Locally robust inference for non-Gaussian SVAR models

• 成果类型: Article
• 署名作者: Hoesch, Lukas; Lee, Adam; Mesters, Geert
• 署名单位: Vrije Universiteit Amsterdam; Tinbergen Institute; BI Norwegian Business School; Pompeu Fabra University; Barcelona School of Economics; Centre de Recerca en Economia Internacional (CREI)
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2274
• 发表日期: 2024
• 页码: 523-570
• 关键词: Weak identification; Semiparametric Inference; Hypothesis Testing; impulse responses; Independent Component Analysis; C32; C39; C51

摘要

All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size/coverage for local-to-Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non-Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply-demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non-Gaussianity for identification.