Minimal State Variable Solutions to Markov-Switching Rational Expectations Models

Farmer, Roger E. A.; Waggoner, Daniel F.; Zha, Tao

doi:10.2139/ssrn.1300463

Cited by 109 publications

(230 citation statements)

References 30 publications

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“…Therefore, Svensson and Williams (2007), Davig and Leeper (2007) and Farmer, Waggoner, and Zha (2008, 2009, 2011 all provide algorithms to solve DSGE models with Markov-switches in structural parameters. 7 In this paper, we adopt the procedure developed by Farmer et al (2008) to solve the model with Markov-switching in simple rule parameters. This model can be recast in the following system…”

Section: Simple Rule Specificationmentioning

confidence: 99%

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How optimal is US monetary policy?

Chen

Kirsanova

Leith

2017

Journal of Monetary Economics

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Most of the literature estimating DSGE models for monetary policy analysis assume that policy follows a simple rule. In this paper we allow policy to be described by various forms of optimal policy -commitment, discretion and quasi-commitment. We find that, even after allowing for Markov switching in shock variances, the inflation target and/or rule parameters, the data preferred description of policy is that the US Fed operates under discretion with a marked increase in conservatism after the 1970s. Parameter estimates are similar to those obtained under simple rules, except that the degree of habits is significantly lower and the prevalence of cost-push shocks greater. Moreover, we find that the greatest welfare gains from the 'Great Moderation' arose from the reduction in the variances in shocks hitting the economy, rather than increased inflation aversion. However, much of the high inflation of the 1970s could have been avoided had policy makers been able to commit, even without adopting stronger anti-inflation objectives. More recently the Fed appears to have temporarily relaxed policy following the 1987 stock market crash, and has lost, without regaining, its post-Volcker conservatism following the bursting of the dot-com bubble in 2000.

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Section: Simple Rule Specificationmentioning

confidence: 99%

“…Following Farmer et al (2008), equation (15) can be rewritten as the following model with regime-invariant parameters…”

Section: Simple Rule Specificationmentioning

confidence: 99%

How optimal is US monetary policy?

Chen

Kirsanova

Leith

2017

Journal of Monetary Economics

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“…Therefore, we make use of the methods developed in Svensson (2005), Farmer et al (2011), Maih (2012 and Foerster et al (2014) that seek to identify the minimum state variable solutions after applying the concept of mean square stability. This characterisation allows us to specify the general form of the Markov-switching rational expectations model as,…”

Section: Solution and Estimationmentioning

confidence: 99%

“…In this case the filter that is used to compute values for the unobserved processes would need to incorporate information up to the present time period, which include information relating to the states of the Markov chains (which is not incorporated in the traditional Kalman or particle filter). Therefore, we implement a version of the Hamilton (1989) filter that limits the number of states that are carried forward after each iteration, as in Farmer et al (2008).…”

Section: Solution and Estimationmentioning

confidence: 99%

“…The use of Markov-switching behaviour in a DSGE model is described in Liu et al (2009), Farmer et al (2009), Farmer et al (2011), Liu and Mumtaz (2011 and Alstadheim et al (2013). These models allow for the analysis of samples with multiple regime changes, where they are largely focused on the way in which the central bank reacts to various factors that influence the policy rule.…”

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confidence: 99%

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Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model

2016

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The aim of this paper is to investigate structural changes in the South African economy using an estimated small open-economy dynamic stochastic general equilibrium (DSGE) model. The structure of the model follows recent work in this area and incorporates the expectations of agents and a number of shocks that are assumed to affect the economy at various points in time. In addition, the dynamic linkages between the respective variables in the model may be explained in terms of the microfoundations that characterise the behaviour of firms, households and the central bank. After estimating the model, we allow for the parameters in a number of different structural equations to change periodically over time. Different versions of the model are assessed using various statistical criteria to identify the model that is able to explain the changing dynamics in the South African economy. The results suggest that the central bank has responded in a consistent manner over the sample period; however, there are periods of time where it does not focus too greatly on output pressure. This impacts on some of the impulse response functions where we note that a monetary policy shock has a slightly larger effect on inflation, while the risk-premium shock has a larger effect on output, inflation and interest rates.JEL Classifications: E32, E52, F41.

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Estimation and Solution of Models with Expectations and Structural Changes

Kulish

Pagan

2016

J of Applied Econometrics

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Standard solution methods for linearised models with rational expectations take the structural parameters to be constant. These solutions are fundamental for likelihood-based estimation of such models. Regime changes, such as those associated with either changed rules for economic policy, institutional changes, or changes in the technology of production, can generate large changes in the statistical properties of observable variables. In practice, structural change is accounted for during estimation by selecting a sub-sample for which a time-invariant structure seems valid. In this paper we develop solutions for linearised models with structural changes under a variety of assumptions regarding agents' beliefs about those structural changes. We put the solutions in state space form and use the Kalman filter to construct the likelihood function. We apply the techniques to three examples: an inflationary program, a disinflation program and a transitory slowdown in trend growth. * We thank

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Minimal State Variable Solutions to Markov-Switching Rational Expectations Models

Cited by 109 publications

References 30 publications

How optimal is US monetary policy?

How optimal is US monetary policy?

Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model

Estimation and Solution of Models with Expectations and Structural Changes

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