A simple demonstration of the existence of the jordan canonical form for any square matrix

All the demonstrations known to this author of the existence of the Jordan Canonical Form are somewhat complex - usually invoking the use of new spaces, and what not. These demonstrations are usually too difficult for an average Mathematics student to understand how he or she can obtain the Jordan Canonical Form for any square matrix. The method here proposed not only demonstrates the existence of such forms but, additionally, shows how to find them in a step by step manner. I do not claim that the following demonstration is in any way “elegant” (by the standards of elegance in fashion nowadays among mathematicians) but merely simple (undergraduate students taking a fist course in Matrix Algebra would understand how it works).

Curso de matematica para economistas, Capitulos I e II: funções, algebra linear e aplicações

Os autores objetivam, com este trabalho preliminar, bem como com aqueles que lhe darão continuidade, na sequência de composição de um livro de matemática para economistas, registrar as suas experiências ao longo dos últimos anos ministrando cadeiras de matemática nos cursos de pós-graduação em economia da Fundação Getúlio Vargas, da UFF (Universidade Federal Fluminense) e da PUC-RJ. Reveste-se de constante repetição em tais cursos a discussão sobre que pontos abordar, bem como com qual grau de profundidade, e em que ordem. É neste sentido que os autores esperam, com a sequência didática que aqui se inicia, trazer alguma contribuição para o assunto.

A class of improved heteroskedasticity-consistent covariance matrix estimators

The heteroskedasticity-consistent covariance matrix estimator proposed by White (1980), also known as HC0, is commonly used in practical applications and is implemented into a number of statistical software. Cribari–Neto, Ferrari & Cordeiro (2000) have developed a bias-adjustment scheme that delivers bias-corrected White estimators. There are several variants of the original White estimator that also commonly used by practitioners. These include the HC1, HC2 and HC3 estimators, which have proven to have superior small-sample behavior relative to White’s estimator. This paper defines a general bias-correction mechamism that can be applied not only to White’s estimator, but to variants of this estimator as well, such as HC1, HC2 and HC3. Numerical evidence on the usefulness of the proposed corrections is also presented. Overall, the results favor the sequence of improved HC2 estimators.