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Computational Economics
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Taming the Curse of Dimensionality:Quantitative Economics with Deep Learning
https://d.repec.org/n?u=RePEc:pen:papers:24-034&r=&r=cmp
Published: October 29, 2024 00:00
We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between…
Forecasting US Presidential Election 2024 using multiple machine learning algorithms
https://d.repec.org/n?u=RePEc:pra:mprapa:122490&r=&r=cmp
Published: October 20, 2024 00:00
The outcome of the US presidential election is one of the most significant events that impacts trade, investment, and geopolitical policies on the global stage. It also sets the direction of the world economy and global politics for the next few years.…
Two halves don't make a whole: instability and idleness emerging from the co-evolution of the production and innovation processes
https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/27&r=&r=cmp
Published: October 9, 2024 00:00
We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model…
Experimental evidence that delegating to intelligent machines can increase dishonest behaviour
https://d.repec.org/n?u=RePEc:osf:osfxxx:dnjgz&r=&r=cmp
Published: October 4, 2024 00:00
While artificial intelligence (AI) enables significant productivity gains from delegating tasks to machines, it can also facilitate the delegation of unethical behaviour. Here, we demonstrate this risk by having human principals instruct machine agents to…
Double machine learning and Stata application
https://d.repec.org/n?u=RePEc:boc:chin23:03&r=&r=cmp
Published: October 2, 2024 00:00
Traditional methods for estimating treatment effects generally assume strong functional forms and are only applicable when the covariates are low-dimensional data. However, using machine learning methods directly often leads to "regularization bias". The…