5 comments about Time Series Analysis.

1. No complaints. I received the book before deadline and book is same as descrition. 100% recomended seller



2. I don't think there is another book out ther that would outperform this book in time series econometrics. A must have if you are a graduate student in economics.



3. I purchased "Time Series Analysis" after reading that this was the time series "Bible". This book is certainly as long as a Bible, yet, paradoxically, it doesn't seem to contain very much useful information. Every time I look something up in it, its not there.
   
   This book is almost 800 pages long and contains chapters on all of the main areas of time series analysis, including stationary ARMA models, VAR models, ARCH models, cointegration, etc. Hamilton gives all of the relevant proofs and algebra relating to these areas. However, he provides no worked examples to illustrate these concepts, which makes them very difficult to absorb. Hamilton provides some exercises at the end of each chapter (with solutions at the end of the book), but not enough to develop a working knowledge of this material.
   
   I purchased this book because I needed to fit time series models as part of my PhD thesis and because I teach a unit on time series analysis to final-year undergraduate students. I have found this book completely useless for both of these purposes. At the same time I purchased this book, I also purchased Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) and Econometric Analysis (5th Edition) and I have found both of these books to be infinitely more helpful.



4. This is a large text in time series analysis that is designed for graduate students as the author acknowledges in his preface. It deals primarily with the theory and the tools rather than providing practical applications. It does not require a Ph.D. but does require a fair amount of mathematical sophistication that comes from advanced courses in probability and statistics. There are many good books at this level. This one has some unique features. It covers the traditional ARIMA models that can be found in most texts and uses the operator notation that Box and Jenkins introduced. It adds vector autoregressions which is fairly recent material. Spectral analysis (the frequency domain approach)is also covered and asymptotic theory is presented. Linear systems (more common to econometric time series than in the standard statistical books) is covered. Topics not commonly covered in competitor texts include nonstationary cases (both univariate and multivariate)with unit roots to the characteristic equation, Bayesian approaches, heteroscedastic models including the ARCH models and the topic of cointegration originally developed by Clive Granger. The book is loaded with references to the literature and is slanted towards methods useful in econometrics. Other good books at this level include Brockwell and Davis (1987), Fuller (1976), Anderson (1971), Harvey (1981) and Shumway and Stoffer (2000). Good texts solely in the frequency domain include Bloomfeld (1976), Priestley (1981), Koopmans (1974) and Brillinger (1981). Box, Jenkins and Reinsel (1994) provides practical applications using the Box-Jenkins time domain approach.



5. Its an excellent book to study advanced time series and for theoretical understanding of Time series. A must read at the graduate level.
   
   Thanks Mr. Hamilton for writing such an excellent book

1 comments about Introductory Econometrics: A Modern Approach (with Economic Applications, Data Sets, Student Solutions Manual Printed Access Card).

1. I got it on time and it was exactly in the condition explained before

5 comments about Analysis of Financial Time Series (Wiley Series in Probability and Statistics).

1. Written by a University of Chicago professor, this book comprehensively covers times series topics relative to investment and trading-oriented finance (i.e., Wall Street money-making machines). Treatment is generally clear and thorough, but an advanced math and stat background is an absolute prerequisite for understanding the materials.
   
   S-Plus/R code is given, but strangely, there is very little on *why* and
   *when* one uses each of the techniques. Under what cirmcustances should I use or not use GARCH? What exactly is PCA good for in real-world applications? These important questions are not answered, in other words, you don't get a sense of the real-world context for these topics.



2. This book is an excellent toolbox for anyove dealing in the field of financial engineering, however, as a real toolbox, the author doesn't explain the exact use of all tools and how to interpret the results. This is why this book is for advanced users who need a well documented reference but it is not very suitable for beginners in the field. The Splus code is welcome.



3. This text is absolutely perfect for Masters students learning financial econometrics. There is a little theory, clear explanations, and quite a few real world examples. (I don't think any text would tell the reader what model to use when, because that's application-specific.) It assumes some knowledge of finance and basic econometrics/statistics, which is fair enough. To get more theory, Hamilton (1994) remains the authority, and Campbell, Lo, MacKinlay (1997) is a great introduction for PhD students, and generally an ideal companion volume to this one.



4. The coverage of the topic is broad and deep. It is one of the few introductory books that devotes some space to transfer function modeling and does so intelligibly.
   A must have for the novice as well as those more familiar with the topic that need a solid reference.



