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...
.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...
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] ...
Create Fully Specified Model 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 = {}; AR2 = {[0.5 0.1; 0.5 ...
The structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA.Thus,using a Markov-switching dynamic regression model in which parameters change when oil ...
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...
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
The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission ...
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-Switching AR Models (Stata...
An additional test is the dynamic quantile (DQ) one introduced by Manganelli and Engle (2004). It is based on a linear regression of the hit variable on a set of explanatory variables including a constant, the lagged values of the hit variable and any useful function of past information. ...