期刊
ECONOMETRIC REVIEWS
卷 41, 期 2, 页码 207-230出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/07474938.2021.1899504
关键词
Autoregressive panel data models; (Generalized) Autoregressive conditional heteroskedasticity models; (Generalized) Random coefficient autoregressive models; Random coefficient panel models; Time-series-cross-section models; C22; C23; C24; C32; C33
Models characterized by autoregressive structure and random coefficients are potent tools for analyzing high-frequency, high-dimensional, and volatile time series data. However, the literature on such models is vast, sector-specific, and convoluted. Most models concentrate on individual data properties, yet combining various models and their sources of heterogeneity can yield greater benefits.
Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sector-specific, overlapping, and confusing. Most models focus on one property of the data, while much can be gained by combining the strength of various models and their sources of heterogeneity. We present a structured overview of the literature on autoregressive models with random coefficients. We describe hierarchy and analogies among models, and for each we systematically list properties, estimation methods, tests, software packages and typical applications.
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