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Local Projection Inference in High Dimensions

Robert Adamek, Stephan Smeekes, Ines Wilms

arXiv 7 Sep 2022 · Econometrics · publishedEconometrics Journal (2024) · 3 citations (OpenAlex)

arXiv:2209.03218 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.

Citation extraction

49
references
140
in-text mentions
49
distinct cited
3
self-citations
7,749
main-text words

appendix boundary found by appendix_titled_section at “Appendix A: Assumptions” · 56% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Ramey, V. A. and S. Zubairy (2018) Government spending multipliers in good times and in bad: evidence from US historical data1.000133100%
2Plagborg-Møller, M. and C. K. Wolf (2021) Local projections and VARs estimate the same impulse responses1.00073100%
3Bernanke, B. S., J. Boivin, and P. Eliasz (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach0.93717482%
4Adamek, R., S. Smeekes, and I. Wilms (2022) Lasso inference for high-dimensional time series self0.75823443%
5Stock, J. and M. Watson (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics0.73732100%
6McCracken, M. W. and S. Ng (2016) FRED-MD: A monthly database for macroeconomic research0.6443267%
7Blanchard, O. and R. Perotti (2002) An empirical characterization of the dynamic effects of changes in government spending and taxes on output0.64422100%
8Adamek, R., S. Smeekes, and I. Wilms (2022) desla: Desparsified Lasso Inference for Time Series self0.64422100%
9Masini, R. P., M. C. Medeiros, and E. F. Mendes (2022) Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations0.64422100%
10Romer, C. D. and D. H. Romer (2004) A new measure of monetary shocks: derivation and implications0.64422100%

Showing the top 10 of 49 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Uniform Inference in High-Dimensional Threshold Regression Models1.00093
2Local Projections Inference with High-dimensional Covariates without Sparsity1.00074
3Semiparametric inference for impulse response functions using double/debiased machine learning0.84333
42602.104150.64422
5Structural Analysis of Vector Autoregressive Models0.40511
6Robust Estimation in Network Vector Autoregression with Nonstationary Regressors0.40511
72410.043300.40511
8xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R0.40511
9Double Machine Learning for Time Series0.40511
10When are time series predictions causal? The potential system and dynamic causal effects0.40511