Philipp F. M. Baumann, Michael Schomaker, Enzo Rossi
arXiv 4 Mar 2020 · Econometrics · publishedJournal of Causal Inference (2021) · 13 citations (OpenAlex)
arXiv:2003.02208 · PDF · DOI · OpenAlex · Extracted main text
The notion that an independent central bank reduces a country's inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dincer \ Eichengreen (2014) Central bank transparency and independence: Updates and new measures | 0.928 | 5 | 3 | 80% |
| 2 | Van der Laan \ Rose (2011) Targeted Learning, Springer | 0.928 | 4 | 3 | 100% |
| 3 | Schomaker, Luque-Fernandez, Leroy \ Davies (2019) Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions | 0.874 | 5 | 2 | 100% |
| 4 | Daniel, Cousens, De Stavola, Kenward \ Sterne (2013) Methods for dealing with time-dependent confounding | 0.843 | 3 | 3 | 100% |
| 5 | Tran, Petersen, Schwab \ van der Laan (2018) Robust variance estimation and inference for causal effect estimation | 0.843 | 3 | 3 | 100% |
| 6 | Hernan \ Robins (2020) Causal Inference, Vol | 0.843 | 3 | 3 | 100% |
| 7 | Imbens (2019) Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics, Working Pap… | 0.811 | 4 | 2 | 100% |
| 8 | Tran, Yiannoutsos, Wools-Kaloustian, Siika, van der Laan \ Petersen (2019) Double robust efficient estimators of longitudinal treatment effects: Comparative performance in simulations and a case study | 0.811 | 4 | 2 | 100% |
| 9 | Romelli (2018) The political economy of reforms in central bank design: evidence from a new dataset | 0.763 | 6 | 2 | 67% |
| 10 | Bang \ Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 131 scored citations.