Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally weighted least squares (DWLS) and unweighted least s...

The aim of this paper is to compare through Monte Carlo simulations the finite sample properties of the estimates of the parameters of the weighted exponential distribution obtained by five estimation methods: maximum likelihood, moments, L-moments, ordinary least-squares, and weighted least-squares. The bias and mean-squared error are used as the criterion for comparison. The simulation study ...

ARfit is a collection of Matlab routines for modeling multivariate time series by autoregressive (AR) models. It provides tools for all stages of the model identiication process: statistics that aid in the selection of the model order, fast algorithms for least squares estimation of parameters, modules that produce approximate conndence regions for the estimated parameters, and routines for dia...

We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discretechoice demand model, which underlies much recent empirical work in IO. We add interactive fixed effects in the form of a factor structure on the unobserved product characteristics. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics (including price), which accommod...

We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discretechoice demand model, which underlies much recent empirical work in IO. We add interactive fixed effects in the form of a factor structure on the unobserved product characteristics. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics (including price), which accommod...

A new noise filtering approach, based on flexible least squares (FLS) estimation of an unobserved component local level model, is introduced. The proposed FLS filter has been found to perform well in Monte Carlo analysis, independently of the persistence properties of the data and the size of the signal to noise ratio, ouperforming in general even the Wiener Kolmogorov filter, which, theoretica...

Neutrino-nucleus elastic scattering is reduced in dense matter because of correlations between ions. The static structure factor for a plasma of electrons and ions is calculated from Monte Carlo simulations and parameterized with a least squares fit. Our results imply a large increase in the neutrino mean free path. This strongly limits the trapping of neutrinos in a supernova by coherent neutr...

This paper studies the generalized spatial two stage least squares (GS2SLS) estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the ca...

Accurate determination of the input function is essential for absolute quantification of physiological parameters in PET and SPECT imaging but it requires an invasive and tedious procedure of blood sampling that is impractical in clinical studies. We previously proposed a technique that simultaneously estimates kinetic parameters and the input function from the tissue impulse response functions...

In designing Monte Carlo simulation studies for analyzing …nite sample properties of econometric inference methods, one can use either IID drawings in each replication for any series of exogenous explanatory variables or condition on just one realization of these. The results will usually di¤er, as do their interpretations. Conditional and unconditional limiting distributions are often equivale...