Jiti Gao, Fei Liu, Bin Peng, Yanrong Yang
arXiv 8 Jun 2023 · Econometrics
arXiv:2306.05593 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parameters involved, we reduce the number of effective parameters by (i) exploring the use of identification restrictions; and (ii) adopting a variable selection method based on the group-LASSO technique. Subsequently, we derive the corresponding estimation theory and propose a dependent wild bootstrap procedure to construct valid inferences accounting for the dependence of data. Finally, we validate our theoretical findings through extensive numerical studies. In an empirical study, we revisit the impacts of a tightening monetary policy action on a variety of economic variables, including short-/long-term interest rate, inflation, unemployment rate, industrial price and equity return via the newly proposed framework using a monthly dataset of the US.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Bauer \ Kohler (2019) `On deep learning as a remedy for the curse of dimensionality in nonparametric regression', The Annals of Statistics 47(4), 2261… | 1.000 | 5 | 3 | 100% |
| 2 | Schmidt-Hieber (2020) `Nonparametric regression using deep neural networks with ReLU activation function', The Annals of Statistics 48(4), 1875–1897 | 0.874 | 7 | 2 | 100% |
| 3 | Farrell, Liang \ Misra (2021) `Deep neural networks for estimation and inference', Econometrica 89(1), 181–213 | 0.811 | 4 | 2 | 100% |
| 4 | Angrist, Jordà \ Kuersteiner (2018) `Semiparametric estimates of monetary policy effects: string theory revisited', Journal of Business & Economic Statistics 36(3),… | 0.693 | 7 | 1 | 100% |
| 5 | Belloni, Chernozhukov \ Hansen (2014) `Inference on treatment effects after selection among high-dimensional controls', The Review of Economic Studies 81(2), 608–650 | 0.644 | 4 | 1 | 100% |
| 6 | Athey (2019) The impact of machine learning on economics, in A. Agrawal, J. Gans \ A. Goldfarb, eds, `The Economics of Artificial Intelligenc… | 0.644 | 2 | 2 | 100% |
| 7 | Fan \ Li (2001) `Variable selection via nonconcave penalized likelihood and its oracle properties', Journal of the American Statistical Associat… | 0.644 | 2 | 2 | 100% |
| 8 | Gao (2007) Nonlinear Time Series: Semiparametric and Nonparametric Methods, Chapman & Hall/CRC self | 0.644 | 2 | 2 | 100% |
| 9 | Li \ Racine (2007) Nonparametric Econometrics Theory and Practice, Princeton University Press, New Jersey | 0.644 | 2 | 2 | 100% |
| 10 | Bernanke \ Kuttner (2005) `What explains the stock market's reaction to federal reserve policy?', The Journal of Finance 60(3), 1221–1257 | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 56 scored citations.