Ebook Structural heterogeneity or asymmetric shocks? Poland and the euro area through the lens of a two&country DSGE model
This paper presents an estimated two country dynamic stochastic general equilibrium (DSGE) model for Poland and the euro area and applies it for assessing the degree of heterogeneity between these two regions. In particular, relative relevance of two sources of heterogeneity are examined: differences in structural parameters and asymmetry of shocks hitting the two economies.
While comparing a set of structural and stochastic characteristics of the Polish economy with those prevailing in the euro area seems to be an interesting task in itself, our research is additionally motivated by Polandms prospective entry to the European Monetary Union (EMU). A standard and well&known implication of the optimal currency area (OCA) theory is that asymmetric temporary shocks and asymmetric short&run response to common shocks weaken the case for a common currency, as being a member of a monetary union implies losing benefits form the monetary autonomy and stabilizing movements of the exchange rate. Consequently, identifying the main sources of heterogeneity between Poland and the euro area may be viewed as an important step towards assessing the costs of Polandms EMU entry.
The structure of our model builds largely on the previous work in the new open economy macroeconomics (NOEM) literature, launched by the influential contribution by Obstfeld and Rogoff (1995). The choice of a particular setup of the workhorse model is always a task involving a great deal of subjectivity. Building a large and comprehensive model, with a large number of stochastic disturbances and rich propagation mechanisms, is a time consuming task.
Moreover, taking big and sophisticated models to the data is usually far from straightforward, if not impossible, given data availability. Needless to say, these kinds of constraints to empirical investigations are particularly severe for countries like Poland. The most relevant obstacles are short time series and scarcity of well established stylized facts.
Taking into account these considerations, our strategy is to keep the size of the model relatively small, so that it is possible to estimate most of its important and not easy to pin down parameters, instead of resorting to calibrations. The small size has an additional advantage of increased clarity and operationality. The obvious limitation is a risk of neglecting potentially important mechanisms, relevant for the problem considered.
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