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Dynamic threshold modelling and the US business cycle
Author(s) -
de Carvalho M.,
Turkman K. F.,
Rua A.
Publication year - 2013
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12008
Subject(s) - business cycle , econometrics , generalized pareto distribution , unemployment , salient , economics , key (lock) , computer science , extreme value theory , pareto principle , mathematics , statistics , macroeconomics , operations management , computer security , artificial intelligence
Summary. Leading economic indicators are often used to anticipate changes in key economic variables. Understanding the dynamics of these indicators is of primary interest for policy‐making objectives and for sustainable economic welfare. We are concerned with the problem of setting a dynamic threshold above which the value of leading indicators would be considered as extreme. We propose a dynamic threshold modelling approach based on fractionally integrated processes where a semiparametric method is used to determine the amount of differencing that is required to obtain a weakly stationary process—to which standard methods of statistics of extremes apply. Given that our approach is linked to the Box–Jenkins method, we refer to the procedure proposed and applied herein as the Box–Jenkins–Pareto procedure. We use our approach to analyse the weekly number of unemployment insurance claims in the USA and explore the connection between its threshold exceedances and the US business cycle.