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Everything about Vector autoregression
Statistical model to calculate the value of multiple quantities as they change over time
Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR is a type of stochastic process model. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. VAR models are often used in economics and the natural sciences. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Vector autoregressionA 5-minute orientation
- Skim a recent paper: How to estimate a vector autoregression after March 2020Influential work since 2022
- Pick up a classic book: The Cointegrated VAR ModelThe most-reprinted book on the subject
- Read the landmark paper: Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive ModelsCited 6,996 times
- Go sideways: Minimum mean square error estimatorA closely related topic
- Go sideways: Simultaneous equations modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models
Statistical inference in vector autoregressions with possibly integrated processes
Estimating Vector Autoregressions with Panel Data
Time Varying Structural Vector Autoregressions and Monetary Policy
Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions
🆕 Influential since 2022
How to estimate a vector autoregression after March 2020
Forecasting and stress testing with quantile vector autoregression
The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review
Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea
Vector autoregression models with skewness and heavy tails
📚 Books worth reading
The Cointegrated VAR ModelKatarina Juselius · 2006
New directions in econometric practiceWojciech Charemza, Wojciech W. Charemza · 1991- 📕A, B, C's (and D)'s for understanding VARSJesús Fernández-Villaverde · 2005
- 📕Analyse kointegrierter Variablen mittels vektorautoregressiver ModelleHans-Eggert Reimers · 1991
- 📕A variance decomposition for stock returnsJohn Y. Campbell · 1990
- 📕Essays on vector autoregressions with cointegrating restrictionsTor Jacobson · 1992
- 📕Foreign entanglementsTamim A. Bayoumi · 2007
- 📕A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variablesAndrew Ang · 2001
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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