Modelling Comovements of Economic Time Series: A Selective Survey

Centoni, Marco; Cubadda, Gianluca

doi:10.2139/ssrn.1949657

Cited by 3 publications

(2 citation statements)

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“…Moreover, our approach allows for serial correlation that is common to variables included in the analysis. This makes empirical models more parsimonious and improves the short-run structural analysis (see, e.g., Centoni and Cubadda, 2011).…”

Section: Introductionmentioning

confidence: 99%

Insulating property of the flexible exchange rate regime: A case of Central and Eastern European countries

Dąbrowski

Wróblewska

2020

International Economics

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We examine the insulating property of flexible exchange rate in CEE economies using the fact that they have adopted different regimes. A set of Bayesian structural VAR models with common serial correlations is estimated on data spanning 1998q1-2015q4. The long-term identifying restrictions are derived from a macroeconomic model. We find that irrespective of the exchange rate regime output is driven mainly by real shocks. Its reactions to these shocks, however, are substantially stronger under less flexible regimes, whereas the responses to nominal shocks are similar. Hence, the insulating property of flexible regimes can reduce the costs from economic shocks.

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Section: Introductionmentioning

confidence: 99%

Insulating property of the flexible exchange rate regime: A case of Central and Eastern European countries

Dąbrowski

Wróblewska

2020

International Economics

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“…Univariate time series log values generated by a single sensor, whereas multivariate time series record signals from multiple sensors simultaneously. Time series are used to study time-varying phenomena in many fields: in the economy [2] (e.g., stock price trends), in medicine [3] (e.g., the progress of health variables) or in industry [4] (e.g., the status or energy consumption of a machine). Given a time series dataset, different tasks can be performed to predict a specific attribute or event at a given timestamp or assign a label to a particular observation.…”

Section: Introductionmentioning

confidence: 99%