3 resultados para seemingly unrelated regressions
em CentAUR: Central Archive University of Reading - UK
Resumo:
Conventional seemingly unrelated estimation of the almost ideal demand system is shown to lead to small sample bias and distortions in the size of a Wald test for symmetry and homogeneity when the data are co-integrated. A fully modified estimator is developed in an attempt to remedy these problems. It is shown that this estimator reduces the small sample bias but fails to eliminate the size distortion.. Bootstrapping is shown to be ineffective as a method of removing small sample bias in both the conventional and fully modified estimators. Bootstrapping is effective, however, as a method of removing. size distortion and performs equally well in this respect with both estimators.
Resumo:
We examine whether macroeconomic factors contain significant information for bank loan contracting terms and conditions (T&Cs), over and above that of standard firm-specific or country-level institutional factors. Our estimation is based on a seemingly unrelated mixed-processes methodology that accommodates two salient data properties: (i) the fact that loan contract terms are determined jointly as a single lending contract, and (ii) the fact that the elements of loan T&Cs are generated by different distributional formats. Our findings indicate that cross-country variation accounts for a significant portion of observed variation in loan T&Cs. In addition, macroeconomic fundamentals significantly explain the “package” of loan T&Cs offered to corporate borrowers, with this effect being distinct from any influence that T&Cs receive from firm-specific factors, and also from country-specific institutional factors.
Resumo:
We consider the case of a multicenter trial in which the center specific sample sizes are potentially small. Under homogeneity, the conventional procedure is to pool information using a weighted estimator where the weights used are inverse estimated center-specific variances. Whereas this procedure is efficient for conventional asymptotics (e. g. center-specific sample sizes become large, number of center fixed), it is commonly believed that the efficiency of this estimator holds true also for meta-analytic asymptotics (e.g. center-specific sample size bounded, potentially small, and number of centers large). In this contribution we demonstrate that this estimator fails to be efficient. In fact, it shows a persistent bias with increasing number of centers showing that it isnot meta-consistent. In addition, we show that the Cochran and Mantel-Haenszel weighted estimators are meta-consistent and, in more generality, provide conditions on the weights such that the associated weighted estimator is meta-consistent.