Advanced Econometrics: Estimating Markov Switching Models (MSDR & MSAR) скачать в хорошем качестве

Advanced Econometrics: Estimating Markov Switching Models (MSDR & MSAR)

Markov Switching is an advanced econometric technique used to analyze time series characterized by structural breaks or multiple regimes (such as economic recessions and expansions), where the transitions between these unobserved states follow a Markov chain. In Stata, the mswitch command provides a comprehensive framework for estimating state-dependent parameters using maximum likelihood. The implementation process requires declaring time-series data using tsset. Depending on the adjustment dynamics of the data, users can choose between the Markov-switching dynamic regression model (mswitch dr) for quick adjustments after state changes, typically for high-frequency data; or the Markov-switching autoregression model (mswitch ar) for more gradual adjustments. Stata allows for flexible customization of the number of states via the states() option and specific identification of variables with switching coefficients using the switch() option, as well as allowing for state-dependent error variances with varswitch. Following estimation, post-estimation commands such as estat transition and estat duration are essential tools for reporting the transition probability matrix and the expected duration of each state ❤️ THANK YOU FOR WATCHING Follow Vietlod for more insightful bite-sized knowledge every day!