EconPapers    
Economics at your fingertips  
 

Modern Econometric Approaches: Application of The ARW Algorithm in Shock Identification

Ivan Todorov
Additional contact information
Ivan Todorov: University of National and World Economy, Bulgaria

Scientific Conference of the Department of General Economic Theory, 2022, issue 1, 55-60

Abstract: Part of the relevance of economic theory is the validation of empirical results and their dissemination. The reliability problems encountered in empirical analysis are often due to the endogeneity of macroeconomic variables. Since the seminal work of Sims (1980), structural vector autoregressives (SVARs) have supplanted large-scale macroeconometric models, but we are unable to interpret how the endogenous variables affect each other if the residuals are not orthogonal. A huge recent step in the development of econometrics is the identification scheme for checking all possible permutations of SVAR models, but retaining only those that have "economically sensible" impulse responses.

Keywords: Eviews; Sign Restrictions; Structural Vector Autoregression; Zero Restrictions (search for similar items in EconPapers)
JEL-codes: E10 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://conference.ue-varna.bg/oit2022/assets/Sbornik_dokladi_OIT_2022.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrn:oitcon:y:2022:i:1:p:55-60

Access Statistics for this article

Scientific Conference of the Department of General Economic Theory is currently edited by Kaloyan Kolev

More articles in Scientific Conference of the Department of General Economic Theory from University of Economics - Varna Contact information at EDIRC.
Bibliographic data for series maintained by Tatyana Ivanova ().

 
Page updated 2025-03-20
Handle: RePEc:vrn:oitcon:y:2022:i:1:p:55-60