In this paper, Hamilton’s (1989) Markov-switching model is extended to the simultaneous equations model. Using a framework for an instrumental variable interpretation of full information maximum likelihood (FIML) by Hausman (1975), we can deal with the problem of simultaneous equations based on the Hamilton filter. When we compared the proposed FIML Markov-switching model to LIML Markovswitchin...

Title of thesis: COMPARING REGIME-SWITCHING MODELS IN TIME SERIES: LOGISTIC MIXTURES: vs. MARKOV SWITCHING Dimitrios V. Paliouras Master of Science, 2007 Thesis directed by: Professor Benjamin Kedem Department of Mathematics The purpose of this thesis is to review several related regime-switching time series models. Specifically, we use simulated data to compare models where the unobserved stat...

We investigate the performance of a battery of standard unit root tests when the true data generating process has a Markov-switching trend growth rate and variance. Regime switching under both the null hypothesis of a unit root and the alternative hypothesis of trend stationarity is considered. In contrast to the case of a single break in trend growth rate, multiple Markov-switching breaks unde...

this paper, i have focused on the tax side of the fiscal policy to  investigate the past and future behavior of fiscal sustainability in iran. to do so, i have employed two different forward-looking and backward-looking approaches. first, the backward-looking approach is the fiscal policy rule proposed by daving & leeper (2011). precisely, this rule determines that whether the fiscal policy is ...

In this paper we analyze the asymptotic properties of the popular distribution tail index estimator by Hill (1975) for dependent, heterogeneous processes. We develop new extremal dependence measures that characterize a massive array of linear, nonlinear, and conditional volatility processes with long or short memory. We prove that the Hill estimator is weakly and uniformly weakly consistent for...

The paper illustrates the computation of marginal likelihoods and Bayes factors when Markov Chain Monte Carlo has been used to produce draws from a model’s posterior distribution. The method is based on Raftery (1996) and does not require that Gibbs sampling is used or conditional posterior distributions are available in closed form. Models used include a normal finite mixture, a GARCH and a St...

Purpose This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central Eastern European countries (CEEC). Design/methodology/approach In the research process, authors use Bayesian method of inference for two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) mod...