We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding...

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...

We apply machine learning techniques to construct a series of models of corporate bond defaults. By combining Chinese accounting information and corporate bond data from January 2012 to December 2019, we construct an out-of-sample forecast that significantly improves the identification rate of...

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or...

gingado is an open source Python library under active development that offers a variety of convenience functions and objects to support usage of machine learning in economics research. It is designed to be compatible with widely used machine learning libraries. gingado facilitates augmenting...

Ein Ziel der Arbeitsgruppe Künstliche Intelligenz1 am Fachbereich Wirtschaftder Hochschule Wismar ist die praktische Anwendung der Methodenund Techniken der Künstlichen Intelligenz in der betriebswirtschaftlichenPraxis. Der Trend hin zum Einsatz von Wissen in entsprechendenIT-Lösungen und...