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Credible Inference for Empirical Macroeconomics

Project description

New tools for macroeconomic research

Macroeconomists face a big challenge when trying to identify cause-and-effect relationships in the economy. The problem is that the economy is complex, with many factors influencing each other at the same time. Often, researchers rely on small variations in data to make conclusions, which can lead to fragile results. This makes it difficult to test theories and build confidence in the findings. In this context, the ERC-funded CreMac project aims to address these issues by developing new tools to work with data that is only weakly informative about the effects of macroeconomic policies. These tools will help researchers use data more efficiently and conduct more precise tests, leading to stronger and more reliable conclusions in macroeconomics.

Objective

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.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

UNIVERSITY COLLEGE LONDON
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 438 705,00
Address
GOWER STREET
WC1E 6BT LONDON
United Kingdom

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Region
London Inner London — West Camden and City of London
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 438 705,00

Beneficiaries (1)

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