Nowcasting the Czech Trade Balance
Oxana Babecká-Kucharčuková and
Jan Bruha ()
Working Papers from Czech National Bank
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
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Persistent link: https://EconPapers.repec.org/RePEc:cnb:wpaper:2016/11
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