Macro Identification
Financiado
New Approaches to the Identification of Macroeconomic Models
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 thi...
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 three 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 analyse new ways of using time-variation in some of the parameters of macroeconomic models, such as trend inflation or the volatility of shocks, to study important open questions in macro, such as the degree of forward versus backward-looking behaviour and the ‘good luck versus good policy’ debate.
The third 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 three lines' methodological analyses will accelerate progress on each line well beyond what would be possible in a piecemeal approach.
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Duración del proyecto: 66 meses
Fecha Inicio: 2015-08-07
Fecha Fin: 2021-02-28
Fecha Fin: 2021-02-28
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2021-02-28
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-CoG-2014:
ERC Consolidator Grant
Cerrada
hace 10 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
117.00K€ |
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