5. Professor Tsay is a student of the Wisconsin school of statisticians where he learned time series from Box and Tiao. He is an excellent lecturer and a good writer. I have attended one of the short courses he taught on time series. New models have been developed to deal with the special behavior of financial time series. Professor Tsay is always at the forefront of that research and teaches at Chicago in one of this country's top business schools. If I am correct George Tiao is also there at present.

5 comments about Microeconometrics: Methods and Applications.

1. The book pretty much covers everything (at more than a basic level) that econometricians would ideally like to know. I think covering so much material in a single book makes it ideal!!



2. If you want a book for microeconometrics such than whenever you need to look at a topic or subjects related to a topic, this is your book. If you need a very deep and comprehensive discussion about a topic, you need more than this. It may be not sufficient, but for sure it is a necessary book for anyone who does applied econometrics. I enjoy reading this book.



3. The book is useful for current line of microeconometric research. However, its presentation is similar to Greene (Econometrics Analysis). So if you are not comfortable with Green this text will not be best for you.



4. Based on previous reviews of this item, I was expected a well-written accessible textbook. Although it has far less mathematical equations than similarily situated microeconometric texts, I do not find it accessible for the less-mathematically inclined.



5. This book is simply amazing. It describes tough problems in understandable words. It is definitely and advanced book and I recommend to read an introductory book such as Wooldridge or Gujarati first. Furthermore you will find not typically econometric yet very useful quantitative methods such as survival analysis or sampling methods in "Microeconometrics". I strongly recommend this book mainly for graduate and PhD students in quantitative methods and economics.

1 comments about Schaum's Outline of Statistics and Econometrics.

1. This was an extremely useful book for the understanding of Statistics and Econometrics. Each topic had examples to show how the formulas work. The computer chapter went over the programming in SAS, Excel, and Eviews for the problems in the book. Best of all, the problems had answers. This is a must-have for beginning statistics and econometrics since it starts from scratch, and for theory students in search of an application.

5 comments about Econometric Analysis of Cross Section and Panel Data.

1. This book provides an excellent overview about state-of-the-art methodologies in econometrics. Instead of other textbooks it stresses more on conditionals and explains potential problems with underlying assumptions in more detail. As I find it sometimes hard to orientate myself, I give 4 stars.



2. This book fills a real gap for those who are interested in things like ATE, ATET and topics related to impact evaluation that are only talked about now in research papers. The explanations are very very clear and they walk you through the thinking process by which the different methods were developed. Of course, it has the traditional coverage of panel data and cross section but with the clarity that Wooldridge always delivers on his books. For all that, this is a perfect addition to your econometric libraries as it covers topics that are not discussed in traditional and introductory econometric books.



3. 
   This book has no graphs - not one. As long there are no graphs or figures, this work will remain incomplete.



4. Its an excellent book for advanced econometrics and a must read at the graduate level. Thanks Mr. Woolbridge



5. As a development economist with some experience with panel data, I found Wooldridge a great guide to the nitty-gritty of longitudinal analysis. Good theoretical and practical background. Examples used to illustrate each point. Organizational structure is simple and elegant. If you've been relying on the Stata Manual to help you with your xt- econometrics, check this out first; you'll be less likely to make a mistake.
   
   A good intermediate text, and a must for any econometrician's shelf. I'm buying a second copy - one for work and one for home. You might consider the same.

5 comments about The Reckoning.

1. If "The Reckoning" is not a classic then certainly it is an authoritative account of Nissan's assault on the U.S. market (and Ford's subsequent demise). The account of the Big Three's arrogance has been told many times since Halberstam published this book, but Halberstam deserves a lot of credit for so much of his book being so relevant to what American automakers are facing today. Insert big SUVs and big trucks any time Halberstam talks about Big Cars = Big Profits and you've got a pretty good idea of what's going on today with Ford and GM and their reluctance to come out with small, fuel-efficient vehicles in favor of high-margin SUVs. It is necessary to point out, though, that even though American auto executives were truly arrogant and complacent with their market share, Japanese manufacturing (lean production) was a paradigm shift. In order to compete on quality with the Japanese American auto would have had to spend billions of dollars on new plants and capital equipment (in essence, almost "betting the farm" that Demings' TQM was the wave of the future). The Japanese had the distinct advantage of having to start from scratch.



