Markov‐Switching Estimation of Spatial Maize Price Transmission Processes between Tanzania and Kenya

We discuss the Markov-switching vector au-toregressive (MS-VAR) class of nonlinear time series models that can be used to analyze recurring discrete structural changes in time series. Hamilton’s (1989) seminal Markov-switching (MS) model of the U.S. business cy-cle triggered considerable interest in the MS approach in economics. Most empirical appli-cations to date have focused on the business cy-cle and financial markets, but we see potential for MS-VAR models in agricultural economics, for example, in price transmission analysis. In the following, we first provide an overview of the MS-VAR framework. We then present an illustrative application to maize price trans-mission between Tanzania and Kenya. The article closes with a discussion of strengths, weaknesses, and potential uses of the MS-VAR approach in price transmission analysis. A Brief Overview of MS-VAR Models Following Krolzig (1997), the basic idea be-hind the MS-VAR class of models is that the parameters of a VAR process are allowed to depend on an unobserved regime variable st ∈ {1,..., M}, representing M possible states of the world. In its most general form, the MS-VAR is given by yt = (st) + p∑ j=1 A j (st)yt − j + ut,