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SUMMARY:Latent Feeling and Uncertainty of Perception and Expectations of P
 rice levels over time: A Change Point Analysis
DTSTART;VALUE=DATE-TIME:20230629T111000Z
DTEND;VALUE=DATE-TIME:20230629T113000Z
DTSTAMP;VALUE=DATE-TIME:20260818T024424Z
UID:indico-contribution-1011@cern.ch
DESCRIPTION:Speakers: Carmela  Cappelli (University of Naples Federico II)
 \nFor the analysis of ordered categorical data\, CUB modelling approach en
 tails the estimation of two main structural latent components of the ratin
 g process: feeling and uncertainty\, parameterized within a two-component 
 mixture of Binomial and uniform distributions: see Piccolo and Simone 2019
  for an overview. Featuring parameters can be possibly linked to subject c
 ovariates to determine twofold response patterns and they can be promptly 
 estimated using the EM algorithm (as implemented in the R package ‘CUB
 ’ available on CRAN).\nThe contribution aims at presenting how change po
 int detection of temporal series of estimated feeling and uncertainty can 
 be pursued to identify if and to what extent Italian people modified their
  perception and judgments of price levels from 1994 to 2019. To this goal\
 , we resort to the framework of Atheoretical Regression Trees (ART\, Cappe
 lli et al. 2008) considering the series of monthly response distributions 
 to questions:\n1-(Judgments): How do you think the price level changed ove
 r the previous 12 months? \n2-(Expectations): How do you think the price l
 evel will change over the next 12 months?\nissued by the Italian National 
 Statistical Institute (ISTAT) within the consumers’ confidence survey. R
 esponses are collected over a scale with m=5 categories (1 =`fall '\, 2 = 
 `stay about the same'\, 3 = `rise slightly'\, 4 = `rise moderately'\, 5 = 
 `rise a lot').\nPreliminary results indicate that ART is effective in part
 itioning the series into sub-intervals characterized by different levels o
 f the estimated model parameters\, allowing to study and compare over time
 \, the change points of both feeling and uncertainty. It’s worth noticin
 g that the model parameters refer to two different aspects of the responde
 nts’ perception and judgment of price level\, thus the study of their ch
 ange points may reveal that they show different number and location of bre
 ak dates providing a further and  valuable insight into the two components
  of respondents’  answers.\nPerformances of ART are also discussed compa
 ratively with those of other techniques for structural change point detect
 ion\, in particular with respect to Bai and Perron’s procedure as ART mi
 mics this procedure.\nKeywords: price expectation\; price judgment\; Atheo
 retical Regression Trees\; CUB model\; change point detection\n\nReference
 s:\nC. Cappelli\, R. N. Penny\, W. S. Rea\, M. Reale (2008). Detecting mul
 tiple mean breaks at unknown points in official time series\, MATHEMATICS 
 AND COMPUTERS IN SIMULATION\, Volume 78\, Issues 2–3\, Pages 351-356\, I
 SSN 0378-4754.\nD. Piccolo\, R. Simone (2019). The class of CUB models: st
 atistical foundations\, inferential issues and empirical evidence. STATIST
 ICAL METHOD AND APPLICATIONS\, Volume 28\, pages 389-435.\n\nhttps://indic
 o.unina.it/event/67/contributions/1011/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1011/
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