Indirect inference for locally stationary models

Frazier, David T.; Koo, Bonsoo

doi:10.1016/j.jeconom.2020.08.004

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, plenty-of-time series models were developed, such as the autoregressive (AR) model, moving average (MA) model, and the combination of methods autoregressive and moving average (ARMA) model to analyze economic and financial data. These linear time series models have gained popularity in various fields, such as econometrics (Frazier & Koo, 2021; Inekwe et al, 2019) and health (Xie & Djurdjanovic, 2021). However, these approaches cannot capture the nonlinear dynamic patterns of financial time series variables (Korley & Giouvris, 2021), which prompted the shift toward regime-switching models.…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications

Phoong

Khek

2022

SAGE Open

View full text Add to dashboard Cite

This study involved a systematic literature review using bibliometric analysis to examine the evolution and current trends of Markov switching studies. The bibliometric analysis was used for the descriptive, intellectual, social, and conceptual network analyses, while systematic reviews were used to identify the application of the Markov switching model. A total of 449 articles were selected and extracted from the Web of Science (WoS) between 1992 and 2021. The publication trends, most influential countries and articles, leading journals, collaboration networks, research hotspots, research fronts, coupling networks, and application of the model in various disciplines were described. This study summarized the research agenda of the Markov switching field, which would be helpful for researchers and funding agencies.

show abstract