Category Markov Chain Model Markov-Switching Dynamic Regression Model State-Space Models All Examples Functions Apps Markov-Switching Dynamic Regression Model Discrete-time Markov model containing switching state and dynamic regression submodelsFunctions expand all Create Model Fit Model to Data Infer Latent...
Markov-switching dynamic regression Sample: 1929m2 - 1972m6 No. of obs = 521 Number of states = 2 AIC = -0.4826 Unconditional probabilities: transition HQIC = -0.4634 SBIC = -0.4336 Log likelihood = 131.7225 S12.mumspcCoef. Std. Err. z P>|z| [95% Conf. Interval] ...
.mswitch dr fedfundsPerforming EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -508.66031 Iteration 1: log likelihood = -508.6382 Iteration 2: log likelihood = -508.63592 Iteration 3: log likelihood = -508.63592 Markov-switching dynamic regression Sample: 1954q3...
Thus, using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other, it is found that for both oil production and oil relative importance, the regime that was dominant during the 1980s and the early 1990s when oil production...
Create the Markov-switching dynamic regression model that describesytandst. Get % Switching mechanismP = [10 1 1; 1 10 1; 1 1 10]; mc = dtmc(P);% VAR submodelsC1 = [1;-1]; C2 = [2;-2]; C3 = [3;-3]; AR1 = {}; ...
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. Specifically, we derive a new information criterion, Markov switching criterion (MSC), which is an estimate of KL divergen...
uncertainty on the overall market returns in several economic sectors (China, Japan, the USA, France, and Germany) between 2000 and 2020 using the importance of the crude oil prices volatility index by applying a Quantile-on-Quantile regression(Q-Q), including dynamic copula with Markov-...
This research introduces a new method for event studies in time-series analysis named the Markov regime-switching event response model (MS-ERM). The MS-ERM
(StataCorp) Markov-switching regression in Stata May 18 2 / 1 Outline 1 When we use Markov-Switching Regression Models 2 Introductory concepts 3 Markov-Switching Dynamic Regression Predictions State probabilities predictions Level predictions State expected durations Transition probabilities 4 Markov-...
stock return dynamics that use the regime switching model, two completely different dynamic processes are found, and they are classified as either the stable regime (high return and low volatility) or the volatile regime (low return and high volatility). The volatile regime includes three periods:...