Credible Inference for Empirical Macroeconomics

Following the credibility revolution, macroeconomists have sought plausibly exogenous instruments and other sources of variation to identify causal effects.

Given the complex nature of the macroeconomy, characterised by simultaneous causality and intemporal dependence, this is a high bar.

Thus, in the pursuit of exogenous variation, researchers often use minor sources of variation or subtle features of the data to identify the effects of interest. When the variation exploited is modest, “weak identification” can arise.

In practice, this means that estimators are no longer asymptotically normal, so standard techniques for statistical inference – conducting hypothesis tests or constructing confidence intervals – are invalid.

While this likely occurs in much empirical research in macroeconomics, few papers acknowledge these issues, partially because there are rarely appealing options to address them. This proposal provides attractive options for researchers to combat weak identification in macroeconometric models.

First, it offers the possibility to avoid weak identification in the first place, via novel frameworks to exploit instrumental variables in panel and time series data. These frameworks extract richer information from a given instrument and expand the set of admissible instruments.

Next, I provide tools to construct confidence sets for dynamic causal effects, a key object of interest, that are valid regardless of how strong the identifying variation is. Existing approaches produce confidence sets that are conservative – too large.

I first consider models identified using instrumental variables, improving both computational burden and performance relative to frontier methods.

Finally, I consider models identified using more general sources of variation, and, working identification scheme by scheme, provide performance gains over leading methods for confidence sets. I thus facilitate credible inference to match credible identification strategies.