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CNB > Economic research > Research publications > CNB Working paper series > 2016 > Nowcasting the Czech Trade Balance

Nowcasting the Czech Trade Balance

Oxana Babecká Kucharčuková, Jan Brůha

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.

JEL codes: C53, C55, F17

Keywords: Dynamic factor models, elastic net regression, mixed–frequency data, nowcasting, principal component analysis, state space models, trade balance

Issued: December 2016

Download: CNB WP No. 11/2016 (pdf, 1.2 MB)