There is a wide literature on the dynamic adjustment of employment and its relationship with the business cycle. In this paper we present a statistical model that offers a congruent representation of part of the UK labour market since the mid 1960s. We use a cointegrated vector autoregressive Markov-switching model in which some parameters change according to the phase of the business cycle. Output, employment, labour supply and real earnings are found to have a common cyclical component. The long run dynamics are characterized by one cointegrating vector relating unemployment to trend-adjusted real wages and output. Despite there having been many changes affecting this sector of the UK economy, the Markov-switching vector-equilibrium-correction model with three regimes (representing recession, normal growth, and high growth) provides a good characterization of the sample data, and performs well relative to alternative linear and non-linear models. The results of an impulse-response analysis highlight the dangers of using VARs when the constancy of the estimated coefficients has not been established, and demonstrate the advantages of generating regime dependent responses.
A Markov-switching vector equilibrium correction model of the UK labour market
Marcellino, M;
2002
Abstract
There is a wide literature on the dynamic adjustment of employment and its relationship with the business cycle. In this paper we present a statistical model that offers a congruent representation of part of the UK labour market since the mid 1960s. We use a cointegrated vector autoregressive Markov-switching model in which some parameters change according to the phase of the business cycle. Output, employment, labour supply and real earnings are found to have a common cyclical component. The long run dynamics are characterized by one cointegrating vector relating unemployment to trend-adjusted real wages and output. Despite there having been many changes affecting this sector of the UK economy, the Markov-switching vector-equilibrium-correction model with three regimes (representing recession, normal growth, and high growth) provides a good characterization of the sample data, and performs well relative to alternative linear and non-linear models. The results of an impulse-response analysis highlight the dangers of using VARs when the constancy of the estimated coefficients has not been established, and demonstrate the advantages of generating regime dependent responses.
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