Causality in structural vector autoregressions: Science or sorcery?
以教学为目的，向主要受训于微观计量经济学的经济学家介绍结构向量自回归（SVAR）方法，用于估计农业和资源经济学中的动态因果效应，并比较其与经典工具变量模型的异同。
Abstract This paper presents the structural vector autoregression (SVAR) as a method for estimating dynamic causal effects in agricultural and resource economics. We have a pedagogical purpose; we aim the presentation at economists trained primarily in microeconometrics. The SVAR is a model of a system, whereas a reduced‐form microeconometric study aims to estimate the causal effect of one variable on another. The system approach produces estimates of a complete set of causal relationships among the variables, but it requires strong assumptions to do so. We explain these assumptions and describe similarities and differences with the classical instrumental variables (IV) model. We demonstrate that the population analogue of the Wald IV estimator for a particular causal effect is identical to the ratio of two impulse responses from an SVAR. We further demonstrate that incorrect identification assumptions about some components of the SVAR do not necessarily invalidate the estimated causal effects of other components. We present an SVAR analysis of global supply and demand for agricultural commodities, which was previously examined using IV. We illustrate the additional economic insights that the SVAR reveals, and we articulate the additional assumptions upon which those insights rest.