機械学習・深層学習・計量経済学に関する先行研究の整理 (Previous Studies on Machine Learning, Deep Learning, and Econometrics)

Abstract

Japanese Abstract: 本研究は、機械学習・深層学習・強化学習の分野と計量経済学の分野の近接性を探す点に特徴をもつ。計量経済学において時系列分析やパネル分析を学んでいれば、機械学習は分析手法とともに計量経済学の考え方に多くの共通点をもつことから今後は関係性をより体系的に考察することが重要となると考える。また計量経済学も因果推論の分野では機械学習が活用されており、その関係性を説明する。さらに機械学習と計量経済学の近年の研究の進展を概観するため、画像認識や音声認識における機械学習のビジネスへの応用やCATEやLASSO、 政策配分問題としてのELMや疫学における因果推論としてCMA（因果媒介分析）・Causal Forest・SDD・構造推定などを幅広く扱う。

English Abstract: This research is characterized in that it seeks closeness between the fields of machine learning, deep learning and reinforcement learning and the field of econometrics. If you are studying time series analysis or panel analysis in econometrics, machine learning has much in common with econometrics as well as analytical methods, so it is important to consider relationships more systematically in the future. I think it will be. In econometrics, machine learning is used in the field of causal inference, and the relationship is explained. In order to give an overview of recent advances in research on machine learning and econometrics, we have applied machine learning to business in image recognition and speech recognition, CATE and LASSO, ELM as a policy distribution problem, and CMA (causal inference in epidemiology). Mediation analysis), Causal Forest, SDD, structure estimation, etc.