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利用独立性细化 VAR 的集合识别AI 解读

Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help…

排序限制下的识别与推断AI 解读

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm,…

初创企业对地方劳动力市场的作用AI 解读

Summary There are substantial differences in startup activity across US local labor markets. We study the causes and consequences of these differences. Startup productivity shocks are found to drive much of these cross‐city differences in startup activity. Examples of such shocks include breakthroughs in biotech that spurred startup formation in San Diego and Philadelphia. Overall, these shocks explain half of the forecast error variance of startup job creation, accounting…

财政 DSGE 模型的叙事识别方法AI 解读

Structural DSGE models are used for analyzing both policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified SVARs based on narrative shocks. This paper asks whether both approaches agree. Specifically, I use narrative data in a DSGE‐SVAR that partially identify policy shocks in the VAR and assess the fit of the DSGE model relative to this…