{"title":"A finite mixture analysis of structural breaks in the G-7 gross domestic product series","authors":"Alessandro Cremaschini , Antonello Maruotti","doi":"10.1016/j.rie.2023.01.001","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper we apply a clustering procedure to detect trend changes in macroeconomic data, focusing on the GDP time series for the G-7 countries. A finite mixture of regression models is considered to show different patterns and changes in GDP slopes over time in the long-trend component. Two popular trend-cycle decompositions (i.e., Beveridge and Nelson Decomposition and Hodrick and Prescott filter) are considered in a preliminary step of the analysis to stress the differences between the two methods in terms of the inferred clustering, if any. This approach can be used also to detect structural breaks or change points and it is an alternative to existing approaches in a probabilistic framework. We also discuss international changes in the GDP distribution for the G-7 countries, highlighting similarities, e.g., in break dates, aiming at adding more insights on the economic integration among countries. Our findings suggest that by looking at changes in slope over time a mixture of regression models is able to detect change points, also compared with alternative procedures.</p></div>","PeriodicalId":46094,"journal":{"name":"Research in Economics","volume":"77 1","pages":"Pages 76-90"},"PeriodicalIF":1.2000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Economics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1090944323000017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
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Abstract
In this paper we apply a clustering procedure to detect trend changes in macroeconomic data, focusing on the GDP time series for the G-7 countries. A finite mixture of regression models is considered to show different patterns and changes in GDP slopes over time in the long-trend component. Two popular trend-cycle decompositions (i.e., Beveridge and Nelson Decomposition and Hodrick and Prescott filter) are considered in a preliminary step of the analysis to stress the differences between the two methods in terms of the inferred clustering, if any. This approach can be used also to detect structural breaks or change points and it is an alternative to existing approaches in a probabilistic framework. We also discuss international changes in the GDP distribution for the G-7 countries, highlighting similarities, e.g., in break dates, aiming at adding more insights on the economic integration among countries. Our findings suggest that by looking at changes in slope over time a mixture of regression models is able to detect change points, also compared with alternative procedures.
期刊介绍：
Established in 1947, Research in Economics is one of the oldest general-interest economics journals in the world and the main one among those based in Italy. The purpose of the journal is to select original theoretical and empirical articles that will have high impact on the debate in the social sciences; since 1947, it has published important research contributions on a wide range of topics. A summary of our editorial policy is this: the editors make a preliminary assessment of whether the results of a paper, if correct, are worth publishing. If so one of the associate editors reviews the paper: from the reviewer we expect to learn if the paper is understandable and coherent and - within reasonable bounds - the results are correct. We believe that long lags in publication and multiple demands for revision simply slow scientific progress. Our goal is to provide you a definitive answer within one month of submission. We give the editors one week to judge the overall contribution and if acceptable send your paper to an associate editor. We expect the associate editor to provide a more detailed evaluation within three weeks so that the editors can make a final decision before the month expires. In the (rare) case of a revision we allow four months and in the case of conditional acceptance we allow two months to submit the final version. In both cases we expect a cover letter explaining how you met the requirements. For conditional acceptance the editors will verify that the requirements were met. In the case of revision the original associate editor will do so. If the revision cannot be at least conditionally accepted it is rejected: there is no second revision.
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