2. The Reckoning is a wonderful book that shows how the United States lost its dominance in the automobile industry. In true Halberstam fashion MacNamara is one of the main villains but through stunning research Halberstam paints a very clear picture of what happened. Japans attention to detail and innovation overshadow America's downfall. The big three are unable to respond and those who are running the Japanese business in the United States who were far more inventive. This book is still relevant even in today's world.



3. I'm really not into cars. However, I happened onto The Reckoning because of having read and liked earlier Halberstam books but ... and found it to be one of the best I have ever read, an absolutely great book. On more than one occasion, I have talked way too much about the remarkable stories it includes, way too long. The Ford:Nissan comparative history is a good mechanism, but the stories are the really great part - the totally hilarious (the salesman in the Port of LA changing the nameplates, trying to explain about the brakes and the engine blankets to the Japanese engineers); the absolutely tragic (Ford and his son, Edsel); the truly remarkable (the business dream team after WWII, the family actions after Edsel's death), the bizarre (pulling the chassis through a barn with a rope and measuring the elapsed time, any color as long as it's black) etc., etc. Lots of car and manufacturing and labor relations stuff, but a really readable book for anyone. And I am truly not into cars ...



4. This book is a masterpiece of narrative journalism. Based on five years of research and interviews, it tells the story of how the Japanese came to dominate the American car industry by telling the stories of key individuals, in the U.S. and Japan, who played important roles in that story. Halberstam is such a skilled writer that every one of these people comes alive on the page; you will meet the Fords and their Japanese counterparts at Nissan, and executives, car designers, union leaders, and workers in both countries. Along the way, as you get to know these people, you will learn the story not only of the automobile industry but also of American business in general, the story of how American companies abandonned the making of quality products under pressure from finance people (trained at the nation's leading business schools) who care only about stock position and short-term profits. There can be no better primer for anyone who wants to understand the economic history of America in the second-half of the twentieth century. Read it and weep--and then take a look at Eamon Fingleton's "In Praise of Hard Industry" (also published under the title "Unsustainable").



5. David Halberstam wrote in 1986 his view of the history of the carmaking industry. He focuses on Ford and Nissan. Other players get mentions here and there, but no more than that. While a broader look at the whole carmaking industry might seem better, Halberstam's approach works.
   
   No one writes this kind of history any more. Rather than lecture, Halberstam brings out details that tell the story themselves. He picks people and follows their careers, mistakes and successes, as they make their contribution to carmaking.
   
   Halberstam does not do everything perfectly. His approach has its flaws. He will boil down someone's life to a few pages, or even paragraphs, which makes the complex more simple than it should be. And much of what he writes can only be speculation -- how is he to know what Henry Ford was thinking back in the early 1900s?
   
   That said, this book captured my interest. Most books like this written 20 years ago are hard to get through. They are way past their shelf life.
   
   Not so with this one. I picked it up because I'm interested in carmaking. I put it down admiring it not just for how it treats carmaking, but for how it treats history. Well worth reading.

5 comments about The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society).

1. This book provides strong arguments that scientists often use tests of statistical significance as a ritual that substitutes for thought about how hypotheses should be tested.
   Some of the practices they criticize are clearly foolish, such as treating data which fall slightly short of providing statistically significant evidence for a hypothesis as reason for concluding the hypothesis is false. But for other practices they attack, it's unclear whether we can expect scientists to be reasonable enough to do better.
   Much of the book is a history of how this situation arose. That might be valuable if it provided insights into what rules could have prevented the problems, but it is mainly devoted to identifying heroes and villains. It seems strange that economists would pay so little attention to incentives that might be responsible.
   Instead of blaming the problems primarily on one influential man (R.A. Fisher), I'd suggest asking what distinguishes the areas of science where the problems are common from those where it is largely absent. It appears that the problems are worst in areas where acquiring additional data is hard and where powerful interest groups might benefit from false conclusions. Which leads me to wonder whether scientists are reacting to a risk that they'll be perceived as agents of drug companies, political parties, etc.
   The book sometimes mentions anti-commercial attitudes among the villains, but fails to ask whether that might be a symptom of a desire for "pure" science that is divorced from real world interests. Such a desire might cause many of the beliefs that the authors are fighting.
   The book does not adequately address concerns that if scientists in those fields abandon easily applied rules, scientists are sufficiently vulnerable to corruption that we'd end up with less accurate conclusions.
   The authors claim the problems have been getting worse, and show some measures by which that seems true. But I suspect their measures fail to capture some improvement that has been happening as the increasing pressure to follow the ritual has caused papers that would previously have been purely qualitative to use quantitative tests that reject the worst ideas.
   The book seems somewhat sloppy in its analysis of specific examples. When interpreting data from a study where scientists decided there was no effect because the evidence fell somewhat short of statistical significance, it claims the data show "St. John's-wort is on average twice as helpful as the placebo". But the data would provide evidence for that only if there were data showing that the remission rate with no treatment was zero. It's likely that some or all of the alleged placebo effect was due to effects that are unrelated to treatment. And their use of the word "show" suggests stronger evidence than is provided by the data.
   I'll close with two quotes that I liked from the book:
   "The goal of an empirical economist should not be to determine the truthfulness of a model but rather the domain of its usefulness" - Edward E. Leamer
   "The probability that an experimental design will be replicated becomes very small once such an experiment appears in print." - Thomas D. Sterling



