This is a 2-day Ph.D. workshop on the application of deep neural networks as a global solution method, held at the MCC 03.09- 04.09.2024.

• RBC_noquad_trad: residuals computed using log(Ct) as policy of states [log(Kt),log(Zt)]. "noquad" refers to no quadrature, so it uses a point estimate for the future TFP level. In simulations stochastic shocks are included though. (This model can also be found in the repo https://github.com/saduineveld/Promes-python which solves two simple model with Time Iteration).

• The objective of the course is to gain practical familiarity with cutting-edge deep learning-aided approaches to solving dynamic optimization problems.

• We will study:

  • Basics in neural networks and deep learning
  • Deep Equilibrium Nets (DEQN) as a global solution method

• The lectures will be interactive, in a workshop-like style, that is, a mix of theory and actively playing with code examples (delivered in Python and deployed on a cloud computing infrastructure).

• All lecture materials (slides, codes, and further readings) will be distributed via the Nuvolos Cloud.
• To enroll in this class, please click on this enrollment key, and follow the steps.

• Nuvolos Support: [email protected]

• Basic econometrics.
• Basic programming in Python (see this link to QuantEcon for a thorough introduction).
• A brief Python refresher is provided under this link.
• A brief Python on Jupyter Notebooks is provided under this link
• Basic calculus and probability (The book Mathematics for Machine learning provides a good overview of skills participants are required to be fluent in).

Lectures will be interactive, in a workshop-like style, using Python, scikit learn, Tensorflow on Nuvolos, a browser-based cloud infrastructure in which files, datasets, code, and applications work together, in order to directly implement and experiment with the introduced methods and algorithms.

• Aleksandra Friedl (ifo Institute, University of Munich)

Please cite Deep Equilibrium Nets, The Climate in Climate Economics, and Deep surrogates for finance: With an application to option pricing in your publications if this repository helps your research:

@article{https://doi.org/10.1111/iere.12575,
author = {Azinovic, Marlon and Gaegauf, Luca and Scheidegger, Simon},
title = {DEEP EQUILIBRIUM NETS},
journal = {International Economic Review},
volume = {63},
number = {4},
pages = {1471-1525},
doi = {https://doi.org/10.1111/iere.12575},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/iere.12575},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/iere.12575},
year = {2022}
}

@article{10.1093/restud/rdae011,
    author = {Folini, Doris and Friedl, Aleksandra and Kübler, Felix and Scheidegger, Simon},
    title = "{The Climate in Climate Economics*}",
    journal = {The Review of Economic Studies},
    pages = {rdae011},
    year = {2024},
    month = {01},
    issn = {0034-6527},
    doi = {10.1093/restud/rdae011},
    url = {https://doi.org/10.1093/restud/rdae011},
    eprint = {https://academic.oup.com/restud/advance-article-pdf/doi/10.1093/restud/rdae011/56663801/rdae011.pdf},
}

@article{chen2023deep,
  title={Deep surrogates for finance: With an application to option pricing},
  author={Chen, Hui and Didisheim, Antoine and Scheidegger, Simon},
  journal={Available at SSRN 3782722},
  year={2023}
}