{"id":23376,"date":"2026-02-08T20:00:00","date_gmt":"2026-02-08T19:00:00","guid":{"rendered":"https:\/\/www.11onze.cat\/?p=23376"},"modified":"2026-02-02T09:59:58","modified_gmt":"2026-02-02T08:59:58","slug":"algorithms-economists","status":"publish","type":"post","link":"https:\/\/www.11onze.cat\/en\/magazine\/algorithms-economists\/","title":{"rendered":"Algorithms now also play at being economists"},"content":{"rendered":"
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The automation of work is creating an occupational metamorphosis, in which tasks usually done by humans are transferred to a set of technologies. Economics is not spared, and artificial intelligence is also gaining ground. But are economic forecasting algorithms a real alternative to economists?<\/b><\/h3>\n

\u00a0<\/b><\/p>\n

As a result of the digitisation process of the last decades, <\/span>huge amounts of data are being generated that are transforming the methods by which we analyse statistical models<\/b>. Storing, registering, and analysing this constant flow of information has become an essential task for many sectors of the economy.<\/span><\/p>\n

A technological revolution has opened up <\/span>new possibilities in economic and financial forecasting capabilities<\/b>. The analysis of these large databases, known as \u2018big data\u2019, would not be possible without artificial intelligence (AI). A rather broad term that encompasses a whole range of ideas.<\/span><\/p>\n

Even so, there are two concepts in this field: <\/span>machine learning (ML) and deep learning (DL)<\/b>, mathematical algorithms that allow computers to identify patterns in data and make predictions by imitating humans. Two computational advances that form the basis of economic forecasting with artificial intelligence.<\/span><\/p>\n

\u00a0<\/span><\/p>\n

An algorithmic crystal ball<\/b><\/h3>\n

Experts often compare algorithmic forecasting to \u201ca crystal ball\u201d. Indeed, this metaphor is the title of an<\/span> internal <\/b>study<\/b><\/a> published by the International Monetary Fund (IMF)<\/b>, in which the authors of the research, Jin-Kyu Jung, Manasa Patnam and Anna Ter-Martirosyan, try to establish whether macroeconomic forecasting algorithms can improve on the results predicted by IMF economists themselves.<\/span><\/p>\n

The study applies three different machine learning algorithms to a common economic forecasting problem, and the results are surprising. In all three cases,<\/span> the algorithmic prediction far surpassed the benchmark performance of IMF economists<\/b>.<\/span><\/p>\n

In their observations, the authors warn that there are still factors that require further research. They also state that, for these predictions to be truly effective, real-time observations would have to be included. They explain that <\/span>there is some freedom in the introduction of the parameters used by the algorithms<\/b>, and that this may be key to determining their effectiveness.<\/span><\/p>\n

Even so, in their conclusion they agree on the fact that the potential of machine learning in terms of statistical analysis of economic data is evident; and that, although these predictions made by algorithms cannot fully replace the work of economists, <\/span>they represent a valuable additional reference when making decisions on economic forecasting<\/b>.<\/span><\/p>\n


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