2. Tests of statistical significance are a particular tool which is appropriate in particular situations, basically to prevent you from jumping to conclusions based on too little data. Because this topic lends itself to definite rules which can be mechanically implemented, it has been prominently featured in introductory statistics courses and textbooks for 80 years. But according to the principle "if all you have is a hammer, then everything starts to look like a nail", it has become a ritual requirement for academic papers in fields such as economics, psychology and medicine to include tests of significance. As the book argues at length, this is a misplaced focus; instead of asking "can we be sure beyond reasonable doubt that the size of a certain effect is not zero" one should think about "how can we estimate the size of the effect and its real world significance". A nice touch is the authors' use of the word oomph for "size of effect".
   
   Misplaced emphasis on tests of significance is indeed arguably one of the greatest "wrong turns" in twentieth century science. This point is widely accepted amongst academics who use statistics, but perversely the innate conservatism of authors and academic journals causes them to continue a bad tradition. All this makes a great topic for a book, which in the hands of an inspired author like Steven Jay Gould might have become highly influential. The book under review is perfectly correct in its central logical points, and I hope it does succeed in having influence, but to my taste it's handicapped by several stylistic features.
   
   (1) The overall combative style rapidly becomes grating.
   
   (2) A little history -- how did this state of affairs arise? -- is reasonable, but this book has too much, with a curious emphasis on the personalities of the individuals involved, which is just distracting in a book about errors in statistical logic.
   
   (3) The authors don't seem to have thought carefully about their target audience. For a nonspecialist audience, a lighter How to Lie With Statistics style would surely work better. For an academic audience, a more focused [logical point/example of misuse/what authors should have done] format would surely be more effective.
   
   (4) Their analysis of the number of papers making logical errors (e.g. confusing statistical significance with real-world importance) is wonderfully convincing that this problem hasn't yet gone away. But on the point "is this just an academic game being played badly, or does it have harmful real world consequences" they assert the latter but merely give scattered examples, which are not completely convincing. If people fudge data in the traditional paradigm then surely they would fudge data in any alternate paradigm; if one researcher concludes an important real effect is "statistically insignificant" just because they didn't collect enough data, then won't another researcher be able to collect more data and thereby get the credit for proving it important? Ironically, they demonstrate the harmful real world effect is of the cult is non-zero but not how large it is ......



3. I attended a seminar by McCloskey when she announced she was working on this then-upcoming book. So I knew beforehand that its style would be more like a victim-tells-all revenge than a fun-seeking discovery typical of most popular science books. The first half of the book (up to Chapter 13) did turn out to be bitter. However, at least that part was largely based on facts, such as a comprehensive count of academic papers failing to meet certain standards. The second half of the book was devoted to the biographies of key persons who led to the rise of what the authors called the "cult of statistical significance". The book lost any pretense of integrity at that point, and just started slinging muds. Gosset was portrayed as a good-natured figure who worked hard like a bee; and Fisher, a mad scientist who stole the labor of others and would attack people by any means to defend his status. At one point the authors didn't even bother to call Fisher by his name, and just referred to him as the Wasp. They also dragged Fisher's mother into the ordeal by making suggestions that she was responsible for turning Fisher into a cold-hearted person that they claimed.
   
