| bibtype |
J -
Journal Article
|
| ARLID |
0574253 |
| utime |
20240402214241.3 |
| mtime |
20230814235959.9 |
| SCOPUS |
85161338880 |
| WOS |
001021137800001 |
| DOI |
10.1016/j.jebo.2023.05.040 |
| title
(primary) (eng) |
Moment set selection for the SMM using simple machine learning |
| specification |
| page_count |
26 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0251194 |
| ISSN |
0167-2681 |
| title
|
Journal of Economic Behavior & Organization |
| volume_id |
212 |
| volume |
1 (2023) |
| page_num |
366-391 |
| publisher |
|
|
| keyword |
Agent-based model |
| keyword |
Machine learning |
| keyword |
Simulated method of moments |
| keyword |
Stepwise selection |
| author
(primary) |
| ARLID |
cav_un_auth*0453094 |
| name1 |
Žíla |
| name2 |
Eric |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0293468 |
| name1 |
Kukačka |
| name2 |
Jiří |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-14817S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0397554 |
|
| abstract
(eng) |
This paper addresses the moment selection issue of the simulated method of moments, an estimation technique commonly applied to intractable agent-based models. We develop a simple machine learning extension reducing arbitrariness and automating the moment choice. Two algorithms are proposed: backward stepwise moment elimination and forward stepwise moment selection. The methodology is tested using simulations on a Markov-switching multifractal framework and two popular financial agent-based models with increasing complexity. We find that both algorithms can identify multiple moment sets that outperform all benchmark sets. Moreover, we achieve considerable in-sample estimation precision gains of up to 66 percent for agent-based models. Finally, an out-of-sample empirical exercise with S&P 500 data strongly supports the practical applicability of our methodology as the estimated models pass the validity test of overidentifying restrictions. |
| result_subspec |
WOS |
| RIV |
JC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2024 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0344591 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
ECONOMICS |
| mrcbT16-j |
1.089 |
| mrcbT16-D |
Q2 |
| arlyear |
2023 |
| mrcbU14 |
85161338880 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001021137800001 WOS |
| mrcbU63 |
cav_un_epca*0251194 Journal of Economic Behavior & Organization Roč. 212 č. 1 2023 366 391 0167-2681 1879-1751 Elsevier |
|