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फैक्टर-ऑगमेंटेड वेक्टर ऑटोरिग्रेशन (FAVAR)×थ्रेशोल्ड और स्मूथ-ट्रांज़िशन VAR (TVAR / STVAR)×वेक्टर ऑटोरिग्रेशन (VAR) मॉडल×
क्षेत्रअर्थमितिअर्थमितिअर्थमिति
परिवारRegression modelRegression modelRegression model
उद्भव वर्ष200519982005
प्रवर्तकBernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexesTsay (multivariate threshold modelling)Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
प्रकारMultivariate time-series modelNonlinear multivariate time-series modelMultivariate time-series model
मौलिक स्रोतBernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422. DOI ↗Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202. DOI ↗Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗
उपनामfactor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)TVAR, STVAR, regime-switching VAR, threshold VARvector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
संबंधित454
सारांशFAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once.Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).
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