   I was not only disgusted by this kind of tabloid sensationalism, but was also disappointed by how little useful information I got out of this long-awaited book. The authors "irrationalized" the popularization of statistical significance by framing it as the work of a cult. To further illegitimatize the use of statistical significance, they argued that it is wrong to rely on it to evaluate scientific hypotheses because (1) what we really want is how likely for a hypothesis to be true given the data, not the other way around; and (2) there are other clues just as, if not more, important, especially the effect size. These could have been reasonable positions if they did not make statistical significance a scapegoat for being a "fallacy" just because it is defined on the likelihoods of observing data given the hypotheses. As the way it is defined, statistical significance provides a measure of precision. That's all. Just because it doesn't answer all the questions of scientific interest doesn't mean it provides no useful information and certainly doesn't automatically make it a fallacy. Furthermore, many hypothesis tests used in academic researches are based on likelihood "ratios" rather than just the conditionals. At least there would be NO fallacy for the believers of the Likelihood Principle. It is quite regrettable that they fail to elaborate on such crucial information to make other people look stupid, whether it was their intention or not. As for the second point, I agree that researchers should have paid more attention to other factors, such as statistical power and sample size, IN ADDITION TO statistical significance. But I think it is misguided to hail any ban on reporting statistical significance as a heroic act of revolt as the authors did in the book. One can report all the effect sizes he wants. But it all means nothing if his inferences are what they appear to be mostly due to "bad luck" in sampling the wrong subjects.
   
   If my views above are on the right track, then this book would serve the research community no good by martyrizing Gosset and demonizing Fisher. There has been no cult all along. If we are justified in believing that some vested interests overemphasized statistical significance to divert our attention away from the more important issues, then we should encourage people (authors and readers alike) to focus on those more important issues instead of treating statistical significance as if it were irrelevant. For a more serious and more informative discussion on this topics, I would recommend Chow's Statistical Significance: Rationale, Validity and Utility (Introducing Statistical Methods) . His first chapter explains the key issues in 12 pages with more varieties of arguments and more intellectually stimulating details than what Ziliak and McClosky attempted in 251 pages.
   
   I give 3 stars for this book's good intent but average quality, and, on top of that, took 1 star off for its mean-spirited rhetorics.



4. The message that I took from this book was two fold:
   1) Queries into existence (does a relationship exist?) are metaphysical, not scientific.
   2) Looking to the statistical significance of a relationship must be coupled with a look at the magnitude of the effect/correlation and the "big picture" of the problem being investigated.
   
   The authors set the scene well, write lucidly, and provide illuminating examples. I would think this book would be "must-read" for anyone preparing to engage the social-science literature.



5. This is a rather poorly written and overwrought summary of how statistical significance testing is misused in many areas of science. The authors are economists, not statisticians, and have a idiosyncratic understanding of the subject matter, regularly confusing different aspects of hypothesis testing and selectively quoting Fisher.
   
   If you are interested in the subject, Jacob Cohen, Gerald Dallal, and others have written clearer summaries. Wikipedia also has some useful pointers. Keep Nasim Taleeb's (the Black Swan) comments about economists and statistics in mind.

5 comments about A Guide to Econometrics.

1. Not many scientists can write but Peter Kennedy is NOT one of them. He presents the mathematical and statistical information in clear, concise language. A wonderful AND informative read!!



2. I highly recommend this book as a source of intuition for econometrics. As a Ph.D. student working on my own research I find this book very helpful when I want a quick and easy explanation. This book is also good for clarifying some basic concepts that never got adequate explanations in my econometrics courses. I only wish that this book had a little more coverage on limited dependent variable models.



3. This is a great econometrics book. I wish that I had found this book earlier in my graduate career, and now I find myself going to it all the time. It covers all the important concepts and is very clearly written. The best thing about the book is that it teaches how to use econometrics not just what it is. It makes that very important jump of teaching students how to apply these tools properly. I cannot recommend this book enough!



4. This is a great book. But buy it for the right reason. All by itself, not as useful as a lot of the reviews suggests.
   
   This has to be used together with Greene's <>. It suppliment a lot of the formulae with ideas and reasons. But it is light on formula by itself, and you can not use it as a reference. This is a explain book, tells you why we should do it this way, what to caution/watch for, what is the logic behind that.
   
   So buy it with greene's book. Learn the math in greene's book, keep greene's book on the shelf as regular reference book. But read this book to understand ideas, and sort out complexicities.
   
   Overall, still a great buy.



5. This book must be mandatory before and during any econometrics class. In intuitive terms and examples, with minimal notation and math, manages to deliver a working knowledge, a basic understanding that can supplement and aid in the use of the usual undergraduate and graduate econometrics books like Green and others.
   
