This paper describes numerically simple estimators that can be used to estimate binary choice and other related models (such as selection and ordered choice models) when some regressors are endogenous or mismeasured. Simple estimators are provided that allow for discrete or otherwise limited endogenous regressors, lagged dependent variables and other dynamic effects, heteroskedastic and autocor...

The paper shows that poor identifiability of parameters can arise in the context of linear panel data model with random effects and autocorrelated disturbances. This causes problems when estimating the model by (Gaussian) maximum likelihood. Corner solutions occur quite frequently for the variance of the random effects, with a consequent bimodal distribution of the other variance and of the aut...

The evolution of specific seed traits in scatter-hoarded tree species often has been attributed to granivore foraging behavior. However, the degree to which foraging investments and seed traits correlate with phylogenetic relationships among trees remains unexplored. We presented seeds of 23 different hardwood tree species (families Betulaceae, Fagaceae, Juglandaceae) to eastern gray squirrels ...

| The search for low autocorrelated binary sequences is a classical example of a discrete frustrated optimization problem. We demonstrate the eeciency of a class of evolutionary algorithms to tackle the problem. A suitable mutation operator using a preselection scheme is constructed and the optimal parameters of the strategy are determined.

In this paper we consider the problem where there is a randomized experimental design with several successive time measurements on each experimental unit. One approach to the analysis of such data is to treat time as the subplot treatment and to use a split-plot analysis of variance. Alternatively, the problem may be considered in a more general multivariate framework. Here we recognize the tim...

I N recent years, statistical process control (SPC) for autocorrelated processes has received a great deal of attention, due in part to the increasing prevalence of autocorrelation in process inspection data. With improvements in measurement and data collection technology, processes can be sampled at higher rates, which often leads to data autocorrelation. It is well known that the run length p...

Some of the most widely-investigated control charting techniques for autocorrelated data are based on time series residuals. If the mean shift in the autocorrelated process is a sudden step shift, the resulting mean shift in the residuals is time varying and has been referred to as the fault signature. Traditional residual based charts, such as a Shewhart, CUSUM, or EWMA on the residuals, do no...

The estimation of coe±cients in a simple regression model with autocorrelated errors is considered. The underlying distribution is assumed to be symmetric, one of Student's t family for illustration. Closed form estimators are obtained and shown to be remarkably e±cient and robust. Skew distributions will be considered in a future paper.

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models. Keywords—Regression model, social mood,...

This paper investigates the use of Maximum Simulated Likelihood estimation for random effects dynamic probit models with autocorrelated errors. It presents a new Stata command, redpace, for this estimator and illustrates its usage. The paper also compares the use of pseudo-random numbers and Halton sequences of quasi-random numbers for the MSL estimation of these models.