Probit and Logit Model Selection

Chen, Guo; Tsurumi, Hiroki

doi:10.1080/03610920903377799

Cited by 58 publications

(26 citation statements)

References 17 publications

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“…Chen and Tsurumi (2010) use Monte Carlo experiments to help select between probit and logit models for binary variables. They find that if unbalanced binary data is generated by a leptokurtic distribution (highly peaked with fat tails) a logit model is preferable to a probit model.10 Note that the logarithm of odds of r i,t.…”

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confidence: 99%

Do Asset Price Drops Foreshadow Recessions?

2013

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This paper examines the usefulness of asset prices in predicting recessions in the G-7 countries. It finds that asset price drops are significantly associated with the beginning of a recession in these countries. In particular, the marginal effect of an equity/house price drop on the likelihood of a new recession can be substantial. Equity price drops are, however, larger and are more frequent than house price drops, making them on average more helpful as recession predictors. These findings are robust to the inclusion of the term-spread, uncertainty, and oil prices. Lastly, there is no evidence of significant bias resulting from the rarity of recession starts.

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confidence: 99%

Do Asset Price Drops Foreshadow Recessions?

2013

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“…Chen and Tsurumi (2011) reported the difficulty of distinguishing Probit and Logit models. The authors suggest that this is only possible with a relevant number of samples.…”

Section: Discussionmentioning

confidence: 99%

“…The survival curves were set up with the data obtained from the probability of germination during the time of longevity analysis. For transformation of the survival curve in the Probit and Logit linear scales, equations (2) and (3) were used, respectively Chen and Tsurumi (2011). where Ф is the normal density curve and x is the probability of germination transformed into equivalent percentage indices ranging from 0 to 1.…”

Section: Methodsmentioning

confidence: 99%

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Probit or Logit? Which is the better model to predict the longevity of seeds?

et al. 2020

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AbstractThe prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

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“…We have also described how our work may be placed within the familiar GLMM framework. While it is beyond the scope of this paper to thoroughly discuss model selection problems involving, e.g., covariance structures or link functions, it is the author's hope that previous and ongoing GLM and GLMM research (e.g., Chen & Tsurumi, 2010) can be used to build upon the proposed work in this area. Further, while we have shown practical operationalizations of the proposed method for binary data in Section 2.6, we leave it for future work to describe the specifics of sophisticated covariance structures (i.e., H •,t 's that are more complicated than I n ) for other data types.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous and temporal autoregressive network models

Sewell¹

2018

Net Sci

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While logistic regression models are easily accessible to researchers, when applied to network data there are unrealistic assumptions made about the dependence structure of the data. For temporal networks measured in discrete time, recent work has made good advances (Almquist & Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories. This assumption can be quite strong and is sometimes difficult to justify. If time steps are rather large, one would typically expect not only the existence of temporal dependencies among the dyads across observed time points but also the existence of simultaneous dependencies affecting how the dyads of the network co-evolve. We propose a general observation driven model for dynamic networks which overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach. This approach can be shown to fit into a generalized linear mixed model framework. We propose a visualization method which provides evidence concerning the existence of simultaneous dependence. We describe a simulation study to determine the method's performance in the presence and absence of simultaneous dependence, and we analyze both a proximity network from conference attendees and a world trade network. We also use this last data set to illustrate how simultaneous dependencies become more prominent as the time intervals become coarser.KEY WORDS: dependence structures; dynamic networks; generalized linear mixed models; multivariate probit; observation driven model.

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Probit and Logit Model Selection

Cited by 58 publications

References 17 publications

Do Asset Price Drops Foreshadow Recessions?

Do Asset Price Drops Foreshadow Recessions?

Probit or Logit? Which is the better model to predict the longevity of seeds?

Simultaneous and temporal autoregressive network models

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