   What is exactly opposite of these other books is that you can really enjoy reading this book while drinking your coffee, or lying on the sand enjoying the beach. I recommend this book in my syllabus to all my students in all graduate and undergraduate courses I teach.

5 comments about Freakonomics [Revised and Expanded]: A Rogue Economist Explores the Hidden Side of Everything.

1. I purchased this book as a gift for my fiance. He really enjoyed and appreciated it. Now it's my turn to read it!



2. Freakonomics addresses many diverse topics. The thing that ties the book together is the interesting kind of questions it examines and the unique blend of intuition, logic, and statistical analysis it uses to answer these questions. This book is enjoyable to read and describes many rather nerdy concepts in easy-to-understand language.
   
   Of the twenty or so topics the book addresses, four were especially interesting to me: rigging of Sumo wrestling matches, cheating by public school teachers on their students' proficiency exams, the drop in U.S. crime rates in the 1990s, and family attributes that correspond to children's educational success. The first two analyses identified statistical anomalies in patterns of wrestling match outcomes and test answers that demonstrated probable corruption in the world of Sumo wrestling and cheating in the Chicago public school system. The third and fourth analyses were the most impressive in the book. Each considered about a dozen variables that might be related (causally or otherwise) to crime rates or educational success and used regression analyses to determine which ones were in fact related.
   
   The analysis of the drop in crime rates in the 1990s is probably Steven Levitt's best known and most controversial work. His analysis shows that four factors were likely responsible for the decrease in crime: harsher prison terms, increased hiring of police, the crash of the crack cocaine market, and the U.S. Supreme Court's 1973 Roe v. Wade abortion decision. Levitt concludes that the first three factors were responsible for approximately 33%, 10%, and 15%, respectively, of the decrease in crime. He does not attach a specific weight to the Roe v. Wade decision, but presumably it accounts for all or most of the remaining 42%. The factors that did not contribute to the decrease in crime included tougher gun laws, increased use of capital punishment, and innovative policing strategies (such as the oft-touted New York City strategy of going after petty crimes to signal that no crimes would be tolerated).
   
   The theory behind the Roe v. Wade contribution is that widespread abortion removed from the population many of the very people (unwanted children) most likely to commit crime when they became teenagers and young adults. This theory has disturbed many people of all political persuasions, apparently because it provides ammunition to both sides of the abortion debate. In addition, some economists have argued that Levitt's analysis has technical errors significant enough to invalidate his conclusions. I don't know whether Levitt has effectively responded to these economists. As for the political controversy, Levitt has basically stayed above the fray, stating that, "The numbers we're talking about, in terms of crime, are absolutely trivial when you compare it to the broader debate on abortion. ... So, my own view...is that our study shouldn't change anybody's opinion about whether abortion should be legal and easily available or not. It's really a study about crime, not abortion." (Source: Wikipedia.)
   
   The analysis of family attributes that correspond to children's educational success concludes that six factors are positively correlated with success: the parents are highly educated, the parents have high socioeconomic status, the mother was at least 30 years old at the time of her first child's birth, the parents speak English at home, the parents are involved in the PTA, and the child has many books at home. Surprisingly, several factors that conventional wisdom suggests are important to success seem to have little or no relationship to actual outcomes, including whether the child's family is intact, whether the child's mother works before the child enters kindergarten, whether the child's parents frequently read to him/her, and whether the child watches a lot of TV.
   
   For me, the biggest surprise in the list of non-correlated variables was the apparent unimportance of intact families. A great deal of social science research contradicts this finding. For example, boys who grow up in homes without fathers are more likely to drop out of school. They also have higher rates of substance abuse, arrest, and incarceration. It would be interesting to hear Levitt's critique of this research. I also wonder if there is a problem with Levitt's data. The Early Childhood Longitudinal Study from which he draws his data tracks educational outcomes only through fifth grade. Perhaps the impacts that broken families have on children are more acute after fifth grade.
   
   Despite these fascinating analyses, I had a few disappointments with the book. For starters, some of the topics that it addressed seemed so unremarkable that I wondered how they made it into the book. Two of the conclusions presented were ones I had reached on my own, before reading the book. One was the conclusion that a real estate agent's incentives are not aligned with a house seller's incentives. (An agent maximizes his/her income by selling as many houses as possible, not by squeezing the last dollar out of a particular sale.) The other was the conclusion that the internet caused term life insurance premiums to fall (not to mention the prices of countless other goods and services).
   
