Estimate a multivariate time series model that contains lagged endogenous and exogenous variables, and how to simulate responses. The response series are the quarterly:
Estimate the parameters of a vector error-correction (VEC) model. Before estimating VEC model parameters, you must determine whether there are any cointegrating relations (see
Tests on B answer questions about the space of cointegrating relations. The column vectors in B , estimated by jcitest , do not uniquely define the cointegrating relations. Rather, they
Tests on A answer questions about common driving forces in the system. When constructing constraints, interpret the rows and columns of the n -by- r matrix A as follows:
The differences between orthogonal and generalized impulse response functions using the three-dimensional VAR(2) model in docid:econ_ug.brz_lcd , p. 78. The variables in the model
Simulates data from an arbitrary 3-D VAR(2) model, and fits a VAR(2) model to the simulated data.
Assess whether a multivariate time series has multiple cointegrating relations using the Johansen test.
Test the null hypothesis that there are no cointegrating relationships among the response series composing a multivariate model.
Convert an n -dimensional VAR model to a VEC model, and then compute and interpret the cointegration rank of the resulting VEC model.
Generate impulse responses from this VEC(3) model ( docid:econ_ug.brz_lcd , Ch. 6.7):
Use Monte Carlo simulation via simulate to forecast a VAR model.
Comparing inferences and estimates from the Johansen and Engle-Granger approaches can be challenging, for a variety of reasons. First of all, the two methods are essentially different,
In addition to testing for multiple cointegrating relations, jcitest produces maximum likelihood estimates of VEC model coefficients under the rank restrictions on B . Estimation
Generate impulse responses of an interest rate shock on real GDP using filter .
Implement the capital asset pricing model (CAPM) using the Econometrics Toolbox™ VAR model framework.
Create a three-dimensional VAR(4) model with unknown parameters using varm and the shorthand syntax. Then, this example shows how to adjust parameters of the created model using dot
Generate Monte Carlo forecasts from a VEC ( q ) model. The example compares the generated forecasts to the minimum mean squared error (MMSE) forecasts and forecasts from the VAR ( q +1) model
Include exogenous data for several seemingly unrelated regression (SUR) analyses. The response and exogenous series are random paths from a standard Gaussian distribution.
Generate simulated responses in the forecast horizon when some of the response values are known. To illustrate conditional simulation generation, the example models quarterly measures
Forecast responses conditional on the current values of other responses in the forecast horizon. To illustrate conditional forecasting, the example models quarterly measures of the