Philippe Goulet Coulombe, Karin Klieber
arXiv 18 May 2025 · Econometrics · 2 citations (OpenAlex)
arXiv:2505.12422 · PDF · DOI · OpenAlex · Extracted main text
Local projections (LPs) are widely used in empirical macroeconomics to estimate impulse responses to policy interventions. Yet, in many ways, they are black boxes. It is often unclear what mechanism or historical episodes drive a particular estimate. We introduce a new decomposition of LP estimates into the sum of contributions of historical events, which is the product, for each time stamp, of a weight and the realization of the response variable. In the least squares case, we show that these weights admit two interpretations. First, they represent purified and standardized shocks. Second, they serve as proximity scores between the projected policy intervention and past interventions in the sample. Notably, this second interpretation extends naturally to machine learning methods, many of which yield impulse responses that, while nonlinear in predictors, still aggregate past outcomes linearly via proximity-based weights. Applying this framework to shocks in monetary and fiscal policy, global temperature, and the excess bond premium, we find that easily identifiable events-such as Nixon's interference with the Fed, stagflation, World War II, and the Mount Agung volcanic eruption-emerge as dominant drivers of often heavily concentrated impulse response estimates.
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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 | Romer, C. D. and Romer, D. H (2004) A new measure of monetary shocks: Derivation and implications | 1.000 | 9 | 4 | 100% |
| 2 | Bilal, A. and Känzig, D. R (2024) The macroeconomic impact of climate change: Global vs. local temperature | 1.000 | 6 | 3 | 100% |
| 3 | Mumtaz, H. and Piffer, M (2022) Impulse response estimation via flexible local projections | 0.928 | 4 | 3 | 100% |
| 4 | Kolesár, M. and Plagborg-Mller, M (2024) Dynamic causal effects in a nonlinear world: the good, the bad, and the ugly | 0.843 | 3 | 3 | 100% |
| 5 | Ramey, V. A. and Zubairy, S (2018) Government spending multipliers in good times and in bad: evidence from us historical data | 0.830 | 14 | 5 | 57% |
| 6 | Goulet Coulombe, P., Göbel, M., and Klieber, K (2024) Dual interpretation of machine learning forecasts self | 0.811 | 4 | 2 | 100% |
| 7 | Goulet Coulombe, P (2025) Ordinary least squares as an attention mechanism | 0.737 | 3 | 2 | 100% |
| 8 | Goulet Coulombe, P., Rapach, D., Schütte, E. C. M., and Schwenk-Nebb… (2023) The anatomy of machine learning-based portfolio performance | 0.737 | 3 | 2 | 100% |
| 9 | Bernanke, B. S., Boivin, J., and Eliasz, P (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach | 0.737 | 3 | 2 | 100% |
| 10 | Goncalves, S., Herrera, A. M., Kilian, L., and Pesavento, E (2024) State-dependent local projections | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 112 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0.29cm 22.5524 dpd Ordinary Least Squares as an Attention Mechanism . 0.25cm | 0.405 | 1 | 1 |