   I also noticed a couple of instances of sloppy thinking. For example, the analysis of family attributes that correspond to children's educational success concluded with some wild generalizations, including: "it isn't so much a matter of what you do as a parent; it's who you are;" and "[child-rearing] technique looks to be highly overrated." The authors admit that they are over generalizing "a bit," but it's more like a giant leap. First, as the authors acknowledge, bad parenting matters a great deal, hence the theory that Roe v. Wade decreased crime rates. Second, parenting is about much more than successful educational outcomes; it is also about physical, emotional, behavioral, social, moral, spiritual, and character development. Third, the set of things that constitutes "what you do" and the set of things that constitutes "who you are" are not discrete sets; they intersect in a big way.
   
   Finally, I grew tired of the subtext that seems to run through this book, namely, that economics is some sort of master science that can explain and predict just about anything. The basic tools of the book -- curiosity, intuition, logic, thinking outside the box, and statistics -- are not peculiar to economics. Also, the book frequently ignores the uncertainties of statistical analysis. For example, there is no discussion of the level of significance of the many correlations that are described. Ironically, the book repeatedly issues warnings about trusting experts, for example, "The typical parenting expert, like experts in other fields, is prone to sound exceedingly sure of himself."
   
   But overall I enjoyed this book and found the topics it examined interesting.
   
   (Note: This review is based on the 2005 edition of Freakonomics.)



3. Everybody knows that economics is about measurement and money and things numerical; that's why most of us find it so damned dull.
   
   But as approached by offbeat economist and Freakonomics co-author Steven D. Levitt, economics is also "the study of incentives": what it takes to get us to do a certain thing--or to not do it, as the case may be. Which makes it human, and therefore fascinating.
   
   This is what I love about this delightful book by Levitt and journalist Stephen J. Dubner: that it comes at things sideways or upside-down or head-on, but never the usual way. I'm still not sold on some of the more radical hypotheses Leavitt coaxes from the data (the link between abortion and falling crime rates being the most widely reviled and quoted), but I'm 100% there on the importance of throwing the numbers against conventional wisdom to see what sticks. The numbers may not always tell the exact truth, but neither do they lie, making them extraordinarily useful in the exploding of myths.
   
   Levitt and Dubner tell fascinating stories about how to combat crappy teaching, bring down the Ku Klux Klan and what happens when you call your kids "Winner" and "Loser" (answer: not necessarily what you'd think on any count). But really, they've written a book celebrating the heart of truth: asking questions, and hacks to stay open to the real answers.
   
   As an interesting side note, the prospect of reading something that seemed like it would rock my world long and hard was too enticing to wait for a library copy to become available, but not enticing enough to get me to part with $26 of my hard-earned money. My break point? A 25¢/day rental from the Beverly Hills Public Library, and pushing the rest of my reading to the bottom of the pile. Some might call that cheap, but I'm betting Levitt would come at it sideways and say that I was already giving up time I'd committed to other reading to explore this book, and therefore it was of great value to me.
   
   And you know what? He'd be right.



4. No theme, no logic thread, but some truly original takes on how the world works, from the standpoint of a "rogue economist." Some topics, like how Roe v. Wade is the strongest explanatory variable for America's decrease in crime, just keep coming up, but still, there are lots of common-sense-defying revelations that keep it interesting. For instance, did you know that time spent reading to children does not correlate to improved test scores, but that the number of books in the house does? Did you know that crack-dealing inner-city gangs are run much like corporate America -- except they offer a slightly higher risk of fatality? Whether or not you agree with the authors' conclusions, these, and a slew more of such revelations, make for an entertaining and thought-provoking read indeed.



5. Economics and economists have the reputation of being dry, dusty old ideas and men with no more life to them than an Egyptian mummy on display in a museum. But Freakonomics brings fresh life to the subject and at the same time encourages its readers to think outside the box or break the mold of conventional thinking.
   
   A series of six hilarious chapters challenge our assumptions on everything from drug dealers to school teachers to parenting. Using statistics and new interpretations, the authors Steven Levitt and Stephen J. Dubner help us view our world with altered eyes, recognizing that truisms and cliches are not necessarily reflections of reality. In today's world of crashing stock markets and neighborhoods full of foreclosed houses, its good to take a fresh look at economics and the long time assumptions we have all relied upon far too long.