Economia
Lendo o texto "Mathematica as an Enviroment for Doing Economics and Econometrics", de David Belsley, do Boston College, me deparei com este trecho que vale a pena reproduzir.
É útil para quem quer trabalhar com economia computacional e está em dúvida sobre qual software (ou linguagem de programação) escolher para investir seu tempo de estudo.
"Numerous environments exist that will be of interest to graduate students in economics. These include such packages as GAUSS, MATLAB, GAMS, Mathematica, Maple, Derive, Reduce, Macsyma, RATS, TROLL, PCGIVE, Ox, SAS, SPSS, S, S+, and Stata. While each of these is potentially useful, they are not all the same type of environ. At the risk of oversimplifying, the rst two, GAUSS and MATLAB are quite specially designed programming environments for doing matrix-type calculations easily and efectively.
GAMS is also a special-purpose environment; it is designed for building and manipulating large-scale economic models. Similarly, the last nine are basically statistical packages, allowing one to do standard, and not so standard, statistical calculations with relative ease and exibility. Among these, RATS, TROLL, and PCGIVE are more econometrically oriented, and S+ is oriented towards data analysis. Stata is somewhere between a statistical package and a fully extensible econometrics programming language. The remaining products, particularly Mathematica, Macsyma, and Maple, are more general environments that allow for a wide spectrum of operations, encompassing most of what has been mentioned, albeit perhaps at the cost of some e ciency in carrying out speci c tasks. Among these, Mathematica and Maple are arguably the two most popular and widely used.
Thus, the strategy I suggest would point toward selecting an environment like Mathematica or Maple.
These provide powerfulmodeling, graphical, statistical, and programming facilities that allow the user, within a single environment, to do almost everything. And even if more special-purpose routines are eventually called for, they allow for rapid prototyping and testing of projects that makes subsequent implementation in other environments very much easier and surer.
Both Mathematica and Maple, for very good reasons, have their devoted adherents. I happen to be a Mathematica sort, and it is Mathematica that I want to deal with here. It is not, however, my purpose to "sell" Mathematica. Rather I want to try to elucidate for those who are considering computational environments what Mathematica's pros and cons are for use in economic research. To this end, I will try to explain some of Mathematica's salient features and illustrate the kinds of economic research that have successfully been accomplished using Mathematica."
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Economia
Por que usar o Mathematica para aplicações em Economia e Econometria?
Lendo o texto "Mathematica as an Enviroment for Doing Economics and Econometrics", de David Belsley, do Boston College, me deparei com este trecho que vale a pena reproduzir.
É útil para quem quer trabalhar com economia computacional e está em dúvida sobre qual software (ou linguagem de programação) escolher para investir seu tempo de estudo.
"Numerous environments exist that will be of interest to graduate students in economics. These include such packages as GAUSS, MATLAB, GAMS, Mathematica, Maple, Derive, Reduce, Macsyma, RATS, TROLL, PCGIVE, Ox, SAS, SPSS, S, S+, and Stata. While each of these is potentially useful, they are not all the same type of environ. At the risk of oversimplifying, the rst two, GAUSS and MATLAB are quite specially designed programming environments for doing matrix-type calculations easily and efectively.
GAMS is also a special-purpose environment; it is designed for building and manipulating large-scale economic models. Similarly, the last nine are basically statistical packages, allowing one to do standard, and not so standard, statistical calculations with relative ease and exibility. Among these, RATS, TROLL, and PCGIVE are more econometrically oriented, and S+ is oriented towards data analysis. Stata is somewhere between a statistical package and a fully extensible econometrics programming language. The remaining products, particularly Mathematica, Macsyma, and Maple, are more general environments that allow for a wide spectrum of operations, encompassing most of what has been mentioned, albeit perhaps at the cost of some e ciency in carrying out speci c tasks. Among these, Mathematica and Maple are arguably the two most popular and widely used.
Thus, the strategy I suggest would point toward selecting an environment like Mathematica or Maple.
These provide powerfulmodeling, graphical, statistical, and programming facilities that allow the user, within a single environment, to do almost everything. And even if more special-purpose routines are eventually called for, they allow for rapid prototyping and testing of projects that makes subsequent implementation in other environments very much easier and surer.
Both Mathematica and Maple, for very good reasons, have their devoted adherents. I happen to be a Mathematica sort, and it is Mathematica that I want to deal with here. It is not, however, my purpose to "sell" Mathematica. Rather I want to try to elucidate for those who are considering computational environments what Mathematica's pros and cons are for use in economic research. To this end, I will try to explain some of Mathematica's salient features and illustrate the kinds of economic research that have successfully been accomplished using Mathematica."
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- A Impossibilidade De Prognosticar A Economia
His first book was on Economic Forecasting (1928), which unfortunately has never been translated into English. He presented a biting and insightful analysis as to why quantitative models would never be able to successfully predict the economic future....
- Anscombe's Quartet
Anscombe's quartet comprises four datasets that have identical simple statistical properties, yet appear very different when graphed. The datasets make a very good case for visualizing data, as the major differences cannot be found with a simple...
- Economia Financeira Quantitativa
Quantitative Financial Economics provides a comprehensive introduction to models of economic behaviour in financial markets, focusing on analysis in discrete time. Following the huge success of the first edition, this second edition has been fully...
- 10 Características Para Ter Sucesso
The 10 Skills That Will Get You Hired In 2013 By Meghan Casserly | Forbes Here, the 10 most critical job skills to parlay in your job search for 2013: No. 1 Critical Thinking (found in 9 out of the 10 most in-demand jobs) Using logic and reasoning...
- Macroeconomia Moderna: Lições E Desafios
Alex divulga um artigo interessante do Kocherlakota (via Mankiw), professor da Universidade de Minnesota e presidente do Fed de Minneapolis. O artigo é muito bom e aqui vão alguns comentários: i) Curiosamente, bons economistas costumam beber da mesma...