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Markov Switching Models (markov + switching_models)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Selected Abstracts

Specification Testing of Markov Switching Models*

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2003
Robert Breunig
Abstract This paper proposes a set of formal tests to address the goodness-of-fit of Markov switching models. These formal tests are constructed as tests of model consistency and of both parametric and non-parametric encompassing. The formal tests are then combined with informal tests using simulation in combination with non-parametric density and conditional mean estimation. The informal tests are shown to be useful in shedding light on the failure (or success) of the encompassing tests. Several examples are provided. [source]

Sample Splitting and Threshold Estimation

ECONOMETRICA, Issue 3 2000
Bruce E. Hansen
Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuously-distributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for nonparametric function estimation. For example, the threshold autoregressive model (TAR) is popular in the nonlinear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. It is known that threshold estimates are super-consistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either cross-section or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. It is found that the distribution of the threshold estimate is nonstandard. A method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic. It is shown that this yields asymptotically conservative confidence regions. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson (1995). [source]

Specification Testing of Markov Switching Models*

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2003
Robert Breunig
Abstract This paper proposes a set of formal tests to address the goodness-of-fit of Markov switching models. These formal tests are constructed as tests of model consistency and of both parametric and non-parametric encompassing. The formal tests are then combined with informal tests using simulation in combination with non-parametric density and conditional mean estimation. The informal tests are shown to be useful in shedding light on the failure (or success) of the encompassing tests. Several examples are provided. [source]

Moment based regression algorithms for drift and volatility estimation in continuous-time Markov switching models

THE ECONOMETRICS JOURNAL, Issue 2 2008
Robert J. Elliott
Summary, We consider a continuous time Markov switching model (MSM) which is widely used in mathematical finance. The aim is to estimate the parameters given observations in discrete time. Since there is no finite dimensional filter for estimating the underlying state of the MSM, it is not possible to compute numerically the maximum likelihood parameter estimate via the well known expectation maximization (EM) algorithm. Therefore in this paper, we propose a method of moments based parameter estimator. The moments of the observed process are computed explicitly as a function of the time discretization interval of the discrete time observation process. We then propose two algorithms for parameter estimation of the MSM. The first algorithm is based on a least-squares fit to the exact moments over different time lags, while the second algorithm is based on estimating the coefficients of the expansion (with respect to time) of the moments. Extensive numerical results comparing the algorithm with the EM algorithm for the discretized model are presented. [source]