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Trends and cycles in macro series: The case of US real GDP
Author(s) -
Caporale Guglielmo Maria,
GilAlana Luis Alberiko
Publication year - 2022
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12278
Subject(s) - economics , gross domestic product , econometrics , macro , real gross domestic product , series (stratigraphy) , mean reversion , per capita , time series , moving average , macroeconomics , mathematics , statistics , computer science , paleontology , population , demography , sociology , biology , programming language
This paper proposes a new modeling framework capturing both the long‐run and the cyclical components of a time series. As an illustration, we apply it to four US macro series, namely, annual and quarterly real gross domestic product (GDP) and GDP per capita. The results indicate that the behavior of US GDP can be captured accurately by a model incorporating both stochastic trends and stochastic cycles that allows for some degree of persistence in the data. Both appear to be mean reverting, although the stochastic trend is nonstationary, while the cyclical component is stationary, with cycles repeating themselves every 6–10 years.