A replication of “A quasi-maximum likelihood approach for large, approximate dynamic factor models” (Review of Economics and Statistics, 2012)
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The authors replicate and extend the Monte Carlo experiment presented in Doz et al. (2012) on alternative (time-domain based) methods for extracting dynamic factors from large datasets; they employ open source software and consider a larger number of replications and a wider set of scenarios. Their narrow sense replication exercise fully confirms the results in the original article. As for their extended replication experiment, the authors examine the relative performance of competing estimators under a wider array of cases, including richer dynamics, and find that maximum likelihood (ML) is often the dominant method; more¬over, the persistence characteristics of the observable series play a crucial role and correct specification of the underlying dynamics is of paramount importance' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Dynamic factor models","EM algorithm","Kalman filter","principal components"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The authors replicate and extend the Monte Carlo experiment presented in Doz et al. (2012) on alternative (time-domain based) methods for extracting dynamic factors from large datasets; they employ open source software and consider a larger number of replications and a wider set of scenarios. Their narrow sense replication exercise fully confirms the results in the original article. As for their extended replication experiment, the authors examine the relative performance of competing estimators under a wider array of cases, including richer dynamics, and find that maximum likelihood (ML) is often the dominant method; more¬over, the persistence characteristics of the observable series play a crucial role and correct specification of the underlying dynamics is of paramount importance' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The authors replicate and extend the Monte Carlo experiment presented in Doz et al. (2012) on alternative (time-domain based) methods for extracting dynamic factors from large datasets; they employ open source software and consider a larger number of replications and a wider set of scenarios. Their narrow sense replication exercise fully confirms the results in the original article. As for their extended replication experiment, the authors examine the relative performance of competing estimators under a wider array of cases, including richer dynamics, and find that maximum likelihood (ML) is often the dominant method; more¬over, the persistence characteristics of the observable series play a crucial role and correct specification of the underlying dynamics is of paramount importance
Dynamic factor models; EM algorithm; Kalman filter; principal components