Econometrics
21 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Econometrics
| Trend | Dataset | Best Model | Paper | Code | Compare |
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BIG-bench
Most implemented papers
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?
In this paper we have updated the hypothesis testing framework by drawing upon modern computational power and classification models from machine learning.
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms.
Simulating Diffusion Bridges with Score Matching
jeremyhengjm/diffusionbridge
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This is known to be a challenging problem that has received much attention in the last two decades.
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
The derived minimax rate corresponds to that of the non-invertible bi-Lipschitz function, which shows that the invertibility does not reduce the complexity of the estimation problem in terms of the rate.
Deep Learning Macroeconomics
We explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate a economic-based output gap.
Causal Imitation Learning under Temporally Correlated Noise
We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions.
Exploiting Independent Instruments: Identification and Distribution Generalization
sorawitj/hsic-x
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Most of the existing estimators assume that the error term in the response $Y$ and the hidden confounders are uncorrelated with the instruments $Z$.
The Economics and Econometrics of Gene-Environment Interplay
Economists and social scientists have debated the relative importance of nature (one's genes) and nurture (one's environment) for decades, if not centuries.
Deep Learning Enhanced Realized GARCH
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures.
Data Scaling Effect of Deep Learning in Financial Time Series Forecasting
For many years, researchers have been exploring the use of deep learning in the forecasting of financial time series.
