecasting employment. Forecast
combination models, however, based on the simple average forecasts of the various
models used, outperform the best performing individual models for six of the eight
sectoral employment series. |
en_US |
dc.description.librarian |
hb2014 |
en_US |
dc.description.uri |
http://link.springer.com/journal/10260 |
en_US |
dc.identifier.citation |
Gupta, R, Kabundi, A, Miller, SM & Uwilingiye, J 2014, 'Using large data sets to forecast sectoral employment', Statistical Methods and Applications, vol. 23, no. 2, pp. 229-264. |
en_US |
dc.identifier.issn |
1618-2510 (print) |
|
dc.identifier.issn |
1613-981X (online) |
|
dc.identifier.other |
10.1007/s10260-013-0243-6 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/40932 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Springer |
en_US |
dc.rights |
© Springer-Verlag Berlin Heidelberg 2013. The original publication is available at : http://link.springer.com/journal/10260. |
en_US |
dc.subject |
Sectoral employment |
en_US |
dc.subject |
Forecasting |
en_US |
dc.subject |
Factora augmented models |
en_US |
dc.subject |
Large-scale BVAR models |
en_US |
dc.subject |
Bayesian Vector Autoregressions (BVAR) |
en_US |
dc.title |
Using large data sets to forecast sectoral employment |
en_US |
dc.type |
Postprint Article |
en_US |