Identification in Macro-Finance: Recent Advances
Saturday, Jan. 4, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: Emi Nakamura, University of California-Berkeley
Shock Restricted Structural Vector Autoregressions
AbstractIdentifying assumptions need to be imposed on dynamic models before they can be used to analyze the dynamic effects of economically interesting shocks. Often, the assumptions are only rich enough to identify a set of solutions. This paper considers two types of restrictions on the structural shocks that can help reduce the number of plausible solutions. The first is imposed on the sign and magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and components of variables external to the model. These non-linear inequality constraints can be used in conjunction with zero and sign restrictions that are already widely used in the literature. The effectiveness of our constraints are illustrated using two applications of the oil market and Monte Carlo experiments calibrated to study the role of uncertainty
shocks in economic fluctuations.
Bartik Instruments: What, When, and Why and How?
AbstractThe Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the Bartik instrument is numerically equivalent to using local industry shares as instruments in a GMM estimator and discuss how different asymptotics imply different identifying assumptions. We argue that in most applications the identifying assumption is in terms of industry shares. Finally, we show how to decompose the Bartik instrument into the weighted sum of the just-identified instrumental variables estimators. These weights measure how sensitive the parameter estimate is to each instrument. We illustrate our results through four applications: estimating the inverse elasticity of labor supply, estimating local labor market effects of Chinese imports, estimating the fiscal multiplier using defense spending shocks, and using simulated instruments to study the effects of Medicaid expansions.
Granular Instrumental Variables
AbstractIn many settings, there is a dearth of instruments, which hampers economists’ ability to investigate causal relations. We propose a quite general way to construct instruments: “granular instrumental variables” (GIVs). In the economies we study, a few large firms or countries account for a large share of economic activity. As they are large, their idiosyncratic shocks affect aggregate outcomes. This makes those idiosyncratic shocks valid instruments for aggregate shocks. We provide a methodology to extract idiosyncratic shocks from the data, this way creating GIVs. Those GIVs allow us to then estimate parameters of interest, including causal elasticities.
We first illustrate the idea in a basic supply and demand framework: we achieve a novel identification of supply and demand elasticities, based on idiosyncratic shocks to supply or demand. We then show how the procedure can be adapted to handle many enrichments. We provide initial illustrations of the procedure with two applications. First, we measure how shocks to domestic banks causally affect sovereign yields. We document how negative shocks to Italian banks adversely affect Italian government bond yields, and vice-versa. This gives the first causal measure of the “doom loop” between banks and sovereign yields. Second, we estimate short-term supply and demand elasticities in the oil market. Our estimates match well existing estimates that use much more complex and labor-intensive (e.g., narrative) methods.
We sketch how GIVs could be useful to estimate a host of other causal parameters in economics, particularly in aggregate macro-finance contexts where instruments are usually very rare.
- E0 - General
- G0 - General