Panel Vector Autoregression (Panel VAR)

Panel vector autoregression (panel VAR) models several endogenous variables jointly as functions of their own and each other's lagged values across many units observed over time, while accounting for unit-specific fixed effects.

패널 벡터자기회귀(패널 VAR)는 여러 단위를 시간에 걸쳐 관측한 자료에서 단위별 고정효과를 고려하면서 여러 내생변수를 각자와 서로의 시차값의 함수로 함께 모형화한다.

How do I analyze dynamic interdependence among several variables across many countries or firms over time?

여러 국가나 기업에서 시간에 따른 여러 변수의 동적 상호의존성을 어떻게 분석하나요?

Panel vector autoregression treats several variables as jointly endogenous and estimates how each responds to past values of all of them across many units observed over time. It fits macroeconomic or firm panels, typically estimated by GMM, and is interpreted through impulse responses and Granger causality tests.

When to use Panel Vector Autoregression (Panel VAR)

• Several variables per unit observed over many periods, such as countries or regions
• Feedback among variables with no clear exogenous ordering
• Analyzing impulse responses and Granger causality in a panel

When to use something else

• A single long time series; use a standard VAR
• A single outcome with exogenous predictors; use a dynamic panel regression

Assumptions and requirements

• Stationary variables or appropriate differencing
• Homogeneous dynamics across units
• Correct lag order
• Valid instruments when using GMM

What the analysis reports

• Lagged coefficient estimates
• Granger causality Wald tests
• Impulse response functions with confidence bands
• Forecast error variance decomposition
• Stability (eigenvalue) check

How to report it

Granger causality tests from the panel VAR indicated that [variable X] predicted [variable Y], χ²([df]) = [value], p = [value].

Related methods

• Vector Autoregression
• Dynamic Panel GMM
• Random Intercept CLPM

Run Panel Vector Autoregression (Panel VAR) on Quorant