A model with first-order autoregressive errors, AR(1), has the form while an AR(2) error process has the form and so forth for higher-order processes. Note that the ...
Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is ...
After computing the sample autocovariance matrices, PROC STATESPACE fits a sequence of vector autoregressive models. These preliminary autoregressive models are used to estimate the autoregressive ...
Bayesian Vars: A Survey of the Recent Literature with An Application to the European Monetary System
Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs).
We study the relationship between vector autoregressive moving-average (VARMA) and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors ...
Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local ...
A mixture vector autoregressive framework to capture extreme events in macro-prudential stress tests
Severe financial turbulence is driven by high impact and low probability events that are the hallmarks of systemic financial stress. These unlikely adverse events arise from the extreme tail of a ...
Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those ...
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