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 ...
Thomas J Catalano is a CFP and Registered Investment Adviser with the state of South Carolina, where he launched his own financial advisory firm in 2018. Thomas' experience gives him expertise in a ...
This paper derives the asymptotic mean square error of multistep prediction for the general vector autoregressive process. For one-step-ahead prediction the result is ...
This article examines frequentist risks of Bayesian estimates of vector autoregressive (VAR) regression coefficient and error covariance matrices under competing loss ...
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 ...
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 ...
Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...
Spatial econometrics addresses the challenges posed by spatially correlated data, enabling researchers to understand and quantify how economic phenomena in one location can influence those in ...
The recent financial crisis raises important issues about the transmission of financial shocks across borders. In this paper, a global vector autoregressive (GVAR) model is constructed to assess the ...
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