nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2013‒05‒22
four papers chosen by
Edoardo Marcucci
Universita' di Roma Tre

  1. Too many skew normal distributions? The practitioner’s perspective By Wojciech Charemza; Carlos Diaz Vela; Svetlana Makarova
  2. Testing for Intertemporal Nonseparability By Matthew Polisson; Ian Crawford
  3. Determinants of Consumer Sentiment: Evidence from Household Survey Data By Kajal Lahiri; Yongchen Zhao
  4. Nonparametric Inference for Max-Stable Dependence. By Segers, Johan

  1. By: Wojciech Charemza; Carlos Diaz Vela; Svetlana Makarova
    Abstract: The paper tackles the issue of possible misspecification in fitting skew normal distributions to empirical data. It is shown, through numerical experiments, that it is easy to choose a distribution which is different from this which actually generated the sample, if the minimum distance criterion is used. It is suggested that, in case of similar values of distance measures obtained for different distributions, the choice should be made on the grounds of parameters’ interpretation rather than the goodness of fit. This is supported by empirical evidence of fitting different skew normal distributions to the estimated monthly inflation uncertainties for Belarus, Poland, Russia and Ukraine.
    Keywords: skew normal distribution; simulated minimum distance estimators; inflation uncertainties; monetary policy in Eastern Europe
    JEL: C46 E52 E37
    Date: 2013–05
  2. By: Matthew Polisson; Ian Crawford
    Abstract: This paper presents a nonparametric analysis of intertemporal models of consumer choice that relax consumption independence. We compare the revealed preference conditions for the intertemporally nonseparable models of rational habit formation and rational anticipation. We show that these models are nonparametrically equivalent in the usual empirical setting.
    Keywords: Anticipation; habits; revealed preference; time separability
    JEL: D11 D12 D91
    Date: 2013–05
  3. By: Kajal Lahiri; Yongchen Zhao
    Abstract: We study the information content of the five components of the University of Michigan¡¯s Index of Consumer Sentiment and identify the main determinants of these measures, using semiparametric ordered choice models and household data from the Surveys of Consumers from January 1978 to September 2012. Our findings suggest that consumers¡¯ own perceptions and expectations, as measured by other survey questions in the Surveys of Consumers, are the most important determinants of the sentiment index. After this set of factors is controlled for, consumers¡¯ demographic characteristics, aggregate macroeconomic variables, and professional forecasts account for little in addition. We also find that the sentiment components about the overall economic conditions are less sensitive to consumers¡¯ own views and characteristics than the components about consumers¡¯ household financial situations. These findings could motivate the use of consumer sentiment measures in a variety of applications, including forecasting consumption expenditures.
    Date: 2013
  4. By: Segers, Johan
    Abstract: The choice for parametric techniques in the discussion article is motivated by the claim that for multivariate extreme-value distributions, “owing to the curse of dimensionality, nonparametric estimation has essentially been confined to the bivariate case” (Section 2.3). Thanks to recent developments, this is no longer true if data take the form of multivariate maxima, as is the case in the article. A wide range of nonparametric, rank-based estimators and tests are nowadays available for extreme-value copulas. Since max-stable processes have extreme-value copulas, these methods are applicable for inference on max-stable processes too. The aim of this note is to make the link between extreme-value copulas and max-stable processes explicit and to review the existing nonparametric inference methods.
    Date: 2012

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