Panel data is commonly used for the numerical response variables, while literature forecasting categorical variables on panel structure still challenging to find. Forecasting important because it helpful government policies. This study aimed forecast multiclass or structure. The proposed models were autoregressive multinomial logit and C5.0. strategy applied so that two could be was add effects...

This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and c...

Using micro panel data, labor market transitions are analyzed for the EU-member states by cumulative year-by-year transition probabilities. As female (non-)employment patterns changed more dramatically than male employment in past decades, the analyses mainly refer to female labor supply. In search for important determinants of these transitions, six EUcountries with different labor market-regi...

The failure rate of Small and medium enterprises (SMEs), is high in Sweden. Around 6000 SMEs go into bankruptcy every year. This paper attempts to identify the main determinants that are perceived to have contribution to the failure of Swedish SMEs. The research is in principle based on the analysis of panel data matched sample consisting of 1991 bankrupted and 1991 nonbankrupted Swedish SMEs. ...

This paper explains how to calculate adjusted risk ratios and risk differences when reporting results from logit, probit, and related nonlinear models. Building on Stata’s margins command, we create a new post-estimation command adjrr that calculates adjusted risk ratios (ARR) and adjusted risk differences (ARD) after running logit or probit models with either binary, multinomial, or ordered ou...

DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. DCM is a class written in Ox, that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cr...

Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups’ test score distributions from such data. Because the scale of HETOP estimates is indeterminate up...

This paper presents an extension to the fixed-effect Logit for panel-data discrete-choice models, where the error component structure is multiplicative (individual effects multiplied by time effects). In linear models with such an error-component structure as investigated by Ahn, Lee and Schmidt (2001), usual fixed-effect estimators are generally inconsistent. We propose a conditional Logit est...