Published on 01 September 2017
Working Paper No. 674:
By Chiranjit Chakraborty and Andreas Joseph
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 limitations. We review the underlying technical sources and the nascent literature applying machine learning to economic and policy problems. We present popular modelling approaches, such as artificial neural networks, tree-based models, support vector machines, recommender systems and different clustering techniques. Important concepts like the bias-variance trade-off, optimal model complexity, regularisation and cross-validation are discussed to enrich the econometrics toolbox in their own right. We present three case studies relevant to central bank policy, financial regulation and economic modelling more widely. First, we model the detection of alerts on the balance sheets of financial institutions in the context of banking supervision. Second, we perform a projection exercise for UK CPI inflation on a medium-term horizon of two years. Here, we introduce a simple training-testing framework for time series analyses. Third, we investigate the funding patterns of technology start-ups with the aim to detect potentially disruptive innovators in financial technology. Machine learning models generally outperform traditional modelling approaches in prediction tasks, while open research questions remain with regard to their causal inference properties.
Tomorrow, we will discuss in our cours forecasting tools for demographics. But first, we will see basic static tools. Before playing with longitudinal dataset, let us use “standard” life tables. Some (French) datasets are available on the INED website, but let us use the most popular ones, the TV8890 and TD8890. Those two tables can…
via Forecasting Techniques for Demographics — Freakonometrics
Estadística y Machine Learning con R recopila apuntes y materiales utilizados en la docencia de cursos de formación para funcionarios sobre estadística para administraciones públicas y estadística con R, y otros materiales utilizados en la docencia de Econometría. El manual ha sido elaborado exclusivamente con R, utilizando las librerias knitr, markdown y bookdown, que permiten editar y compilar documentos en diferentes formatos.
Estadística y Machine Learning con R
Explicando Deep Learning y Redes Neuronales -sin código- Intentaré explicar brevemente en qué consiste el Deep Learning ó Aprendizaje Profundo utilizado en Machine Learning describiendo sus componentes básicos. Conocimientos Previos Daré por sentado que el lector ya conoce la definición de Machine Learning y sus principales aplicaciones en el mundo real y el panorama de algoritmos…
via Aprendizaje Profundo: una Guía rápida — Aprende Machine Learning
(This article was first published on R – Giga thoughts …, and kindly contributed to R-bloggers) Introduction This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. In this series I included the implementations of the most common Machine Learning algorithms in R and Python. The algorithms…
via Practical Machine Learning with R and Python – Part 6 — R-bloggers
(This article was first published on R – Giga thoughts …, and kindly contributed to R-bloggers) This is the 5th and probably penultimate part of my series on ‘Practical Machine Learning with R and Python’. The earlier parts of this series included 1. Practical Machine Learning with R and Python – Part 1 In this initial…
via Practical Machine Learning with R and Python – Part 5 — R-bloggers
(This article was first published on R – Data Science Heroes Blog, and kindly contributed to R-bloggers) Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than 1 year ago tl;dr: Hi there! I invite you to read the…
via Data Science Live Book (open source) ~ new big release! 200-pages — R-bloggers