a b s t r a c tIt is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across states. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standard structural vector autoregressive analysis. The approach is illustrated by two SVAR examples which have been reported in the literature and which have features that can be accommodated by the MS structure.

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Recently, a dynamic development of intermittent renewable energy sources (RES) has been observed. In order to allow for the adoption of trading contracts for unplanned events and changing weather conditions, the day-ahead markets have been complemented by intraday markets; in some countries, such as Poland, balancing markets are used for this purpose. This research focuses on a small RES generator, which has no market power and sells electricity through a larger trading company. The generator needs to decide, in advance, how much electricity is sold in the day-ahead market. The optimal decision of the generator on where to sell the production depends on the relation between prices in different markets. Unfortunately, when making the decision, the generator is not sure which market will offer a higher price. This article investigates the possible gains from utilizing forecasts of the price spread between the intraday/balancing and day-ahead markets in the decision process. It shows that the sign of the price spread can be successfully predicted with econometric models, such as ARX and probit. Moreover, our research demonstrates that the statistical measures of forecast accuracy, such as the percentage of correct sign classifications, do not necessarily coincide with economic benefits.