Markov-switching Regression Research Articles

Fiscal Policy Rules and Regime (In)Stability: Evidence from the U.S.

We employ Markov-switching regression methods to estimate fiscal policy feedback rules in the U.S. for the period 1960-2002. Our approach allows to capture policy regime changes endogenously. We reach three main conclusions. First, fiscal policy may be characterized, according to Leeper (1991) terminology, as active from the 1960s throughout the 1980s, switching gradually to passive in the early 1990s and switching back to active in early 2001. Second, regime-switching fiscal rules are capable of tracking the time-series behaviour of the U.S. primary deficit better than rules based on a constant parameter specification. Third, regime-switches in monetary and fiscal policy rules do not exhibit any degree of synchronization. Our results are at odds with the view that the post-war U.S. fiscal policy regime may be classified as passive at all times, and seem to pose a challenge for the specification of the correct monetary-fiscal mix within recent optimizing macroeconomic models considered suitable for policy analysis.

The curse of Moctezuma: American silver and the Dutch disease

This study formalizes and empirically tests the conjecture that the discovery of large silver reserves in its American colonies during the 1540s triggered in Spain a case of Dutch disease, diverting factors of production to non-traded goods industries and undermining the Spanish comparative advantages in the Early Modern Age. I develop an open-economy model to mimic the economic conditions in Imperial Spain. I then present new consumption weights built from primary sources, which I combine with existing price data to produce price indexes for traded and non-traded goods; these are then used to test the implications of the model in a Markov-switching regression framework. I identify a strong and persistent increase in the relative price of non-traded goods coinciding with the silver discoveries, lasting for almost three decades and reversing itself only after the 1575 and 1579 crown bankruptcies.

Markov-Switching Models with Endogenous Explanatory Variables II: A Two-Step MLE Procedure with Standard-Error Correction

This paper provides a unified framework for a two-step MLE procedure to deal with the problem of endogeneity in Markov-switching regression models. Two important issues are considered. First, a consistent estimation of the Markov-switching regression equation of interest is considered. Second, obtaining correct standard errors of the coefficients estimates in the second step is considered, in light of Pagan's (1984) 'generated regressors.' Our Monte Carlo experiments provide the validity of the proposed methods in small samples. The model and the proposed methods are applied to Campbell and Mankiw's (1989) consumption function, by allowing for possibilities of structural breaks in the sensitivity of consumption growth to the predictable component of income growth. Empirical results suggest that during the 1970's and 1980's, when uncertainty in future income growth was highest, the measure of sensitivity was high and statistically significant, while it was not significant in the rest of the sample.

Edgeworth Price Cycles, Cost-Based Pricing, and Sticky Pricing in Retail Gasoline Markets

Abstract This paper examines dynamic pricing behavior in Canadian retail gasoline markets. I find three distinct pricing patterns: cost-based pricing, sticky pricing, and sharp asymmetric retail price cycles that resemble the Edgeworth cycles of Maskin and Tirole (1988). I use a Markov-switching regression to estimate the prevalence of the regimes and the structural characteristics of the cycles themselves. I find cycles are more prevalent when there are more small firms and are accelerated and amplified with very many small firms. In markets with few small firms, sticky pricing dominates. The findings are consistent with the theory of Edgeworth cycles.

Markov-switching models with endogenous explanatory variables

The maximum likelihood estimation of a Markov-switching regression model based on the Hamilton filter is not valid in the presence of endogenous explanatory variables. However, we show that there exists an appropriate transformation of the model that allows us to directly employ the Hamilton filter. The transformed model explicitly allows for a vector of bias correction terms as additional regressors, and the new disturbance term is uncorrelated with all the regressors in the transformed model. Within this framework, a quasi maximum likelihood estimation procedure is presented. A procedure to test for endogeneity based on the Wald statistic or the likelihood ratio statistic is also presented.

Fundamentals and asset price dynamics

The relation between fundamentals and asset returns is analyzed by means of Markov-switching regression models with time-varying transition probabilities. By referring to the Italian Stock Exchange over the 1973-2002 period, we find that (i) returns ‘switch’ between a zero-expected return/low volatility state and a high expected return/high volatility state; (ii) states are persistent and hence state changes can be forecast to some extent; (iii) the probability of state changes can be explained in terms of changes in the fundamentals; (iv) fundamentals do not have a direct impact on the expected returns but they only affect the transition probability matrix. Overall, our results show that a non-linear relation between market price changes and market fundamentals can be caught within the framework of (Markov) switching regession models.

Finite-sample properties of the bootstrap estimator in a Markov-switching model

The size distortion problem is clearly indicative of the small-sample approximation in the Markov-switching regression model. This paper shows that the bootstrap procedure can relieve the effects that this problem has. Our Monte Carlo simulation results reveal that the bootstrap maximum likelihood asymptotic approximations to the distribution can often be good when the sample size is small.

Social interactions and fluctuations in birth rates

Fluctuations in birth rates in developed countries have been considerably less regular than many explanatory theories suggest. This paper argues that social interaction, i.e., the dependence of individuals' fertility decisions on the fertility behaviour of other population members, is relevant even in developed countries. A formal model for investigating the static and dynamic consequences of social interaction for fertility is developed, and its aggregate implications are tested using a Markov switching regression model. The findings show that social interaction can lead to fluctuations in birth rates that are swift and difficult to foresee, and that these fluctuations are likely to be asymmetric: spells of low fertility have a considerably higher persistence than spells of high fertility. The paper suggests that high birth rates are likely to be followed by spells of low fertility. Transitions from low to high fertility occur at a substantially lower rate than transitions in the opposite direction.

A dynamic programming approach to the estimation of markov switching regression models

This paper suggests an alternative two-stage estimation method for Markov Switching Regression models. The procedure, based on the Maximum A Posteriori (MAP) decision rule, is essentially related to Bellman's dynamic programming algorithm. Monte Carlo results indicate that this procedure is a good alternative to Hamilton's method. The empirical application shows that the dating of the U.S. business cycles by the MAP procedure is closer to the dating by NBER than that given by Hamilton's method.