Learn Quantitative Methods In Economics? In a series of entries I will present a brief guide of recommendations to learn quantitative methods in economics. My main objective is that such a guide be useful for those students who feel the need to complete the scarce training in this area offered by many of the undergraduate curricula of the faculties of economics of Spain. A secondary objective is that the guide can serve third parties interested in these issues, from economists who have been engaged in other tasks to readers of different fields of knowledge for a long time.
Quantitative Methods In Economics
During the last years I have often met with the request of such a guide. Without going any further, last week and following my entry on machine learning, several readers left comments indicating interest in it. It is a task that I face with some fear because it is a more complex task than it seems.
First, by the mere size of the effort. In the space of an entry in NeG I can only scratch the surface of the multiple topics to be treated and I run the risk of confusing rather than helping if I am not careful in my recommendations.
Second, because of the wide variety of existing books and resources. There are, for example, many more quality books in real analysis than in macroeconomics. While one wants to learn, let’s say, modern growth theory, there are only 3 or 4 books that need to be handled, a couple of dozens of very good real analysis books circulate at this moment. Selecting one or the other is more the result of personal experience (which book do I use?) Than of the clear superiority of one text over the others. I therefore ask the readers to be especially careful in assessing my recommendations further from what they deserve. This guide could be rewritten with totally different books and be much better. This is the way to Quantitative Methods In Economics.
Third, and perhaps the main reason, because learning quantitative methods is a challenge if it has to be done alone. If a student wants to learn, let’s say, global economic history, the work is simpler. Reading carefully a selection of 5 or 6 books can be reached with some ease to more than adequate knowledge of the area, for example, to start a doctoral program in this field with guarantees. Of course it is better to be able to sit in the class of some of the leaders in this area and learn directly from them. But if circumstances prevent such a fortune, the reading remedy covers the situation with some solvency. In comparison, without anyone explaining to you the subtleties of a theorem or how to write a proof correctly, it is much more difficult to attain the necessary mathematical looseness and maturity. When I look at my own classes throughout my university career I remember as much more important to my professors of econometrics pointing out one idea or the other than to my professors of industrial organization doing the same thing. And not because this second area is less important or my professors were worse, simply because of the higher percentage of added value of the classes on the total content of the subject in econometrics than in the industrial organization. Perhaps for this reason, there are almost no MOOCs sufficiently advanced on quantitative issues. And not because this second area is less important or my professors were worse, simply because of the higher percentage of added value of the classes on the total content of the subject in econometrics than in the industrial organization. Perhaps for this reason, there are almost no MOOCs sufficiently advanced on quantitative issues. And not because this second area is less important or my professors were worse, simply because of the higher percentage of added value of the classes on the total content of the subject in econometrics than in the industrial organization. Perhaps for this reason, there are almost no MOOCs sufficiently advanced on quantitative issues. This is another way to Quantitative Methods In Economics.
Learn Quantitative Methods In Economics
Finally, three “rule of the game”. One, my objective reader is an economist, defined here as someone who is dedicated to the study and application of economics, either in a university or in a public or private institution. Therefore, I exclude people dedicated to company management. The fact that in Spain the word “economist” is used to designate both an economist in the IMF and a manager of a portfolio of securities seems to me an error. Not because the work of the latter lacks merit or social utility, but because they are different things and mixing them confuses and gives rise to errors. At my university, Penn(as in many other universities), the studies of economics and business management are not even in the same school (a school is more or less equivalent to a faculty in Spain, although not exactly): economics is in science and literature and business management in the business school. And although there is no doubt that there is a relationship between the two fields (I teach a class in the business school myself), the fact that both areas are functionally separated serves both of us well. That is why while readers interested in business management can find something useful in the following paragraphs, they should look for other complementary sources of information more directly addressed to them. Similarly, if the reader is a mathematician, he may feel angry at my forgetting of fundamental fields of his science. The explanation is that this guide is not to train a mathematician, it is for an economist. Knowing number theory may be precious but it has little use in economics (which is why I have criticized the program, for example, double degree of economics and mathematics of the Computes , which seems more an accumulation of subjects than a rational structure of studies with a specific objective). This is the other way to Quantitative Methods In Economics.
Two, I’m going to quote books in English. Already on other occasions I have expressed my skepticism with many of the manuals written in Spanish (or translated). For all those areas of knowledge that are international (and the actual analysis is the same in Spain, China or Kenya), the option should always use internationally recognized manuals in English and in the world. That way we would save ourselves more than one dislike in this Spanish university our so castiza.
Three, I will avoid (without being doctrinaire) books “mathematics for economists“. This is something I learned from Leo Hurwicz in Minnesota. One has to read mathematics books written by mathematicians, which for that they know about the subject.
After these warnings, I can begin. Today I will cover the material that would give an undergraduate student excellent math training along with certain more advanced subjects. This training is the knowledge necessary to successfully face the study of the topics dealt with in the later entries of this series and more properly quantitative. In the second entry I will cover probability (including measurement theory), statistics and econometrics. In the third entry I will deal with numerical methods. Finally, in two final entries, I will focus on computing. I will not cover in the series, only to delimit the terrain to more manageable magnitudes, eminently formal subjects like theory of games but that are more properly of substantive economy that of pure methods. Maybe one of my co-editors will be encouraged. This is also another way to Quantitative Methods In Economics.
The foundation of any training is to get a good level of calculation. And, in addition, this has to be more focused on the understanding of concepts than on the ability to quickly solve integrals or derivatives. This is due to the fact that the calculation will be the foundation of subsequent subjects as well as the existence of programs that solve many of the operations that were previously done by hand. While attaining skill in mere manipulations is important (contrary to what is sometimes asserted, I do not believe that there is true understanding of a concept until it has been used repeatedly, often mechanically), it does not make much sense to an economist spend hours and hours completing long lists of integral exercises like those that appear in many books.
A very common textbook is Calculus (8th Edition) by James Stewart. This other usual suspect is the one given, for example, in Penn’s math department. Both books cover from the contents that were previously given in the baccalaureate (functions of a variable, introduction to differential and integral calculus) to a basic treatment of differential equations.
I have always preferred (and as you can see in the picture that I have just taken, they are the two volumes that I have always at hand), the work of Tom Apostol (who certainly died a few months ago), volume 1 and 2. The Apostle is very much his own (he begins with integral calculus and then continues with differential calculus), but he is much more careful in presenting the material and less focused on “preparing for the exam”. Another rigorous calculation book is the Spivak , although this is may be harder to find. This is one of the best way to Quantitative Methods In Economics.
Working well these books is the equivalent of two or three semesters / semester of class according to the initial level of each (semesters / semesters in this entry are defined as in many Spanish universities as 15 weeks of classes, which discounted the holidays, which it corresponds to about 56 classroom hours, in Penn, a semester has 28 80 minute sessions plus 14 50 minute practice sessions, both already discounted 10 minutes of changing class).
The basic calculus material can be completed with a good book of differential equations ( this one by Morris Tenenbaum and Harry Pollard is cheap and despite its years covers what one has to know) and another one of equations of differences (like this one from Saber Elaydi ). The essential material can be given in an additional semester. A class in partial differential equations is less important for economists. Anyway, a simple standard textbook is this one. This is the best way to Quantitative Methods In Economics.