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Machine Learning Macroeconometric Methods for Dynamic Causal Inference

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... ver más

31/03/2025

CBS

231K€

Presupuesto del proyecto: 231K€

Líder del proyecto

COPENHAGEN BUSINESS SCHOOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-03-03

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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2 Participantes

CBS

230.77K€ | Lider

UPF

N.D. | Participante

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Duración del proyecto: 24 meses Fecha Inicio: 2023-03-03
Fecha Fin: 2025-03-31

Líder del proyecto

COPENHAGEN BUSINESS SCHOOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 231K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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