Economics at your fingertips  

Nowcasting the Czech Trade Balance

Oxana Babecká-Kucharčuková and Jan Bruha ()

Working Papers from Czech National Bank

Abstract: In this paper we are interested in nowcasting and short-run forecasting of the main external trade variables. We consider four empirical methods: principal component regression, elastic net regression, the dynamic factor model and partial least squares. We discuss the adaptation of those methods to asynchronous data releases and to the mixed-frequency set-up. We contrast them with a set of univariate benchmarks. We find that for variables in value terms (both nominal and real), elastic net regression typically yields the most accurate predictions, followed by the dynamic factor model and then by principal components. For export and import prices, univariate techniques seem to have the higher precision for backcasting and nowcasting, but for short-run forecasting the more sophisticated methods tend to produce more accurate forecasts. Here again, elastic net regression dominates the other methods.

Keywords: Dynamic factor models; elastic net regression; mixed-frequency data; nowcasting; principal component analysis; state space models; trade balance (search for similar items in EconPapers)
JEL-codes: C53 C55 F17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-int
Date: 2016-12
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) ... wp/cnbwp_2016_11.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from Czech National Bank Contact information at EDIRC.
Bibliographic data for series maintained by Jan Babecky ().

Page updated 2019-12-03
Handle: RePEc:cnb:wpaper:2016/11