plot.stvarpred: Predict method for class 'stvar' objects

Description

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

Usage

# S3 method for stvarpred
plot(x, ..., nt, trans_weights = TRUE)
# S3 method for stvar
predict(
  object,
  ...,
  nsteps,
  nsim = 1000,
  pi = c(0.95, 0.8),
  pred_type = c("mean", "median"),
  exo_weights = NULL
)
# S3 method for stvarpred
print(x, ..., digits = 2)

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.

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

Functions

plot(stvarpred): predict method
print(stvarpred): print method

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.

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 Unpublished working paper, available as arXiv:2403.14216.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.

Examples

Run this code

 # 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)

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