VAR Model Forecasting, Simulation, and Analysis
VAR Model Forecasting
When you have models with parameters (known or estimated), you can examine the predictions of the models. For information on creating VAR models, see Vector Autoregression (VAR) Model Creation. For information on estimating models, see VAR Model Estimation.
This list describes the main forecasting methods.
forecast, you can:
Generate minimum mean square error forecasts and corresponding mean square error matrices. For an example, see Forecast VAR Model.
Generate conditional forecasts and corresponding mean square error matrices given some future response values in the forecast horizon. For an example, see Forecast VAR Model Conditional Responses.
simulate, you can:
Generate many random conditional future response paths given some future response values in the forecast horizon. For an example, see Simulate VAR Model Conditional Responses.
These functions base their forecasts on a fully specified model object and initial data. The functions differ in their innovations processes:
forecastassumes zero-valued innovations. Therefore,
forecastyields a deterministic forecast, conditional or otherwise.
simulateassumes the multivariate innovations are jointly Gaussian distributed with covariance matrix Σ.
simulateyields pseudorandom, Monte Carlo sample paths.
filterrequires innovations process paths.
filteryields a sample path that is deterministically based on the specified innovations process paths.
forecast is faster and requires less memory than generating many sample paths using
forecast is not as flexible as
filter. For example, suppose you transform some time series before making a model, and want to undo the transformation when examining forecasts. The error bounds given by transforms of
forecast error bounds are not valid bounds. In contrast, the error bounds given by the statistics of transformed simulations are valid.
How Forecasting Functions Work
For unconditional forecasting,
forecast generates two
A deterministic forecast time series based on 0 innovations
Time series of forecast mean square error matrices based on the Σ, the innovations covariance matrix.
For conditional forecasting:
forecastrequires an array of future response data that contains a mix of missing (
NaN) and known values.
forecastgenerates forecasts for the missing values conditional on the known values.
The forecasts generated by
forecastare also deterministic, but the mean square error matrices are based on Σ and the known response values in the forecast horizon.
forecastuses the Kalman filter to generate forecasts. Specifically:
forecastrepresents the VAR model as a state-space model (
ssmmodel object) without observation error.
forecastfilters the forecast data through the state-space model. That is, at period t in the forecast horizon, any unknown response is
where s < t, is the filtered estimate of y from period s in the forecast horizon.
forecastuses presample values for periods before the forecast horizon.
For either type of forecast, To initialize the VAR(p) model
in the forecast horizon,
p presample observations. You can optionally specify more
than one path of presample data. If you do specify multiple paths,
forecast returns multiple paths of forecasted responses,
with each path (page of an output 3-D array or column of a table or timetable
output) corresponding to and evolving from a path of presample values.
For unconditional simulation,
Generates random time series based on the model using random paths of multivariate Gaussian innovations distributed with a mean of zero and a covariance of Σ
Filters the random paths of innovations through the model
For conditional simulation:
forecast, requires an array of future response data that contains a mix of missing and known values, and generates values for the missing responses.
simulateperforms conditional simulation using this process. At each time
tin the forecast horizon:
simulateinfers (or, inverse filters) the innovations (
E() from the known future responses.
For missing future innovations,
Z1, which is the random, standard Gaussian distribution disturbances conditional on the known elements of
Z1by the lower triangular Cholesky factor of the conditional covariance matrix. That is,
Covarianceis the covariance of the conditional Gaussian distribution.
Z2in place of the corresponding missing values in
For the missing values in the future response data,
simulatefilters the corresponding random innovations through the VAR model
For either type of simulation:
When you specify optional input data as numeric arrays to return a numeric array of simulated paths,
simulatedoes not require presample observations. However, for time or timetable inputs and outputs,
simulaterequires a presample to infer table or timetable properties. For details, see the
To carry out inference, generate 1000s of response paths, and then estimate sample statistics from the generated paths at each time in the forecast horizon. For example, suppose
Yis a three-dimensional array of forecasted paths. Monte Carlo point and interval estimates of the forecast at time
tin the forecast horizon is
MCPointEst = mean(Y(t,:,:),3); MCPointInterval = quantile(Y(t,:,:),[0.025 0.975],3);
That is, the Monte Carlo point estimate is the mean across pages and the Monte Carlo interval estimate is composed of the 2.5th and the 97.5th percentiles computed across paths. Observe that Monte Carlo estimates are subject to Monte Carlo error, and so estimates differ each time you run the analysis under the same conditions, but using a different random number seed.
If you scaled any time series before fitting a model, you can unscale the resulting time series to understand its predictions more easily.
If you scaled a series with
log, transform predictions of the corresponding model with
If you scaled a series with
price2ret, transform predictions of the corresponding model with
cumsum(exp), or, equivalently,
cumsumis the inverse of
diff; it calculates cumulative sums. As in integration, you must choose an appropriate additive constant for the cumulative sum. For example, take the log of the final entry in the corresponding data series, and use it as the first term in the series before applying
Calculating Impulse Responses
You can examine the effect of impulse responses to models with
armairf. An impulse response is the deterministic response of a time series model to an innovations process that has the value of one standard deviation in one component at the initial time, and zeros in all other components and times. The main component of the impulse response function are the dynamic multipliers, that is, the coefficients of the VMA representation of the VAR model.
Given a fully specified
varm model, you must supply the autoregression coefficients to
armairf. By default,
armairf sends a unit shock through the system, which results in the forecast error impulse response. You can optionally supply the innovations covariance matrix and choose whether to generate generalized or orthogonalized impulse responses. Generalized impulse responses amount to filtering a shock of one standard error of each innovation though the VAR model. Orthogonalized impulse responses scale the dynamic multipliers by the lower triangular Cholesky factor of the innovations covariance. For more details, see .
For an example, see Generate VAR Model Impulse Responses.
 Lütkepohl, H. New Introduction to Multiple Time Series Analysis. Berlin: Springer, 2005.
 Pesaran, H. H. and Y. Shin. “Generalized Impulse Response Analysis in Linear Multivariate Models.” Economic Letters. Vol. 58, 1998, 17–29.