Abstract: This paper studies grouped model averaging methods for finite sample size situation. Sufficient conditions under which the grouped model averaging estimator dominates the ordinary least squares estimator are provided. A class of grouped model averaging estimators, g-class, is introduced, and its dominance condition over the ordinary least squares is established. All theoretical findin...

Examining the correlation between trade and income cannot identify the direction of causation between the two. Countries’ geographic characteristics, however, have important effects on trade, and are plausibly uncorrelated with other determinants of income. This paper therefore constructs measures of the geographic component of countries’ trade, and uses those measures to obtain instrumental va...

This work analyses the market value of second hand agricultural tractors in Spain for the period 1999-2002, with the aims of obtaining the most appropriate valuation models (through the use of ordinary least squares regression) and proposing an empirical model that estimates the true depreciation of these vehicles. Differences in tractor depreciation were studied in terms of the three horsepowe...

Starting from the one{dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random eeects in units and time. Upper bounds are derived for the rst{ order approximation to the diierence between both estimators and for the spectral norm of the diierence bet...

We use U.S. county data (3,058 observations) and 41 conditioning variables to study growth and convergence. Using ordinary least squares (OLS) and three-stage least squares with instrumental variables (3SLS-IV), we report on the full sample and metro, nonmetro, and and regional samples: (1) OLS yields convergence rates around 2%; 3SLS yields 6%–8%; (2) convergence rates vary (for example, the S...

Regression diagnostics are geared toward identifying individual points or groups of points that have an important influence on a fitted model. When fitting a model with survey data, the sources of influence are the response variable Y, the predictor variables X, and the survey weights, W. This article discusses the use of the hat matrix and leverages to identify points that may be influential i...

Ordinary least squares is unlikely to estimate growth regressions consistently. A simple alternative method is formulated. Applying ordinary least squares and this method to data on 85 countries over the period 1964-1990 reveals that ordinary least squares is strongly biased. The consistent estimates are about twice as large in magnitude as the inconsistent estimates. This finding suggests that...

This paper examines economic returns to schooling for China’s Korean minority in the urban labour market using ordinary least squares (OLS) and two-stage least squares. The OLS estimates of the returns to schooling are similar to findings from recent studies for the Chinese urban labour market. We use father’s education, mother’s education and spouse’s education to instrument for education. The...

Increasingly, social and personality psychologists are conducting studies in which data are collected simultaneously at multiple levels, with hypotheses concerning effects that involve multiple levels of analysis. In studies of naturally occurring social interaction, data describing people and their social interactions are collected simultaneously. This article discuses how to analyze such data...

When the errors in an ordinary least squares (OLS) regression model are heteroscedastic, hypothesis tests involving the regression coefficients can have Type I error rates that are far from the nominal significance level. Asymptotically, this problem can be rectified with the use of a heteroscedasticity-consistent covariance matrix (HCCM) estimator. However, many HCCM estimators do not perform ...