| 词条 | Bayesian vector autoregression |
| 释义 |
In statistics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR). In that respect, the difference with standard VAR models lies in the fact that the model parameters are treated as random variables, and prior probabilities are assigned to them. Vector autoregressions are flexible statistical models that typically include many free parameters. Given the limited length of standard macroeconomic datasets, Bayesian methods have become an increasingly popular way of dealing with this problem of over-parameterization.[1] The general idea is to use informative priors to shrink the unrestricted model towards a parsimonious naïve benchmark, thereby reducing parameter uncertainty and improving forecast accuracy (see [2] for a survey). A typical example is the shrinkage prior proposed by Robert Litterman,[3][4] and subsequently developed by other researchers at University of Minnesota,[5][6] which is known in the BVAR literature as the "Minnesota prior". The informativeness of the prior can be set by treating it as an additional parameter, based on a hierarchical interpretation of the model.[7] Recent research has shown that Bayesian vector autoregression is an appropriate tool for modelling large data sets.[8] See also
References1. ^{{cite journal |first1=G. |last1=Koop |first2=D. |last2=Korobilis |title=Bayesian multivariate time series methods for empirical macroeconomics |journal=Foundations and Trends in Econometrics |volume=3 |issue=4 |pages=267–358 |year=2010 |doi=10.1561/0800000013 |ssrn=1514412|citeseerx=10.1.1.164.7962}} 2. ^{{cite book|last1=Karlsson|first1=Sune|title=Forecasting with Bayesian Vector Autoregression|journal=Handbook of Economic Forecasting|date=2015|volume=2 B|doi=10.1016/B978-0-444-62731-5.00015-4|url=https://ideas.repec.org/p/hhs/oruesi/2012_012.html|pages=791–897|isbn=9780444627315}} 3. ^{{cite journal |first1=R. |last1=Litterman |title=Techniques of forecasting using vector autoregressions |journal=Federal Reserve Bank of Minneapolis Working Paper |volume=no. 115 |pages=pdf |year=1979}} 4. ^{{cite journal |first1=R. |last1=Litterman |title=Specifying VAR's for macroeconomic forecasting |journal=Federal Reserve Bank of Minneapolis Staff Report |volume=no. 92 |year=1984}} 5. ^{{cite journal |first1=T. |last1=Doan |first2=R. |last2=Litterman |first3=C. |last3=Sims |title=Forecasting and conditional projection using realistic prior distributions |journal=Econometric Reviews |volume=3 |pages=1–100 |year=1984 |doi=10.1080/07474938408800053}} 6. ^{{cite journal |first1=C. |last1=Sims |title=A nine variable probabilistic macroeconomic forecasting model |journal=Federal Reserve Bank of Minneapolis Discussion Paper |volume=no. 14 |pages=pdf |year=1989}} 7. ^{{cite journal|last1=Giannone|first1=Domenico|last2=Lenza|first2=Michele|last3=Primiceri|first3=Giorgio|title=Prior Selection for Vector Autoregressions|journal=Review of Economics and Statistics|date=2014|url=https://ideas.repec.org/p/nbr/nberwo/18467.html|doi=10.1162/rest_a_00483|volume=97|issue=2|pages=436–451|citeseerx=10.1.1.375.7244}} 8. ^{{cite journal |first1 = T.|last1 = Banbura|first2 = R.|last2 = Giannone|first3 = L.|last3 = Reichlin|title = Large Bayesian vector auto regressions|journal = Journal of Applied Econometrics|volume = 25|issue = 1|pages = 71–92|year = 2010|doi = 10.1002/jae.1137}} Further reading
2 : Bayesian statistics|Multivariate time series |
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