Bc. Juraj Szitás

Master's thesis

Fiscal multipliers through machine learning

Fiscal multipliers through machine learning
Abstract:
V tejto práci sú prezentované nové metódy pre odhad tzv. treatment efektov vďaka novým poznatkom z oblasti neparametrických odhadov, ktoré sú zbežne známe ako "strojové učenie". Tieto sú diskutované na začiatku práce, mimo krátkeho odklonu počas ktorého sú diskutované fiškálne multiplikátory, a bežné metódy ich odhadu. Následne je demonštrované ako sa tieto nové metódy strojového učenia dajú použiť …more
Abstract:
This work presents new methods for estimating treatment effects through recent breakthroughs in non-parametric methods commonly known as 'machine learning'. These are exposed in the first few chapters, barring a short discourse into the topic of fiscal multipliers, and common methods to estimate them. It is then shown how these new methods can be used for the estimation of fiscal multipliers, and estimated …more
 
 
Language used: English
Date on which the thesis was submitted / produced: 24. 7. 2020

Thesis defence

  • Date of defence: 10. 9. 2020
  • Supervisor: Ing. Mgr. Vlastimil Reichel
  • Reader: Ing. Jan Čapek, Ph.D.

Citation record

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Institution archiving the thesis and making it accessible: Masarykova univerzita, Ekonomicko-správní fakulta

Masaryk University

Faculty of Economics and Administration

Master programme / field:
Mathematical and Statistical Methods in Economics / Mathematical and Statistical Methods in Economics