predict.stvar: Predict method for class 'stvar' objects

In sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Predict method for class 'stvar' objects

Description

predict.stvar is a predict method for class 'stvar' objects.

Usage

## S3 method for class 'stvarpred'
plot(x, ..., nt, trans_weights = TRUE)

## S3 method for class 'stvar'
predict(
  object,
  ...,
  nsteps,
  nsim = 1000,
  pi = c(0.95, 0.8),
  pred_type = c("mean", "median"),
  exo_weights = NULL
)

## S3 method for class 'stvarpred'
print(x, ..., digits = 2)

Arguments

x	object of class 'stvarpred'
...	currently not used.
nt	a positive integer specifying the number of observations to be plotted along with the forecast.
trans_weights	should forecasts for transition weights be plotted?
object	an object of class 'stvar'.
nsteps	how many steps ahead should be predicted?
nsim	to how many independent simulations should the forecast be based on?
pi	a numeric vector specifying the confidence levels of the prediction intervals.
pred_type	should the pointforecast be based on sample "median" or "mean"?
exo_weights	if weight_function="exogenous", provide a size (nsteps x M) matrix of exogenous transition weights for the regimes: [step, m] for step steps ahead and regime m weight. Ignored if weight_function!="exogenous".
digits	the number of decimals to print

Details

The forecasts are computed by simulating multiple sample paths of the future observations and using the sample medians or means as point forecasts and empirical quantiles as prediction intervals.

Value

Returns a class 'stvarpred' object containing, among the specifications,...

$pred

Point forecasts

$pred_ints

Prediction intervals, as [, , d].

$trans_pred

Point forecasts for the transition weights

$trans_pred_ints

Individual prediction intervals for transition weights, as [, , m], m=1,..,M.

Functions

• plot(stvarpred): predict method

• print(stvarpred): print method

References

• Anderson H., Vahid F. 1998. Testing multiple equation systems for common nonlinear components. Journal of Econometrics, 84:1, 1-36.

• Hansen B.E. 1994. Autoregressive Conditional Density estimation. Journal of Econometrics, 35:3, 705-730.

• Kheifets I.L., Saikkonen P.J. 2020. Stationarity and ergodicity of Vector STAR models. International Economic Review, 35:3, 407-414.

• Lanne M., Virolainen S. 2025. A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks. Unpublished working paper, available as arXiv:2403.14216.

• Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.

• McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.

• Kilian L., Lütkepohl H. 20017. Structural Vector Autoregressive Analysis. 1st edition. Cambridge University Press, Cambridge.

• Tsay R. 1998. Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93:443, 1188-1202.

• Virolainen S. 2025. Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models. Unpublished working paper, available as arXiv:2404.19707.

See Also

simulate.stvar

Examples

 # p=2, M=2, d=2, Gaussian relative dens weights
 theta_222relg <- c(0.356914, 0.107436, 0.356386, 0.08633, 0.13996, 0.035172,
   -0.164575, 0.386816, 0.451675, 0.013086, 0.227882, 0.336084, 0.239257, 0.024173,
   -0.021209, 0.707502, 0.063322, 0.027287, 0.009182, 0.197066, 0.205831, 0.005157,
   0.025877, 1.092094, -0.009327, 0.116449, 0.592446)
 mod222relg <- STVAR(data=gdpdef, p=2, M=2, d=2, params=theta_222relg,
   weight_function="relative_dens")

 # Predict 10 steps ahead, point forecast based on the conditional
 # mean and 90% prediction intervals; prediction based on 100 sample paths:
 pred1 <- predict(mod222relg, nsteps=10, nsim=100, pi=0.9, pred_type="mean")
 pred1
 plot(pred1)

 # Predict 7 steps ahead, point forecast based on median and  90%, 80%,
 # and 70% prediction intervals; prediction based on 80 sample paths:
 pred2 <- predict(mod222relg, nsteps=7, nsim=80, pi=c(0.9, 0.8, 0.7),
  pred_type="median")
 pred2
 plot(pred2)

sstvars documentation built on April 11, 2025, 5:47 p.m.