HEИндивидуална стипендия2023–2025

MACROML · Machine Learning Macroeconometric Methods for Dynamic Causal Inference

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2023-04-01 → 2025-03-31
Финансиране от ЕС: 230 774 €
Участници: 2
Схема: HORIZON-TMA-MSCA-PF-EF

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Накратко на български

Методите за машинно обучение се прилагат върху макроикономически данни, за да се разбере как една промяна влияе върху друга във времето. Това помага на политиците да анализират по-точно връзките във финансите и икономиката.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Machine Learning Macroeconometric Methods for Dynamic Causal Inference

The literature on microeconometric methods based on statistical learning techniques has grown substantially over the last decade, yet macroeconometrics literature lacks an understanding of such methods which could be applied to answer causal inference questions. The primary goal of the macroml research project is to put forward theory-driven methods for dynamic causal inference analysis based on models typically used in the macroeconometrics literature, bridging the gap between machine learning and macroeconometric modelling. The key distinction of this project from the state-of-the-art methods is the analysis of heavy-tailed and highly persistent time series data — a critical feature that has been largely overlooked in the literature. In particular, the research project will investigate: I. accurate and theoretically-valid estimation and inference econometric techniques for general high-dimensional time series models; II. a general methodology for high-dimensional local projection estimators which allows studying the dynamic causal relationship between economic time series data. The project enlarged policymakers’ toolbox for the analysis of macroeconomics and finance data to assess different dynamic causal hypotheses in a flexible and accurate way, thereby making it highly policy-relevant. In addition, new estimation methods of machine learning time series models allow practitioners to implement ML techniques for time series data in a data-driven way. The project also delivered several interesting empirical applications.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Data lies at the heart of all economic decisions. Everyone — and especially central bankers, investors, and policymakers — processes data when making choices. Thanks to technological innovations, the speed at which (raw) data are generated and shared by businesses, public administrations, and scientific research (among others) have increased exponentially. Large amounts of data bring new opportunities and challenges to econometrics. The literature on microeconometric methods based on statistical learning techniques has grown substantially over the last decade, yet macroeconometrics literature lacks an understanding of such methods which could be applied to answer causal inference questions. The primary goal of the macroml research project is to put forward theory-driven methods for dynamic causal inference analysis based on models typically used in the macroeconometrics literature, bridging the gap between machine learning and macroeconometric modelling. The key distinction of this project from the state-of-the-art methods is the analysis of heavy-tailed and highly persistent time series data — a critical feature that has been largely overlooked in the literature.In particular, the research project will investigate: I. accurate and theoretically-valid estimation and inference econometric techniques for general high-dimensional time series models;II. a general methodology for high-dimensional local projection estimators which allows studying the dynamic causal relationship between economic time series data.The project will enlarge policymakers’ toolbox for the analysis of macroeconomics and finance data to assess different dynamic causal hypotheses in a flexible and accurate way, thereby making it highly policy-relevant. In addition, new estimation methods of machine learning time series models will allow practitioners to implement ML techniques for time series data in a data-driven way. The project also will deliver several interesting empirical applications.

Оригинален текст от CORDIS (на английски).

Участници

COPENHAGEN BUSINESS SCHOOL · FrederiksbergКоординаторДания
UNIVERSIDAD POMPEU FABRA · BarcelonaИспания