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Measuring the probability of a business cycle turning point by using a multivariate qualitative hidden Markov model
Author(s) -
Gregoir Stéphane,
Lenglart Fabrice
Publication year - 2000
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(200003)19:2<81::aid-for734>3.0.co;2-l
Subject(s) - econometrics , multivariate statistics , measure (data warehouse) , markov chain , business cycle , set (abstract data type) , markov process , computer science , hidden markov model , process (computing) , point process , point (geometry) , order (exchange) , markov model , statistics , mathematics , economics , artificial intelligence , data mining , finance , macroeconomics , geometry , programming language , operating system
A two‐step procedure to produce a statistical measure of the probability of being in an accelerating or decelerating phase of economic activity is proposed. It consists of, first, an extraction of the individual linear innovations of a set of relevant macroeconomic variables whose signs are accumulated into a qualitative vector process and, second, of a factor analysis applied to this vector. The factor process is a two‐state Markov process of order one whose states are described as favourable and unfavourable. Estimated on French business surveys, this measure appears to be a competitive coincident indicator. Copyright © 2000 John Wiley & Sons, Ltd.