Professor Sophocles Mavroeidis, Univ Tutorial Fellow in Economics and Professor of Macroeconomics, has been awarded a prestigious ERC Consolidator grant.

The ERC Consolidator grant is given to researchers who are within 7 to 12 years from completion of their DPhil. Professor Mavroeidis research project is entitled “New Approaches to the Identification of Macroeconomic Models” and will run for a period of 5 years starting on 1st September 2015. The total amount of funding is 1.3 million Euros.

Professor Mavroeidis said, “I am grateful to the College for allowing me to apply for this grant with a significant reduction in my College teaching duties. It demonstrates the College’s commitment to strong research as well as teaching.”

Project summary:

Macroeconomic data are largely non-experimental. Thus, causal inference in macroeconomics is largely based on assumptions about what aspects of the variation in the data are exogenous. This presents two major challenges, which this research addresses directly. First, few such assumptions are generally accepted. Second, conditional on any set of assumptions, identification of causal effects is often weak because there is little relevant variation in the data. To tackle these challenges, I propose two lines of enquiry to explore new sources of identification and develop the requisite econometric methods.

The first line will study the implications of the so-called “zero lower bound” (ZLB) on nominal interest rates for identification. The key novel insight is that the ZLB causes monetary policy to be set at least in part exogenously. This can be thought of as a natural experiment that generates a new instrument to identify the underlying policy model. This insight applies more generally to policy functions subject to exogenous constraints. The informativeness of these constraints depends on the probability that they bind, so recent experience makes the ZLB a promising application of the idea.

The second line will contribute to the on-going research on developing methods of inference that are robust to weak identification. This is a pervasive problem in macro that threatens the validity of structural inference under any identification scheme.
The synergies among these two lines’ methodological analyses will accelerate progress on each line well beyond what would be possible in a piecemeal approach.

Published: 20 March 2015

Explore Univ on social media