In this thesis we apply dynamic augmented gravity models for panel data to model selected issues of EU accession in the agriculture sector. Furthermore, we compare several dynamic panel estimators for modeling the agriculture trade and use various bootstrap options to approximate the distribution of the sample estimator. According to our knowledge, our thesis represents the first application of these methods to trade and especially to the EU enlargement.
We show that dynamic panel data models are appropriate tools for modelling of agricultural trade flows. The lagged levels of the agricultural trade are significant determinants of contemporaneous trade level, which underlines the importance of history in this market. The application of dynamic models enable us to make inference on the long-run effects of EU accession despite short time series.
In general, we find low income but high price elasticities of demand for agricultural imports. Thus, the agricultural market is already saturated and highly sensitive to price changes. Despite many limitations behind our analysis, our results show slightly positive implications for the new Member States. We find positive and significant EU enlargement effects especially for exports of the new Member States, which vary strongly between agricultural commodities.
On the other hand the agro-food imports of the new Member States show lower growth dynamic after the Eastern enlargement of the EU. As a result, it seems that the new Member States gained significantly from the liberalization of the agricultural trade, although the effects remained rather moderate.
 Bartošová, D., Bartová, L., Fidrmuc J. ,
Agropotravinársky obchod po vstupe do Európskej únie,
Ekonomický casopis - Journals of Economics 55, April (2007), 327-344.
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