OLS Estimation of Markov switching VAR models: asymptotics and application to energy use
| Autor: | Maddalena Cavicchioli |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Statistics and Probability Economics and Econometrics Population 010603 evolutionary biology 01 natural sciences Energy use 010104 statistics & probability Matrix (mathematics) Statistics::Methodology Applied mathematics Markov switching VAR model OLS estimator Asymptotic covariance matrix Energy use Economic growth 0101 mathematics education OLS estimator Economic growth Mathematics education.field_of_study Asymptotic covariance matrix Series (mathematics) Markov chain Applied Mathematics Estimator Delta method Autoregressive model Modeling and Simulation Ordinary least squares Markov switching VAR model Social Sciences (miscellaneous) Analysis |
| Zdroj: | AStA Advances in Statistical Analysis. 105:431-449 |
| ISSN: | 1863-818X 1863-8171 |
| DOI: | 10.1007/s10182-020-00383-4 |
| Popis: | We show that the ordinary least squares (OLS) estimates of population parameters for Markov switching vector autoregressive (MS VAR) models coincide with the maximum likelihood estimates. Then, we propose an algorithm in matrix form for the estimation of model parameters, and derive an explicit expression in closed-form for the asymptotic covariance matrix of the OLS estimator of such models. The obtained characterization of the asymptotic variance is new to our knowledge. It is easier to program than the usual approach based on second derivatives, and more accurate. Our theorems generalize the classical results known for a linear VAR process, and complete those existing in the literature on the estimation of the asymptotic covariance matrix for multivariate stationary time series. Numerical simulations are provided to illustrate the obtained theoretical results. Finally, an application on energy use and economic growth in the Euro area gives some insights on the nonlinear nature of the corresponding time series, and reproduces the major stylized facts. |
| Databáze: | OpenAIRE |
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