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Tesi etd-09202020-232710


Thesis type
Tesi di laurea magistrale
Author
CALEMMA, CORRADO
URN
etd-09202020-232710
Title
ASSESSING IDENTIFICATION RESTRICTIONS IN STRUCTURAL VECTOR AUTOREGRESSIVE MODELS: A GENERALISED, DATA-DRIVEN APPROACH
Struttura
ECONOMIA E MANAGEMENT
Corso di studi
ECONOMICS
Supervisors
relatore Prof. Moneta, Alessio
Parole chiave
  • shock identification
  • time-series analysis
  • Bayesian VAR
Data inizio appello
05/10/2020;
Consultabilità
Completa
Riassunto analitico
Shock identification in Vector Autoregressive (VAR) models has often put researchers in a position from which they can only rely, for the purpose of obtaining a structural representation of the economic mechanisms that they try to capture, on a number of assumptions derived mostly from economic theory. Many of these assumptions cannot be easily tested jointly with the specification of the model. Recent developments in the VAR literature, drawing on the generical assumption of independent (and, in many cases, non-Gaussian) structural shocks, have demonstrated that it is possible to identify structural shocks by using only the distribution of reduced-form shocks and taking advantage of the information provided by its moments even beyond the variance-covariance matrix, offering a new way to evaluate – or even test – previous identification strategies. The primary question driving the research at the basis of the following work is about looking for suitable ways in which we can assess the plausibility of a priori shock identification assumptions (depending on the category they belong to) in the light of the results obtained with these new models.
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