Categorical Data

A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and conditional logit, nested logit, multinomial probit, and random parameters logit. The last two models are estimated using simulation or Bayesian methods. For ordered data, standard multinomial models are ordered logit and probit, or count models are used if ordered discrete data are actually a count.binary data, multinomial, logit, probit, count data

Efficient conjoint choice designs in the presence of respondent heterogeneity.

The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice designs for mixed logit models in the presence of respondent heterogeneity. With this new algorithm, the construction of semi-Bayesian D-optimal mixed logit designs with large numbers of attributes and attribute levels becomes practically feasible. The results from the comparison of eight designs (ranging from the simple locally D-optimal design for the multinomial logit model and the nearly orthogonal design generated by Sawtooth (CBC) to the complex semi-Bayesian mixed logit design) across wide ranges of parameter values show that the semi-Bayesian mixed logit approach outperforms the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it was found that semi-Bayesian mixed logit designs constructed with large heterogeneity parameters are most robust against the misspecification of the values for the mean of the individual level coefficients for making precise estimations and predictions.Keywords:semi-Bayesianmixedlogitdesign,heterogeneity,predictionaccuracy,multinomiallogitdesign,model-robustdesign,D-optimality,algorithmAlgorithm; D-Optimality; Heterogeneity; Model-robust design; Multinomial logit design; Prediction accuracy; Semi-Bayesian mixed logit design;

On the Equivalence of Location Choice Models: Conditional Logit, Nested Logit and Poisson

It is well understood that the two most popular empirical models of location choice - conditional logit and Poisson - return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region's gain is the other regions' loss. In contrast, the Poisson model implies a positive-sum economy, in which one region's gain is no other region's loss. We also show that all intermediate cases can be represented as a nested logit model with a single outside option. The nested logit turns out to be a linear combination of the conditional logit and Poisson models. Conditional logit and Poisson elasticities mark the polar cases and can therefore serve as boundary values in applied research.firm location, residential choice, conditional logit, nested logit, Poisson count model

FORECASTING MARKET SHARE USING A FLEXIBLE LOGISTIC MODEL

There is a strong competition from low-priced imported catfish fillets resulting in a declining market share for domestic farm-raised catfish fillets. To match the competition, catfish processors are embarking on pricing policy measures that are volume-oriented instead of profit- or image-oriented. This could be an effective short-run pricing policy measure for optimal long-run sustainability and profitability of the industry. Volume pricing strategies are aimed at meeting target sales volumes or market shares. This paper explores and compares the performance of the standard logit, the inverse power transformation (IPT) logit and the logarithmic version of the inverse power transformation logit models in terms of generating forecasts for market share of U.S. farm-raised catfish fillets. The results suggest a better performance of the log-IPT in every aspect compared to the linear standard logit and the IPT logit models.market share, forecasting, flexible logit, Marketing, Q130, C250, C530,

A note on the linear, logit and probit functional form of the labour force participation rate equation

The commonly used specification in regional economic research on labour force participation is the linear probability function. An important alternative recommended in the Handbook of Regional and Urban Economics in the contribution of Isserman et al. (1986) on `Regional Labor Market Analysis' is the logit probability function. Their argument for the logit probability function is as follows. Given that economic theory on labour force participation does not suggest to pick one functional form over another and that the parameters of the logit probability function are estimable by OLS under the usual assumptions about the error term, the benefit of the logit probability function is that any estimated value for L lies within the logical bounds [0,1]. This feature is particularly desirable in a forecasting context when out of sample data might otherwise potentially yield absurd labour force participation rates. In this note two counter-arguments are gathered against using the logit probability function which are lacking in the Handbook of Regional and Urban Economics. Furthermore, it is shown that the logit probability function in this discourse can be replaced by the probit probability function equally well. Keywords: logit, probit, labour force participation rate.

Angler Heterogeneity and the Species-Specific Demand for Marine Recreational Fishing

In this study we assess the viability of single-species recreation demand models given commonly available data sets. Using the 2000 MRFSS southeast intercept data combined with the economic add-on, we determine that the MRFSS data will support only a few species-specific recreation demand models. Considering species of management interest in the southeast, we focus on dolphin, king mackerel, red snapper and red drum. We examine single-species recreational fishing behavior using random utility models of demand. We explore mixed logit (i.e., random parameter) logit and finite mixture (i.e., latent class logit) models for dealing with angler heterogeneity. We compare these to the commonly used conditional and nested logit models in terms of the value of catching (and keeping) one additional fish. Mixed logit models illustrate that the value of catch can be highly heterogeneous and, in some cases, can include both positive and negative values. The finite mixture model generates value estimates that were some times strikingly different than conditional, nested and mixed logit models. Preference heterogeneity is significant within the MRFSS data. We find evidence that single-species models outperform multiple species models and recreational values differ. Key Words: marine recreational fishing, single-species demand, preference heterogeneity models

Detecting financial distress

This paper examines two types of statistical tests, which are multiple discriminant analysis (MDA) and the logit model to detect financially distressed companies. Comparison between the two statistical tests is implemented to identiy factors that could differentiate financially distressed companies from the healthy company. Among the fifteen explanators, M D A shows that the current ratios, net income to total asset, and sales to current asset, are the indicators of financially distressed companies. Other than net income to total asset, the logit model provides two different ratios which are shareholders’filnd to total liabilities, and cash flow from financing to total liabilities, to identi@ financially distressed companies. It zuasfound that the logit model could accurately predict 91.5% of the estimation sample and 90% of the holdout sample whereas the discriminant model shows an overall accuracy rate of 84.5% and 80% for the estimatiorl and the holdout sample respectively

Dual Income Tax Reform in Germany. A Microsimulation Approach

This paper assesses the impact on household labor supply of a Dual Income Tax reform in Germany. It relies on GMOD, a population-based tax-benefit microsimulation model, and uses flexible mixed logit simulation estimators.Dual Income Tax, Labor Supply, Mixed Logit