Philipp F. M. Baumann, Enzo Rossi, Alexander Volkmann
arXiv 11 Jun 2020 · Statistics — Applications · publishedFrontiers in Applied Mathematics and Statistics (2023) · 6 citations (OpenAlex)
arXiv:2006.06274 · PDF · DOI · OpenAlex · Extracted main text
We analyze the forces that explain inflation using a panel of 122 countries from 1997 to 2015 with 37 regressors. 98 models motivated by economic theory are compared to a gradient boosting algorithm, non-linearities and structural breaks are considered. We show that the typical estimation methods are likely to lead to fallacious policy conclusions which motivates the use of a new approach that we propose in this paper. The boosting algorithm outperforms theory-based models. We confirm that energy prices are important but what really matters for inflation is their non-linear interplay with energy rents. Demographic developments also make a difference. Globalization and technology, public debt, central bank independence and political characteristics are less relevant. GDP per capita is more relevant than the output gap, credit growth more than M2 growth.
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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 | Mundlak, Y. (1978) On the pooling of time series and cross section d… Econometrica, 46, 69–85 | 0.811 | 4 | 2 | 100% |
| 2 | Bühlmann, P., Hothorn, T. et al. (2007) Boosting algorithms: Regular… Statistical science, 22, 477–505 | 0.511 | 2 | 2 | 50% |
| 3 | Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M. and Hofner, B. (20… (2018) https://CRAN.R-project.org/package=mboost | 0.511 | 2 | 2 | 50% |
| 4 | Wood, S. N. (2011) Fast stable restricted maximum likelihood and mar… Journal of the Royal Statistical Society (B), 73, 3–36 | 0.511 | 2 | 2 | 50% |
| 5 | Wood, S. and Scheipl, F. (2017) gamm4: Generalized Additive Mixed Mo… https://CRAN.R-project.org/package=gamm4 | 0.511 | 2 | 2 | 50% |
| 6 | Wood, S. N. (2011) Fast stable restricted maximum likelihood and mar… (2013) Biometrika, 100, 221–228 | 0.511 | 2 | 1 | 100% |
| 7 | Hofner, B., Mayr, A., Robinzonov, N. and Schmid, M. (2014) Model-bas… Computational Statistics, 29, 3–35 | 0.511 | 2 | 1 | 100% |
| 8 | Honaker, J., King, G. and Blackwell, M. (2011) Amelia II: A program… Journal of Statistical Software, 45, 1–47 | 0.405 | 1 | 1 | 100% |
| 9 | Baumann, P. F. M., Schomaker, M. and Rossi, E. (2021b) Estimating th… Journal of Causal Inference, 9, 109–146 self | 0.405 | 1 | 1 | 100% |
| 10 | Baumann, P. F. M., Rossi, E. and Volkmann, A. (2021a) What drives in… Swiss National Bank Working Paper Series, 12 self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating the Effect of Central Bank Independence on Inflation Using Longitudinal Targeted Maximum Likelihood Estimation | 0.644 | 2 | 2 |