Structural vector autoregressions with Markov switching : identification via heteroskedasticity

Abstract

Structural vector autoregressions are of great importance in applied macroeconometric work. The main difficulty associated with structural analysis is to identify unique shocks of interest. In a conventional approach this is done via zero or sign restrictions. Heteroskedasticity is proposed for use in identification. Under certain assumptions when volatility of shocks changes over time, unique shocks can be obtained. Then formal testing of the restrictions and impulse response analysis can be performed. In this thesis I show how identification via heteroskedasticity can be used in different contexts. In the first chapter I analyze the dynamics of trade balances in response to macroeconomic shocks. I show that identifying restrictions, which are known in the literature, are rejected for two out of seven countries. Partially identified models fail to provide enough information to fully identify shocks. The second chapter, coauthored with my supervisor, demonstrates how one can benefit from identification via heteroskedasticity when sign restrictions are used. The approach is illustrated with a model of the crude oil market. It is shown that shocks identified via previously known sign restrictions are in line with the properties of the data. Use of tighter restrictions uncovers that the approach can be discriminative. The third chapter reconsiders the conflicting results in the debate on the effects of technology shocks on hours worked. Using six ways of identifying technology shocks, I find that not all of them are supported by the data. There is no clear-cut evidence in favor of positive reaction of hours to technology shocks. However, it is plausible for real wage and disentangled investment-specific and neutral technology shocks, even though conventional identification of the latter shocks is rejected.