A new class of regression type models termed essentially linear models is proposed. The class is characterized by geometric considerations. Within the class the distribution of the maximum likelihood estimator is easily approximated by a natural extension of the p*-formula even though the MLE...

A new class of regression type models termed essentially linear models is proposed. The class is characterized by geometric considerations. Within the class the distribution of the maximum likelihood estimator is easily approximated by a natural extension of the pstar-formula even though the MLE...

We provide general results for the asymptotic variance of regression coefficients computedfrom a sample drawn from a finite population. We encompass the potentialoutcomes and classic regression frameworks allowing for both heterogeneous treatmenteffects and random-across-resampling shocks....

We provide general results for the asymptotic variance of regression coefficients computed from a sample drawn from a finite population. We encompass the potential outcomes and classic regression frameworks allowing for both heterogeneous treatment effects and random-across-resampling shocks....

The intuition behind linear regression can be difficult for students to grasp particularly without a readily accessible context. This paper uses basketball statistics to demonstrate the purpose of linear regression and to explain how to interpret its results. In particular, the student will...

In this paper we show that the answer to the question in the title is affirmative, i.e. seasonal adjustment increases the probabilities in a Markov switching regime model of staying in the same regime. This phenomenon is illustrated through Monte Carlo Simulations and with two examples...

Theil (1968) proposed a transformation of regression residuals so that they are best (minimizes the trace of its covariance matrix), linear, unbiased and subject to the constraint that its covariance matrix is scalar (BLUS) in the sense that it is proportional to the identity matrix. Despite...

In this paper we show that the answer to the question in the title is affirmative, i.e. seasonal adjustment increases the probabilities in a Markov switching regime model of staying in the same regime. This phenomenon is illustrated through Monte Carlo Simulations and with two examples...

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The...

In this paper we propose a new bootstrap, or Monte-Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages,...