Wednesday, April 23, 2014

Research Heroes: Richard Thaler

I wish someone had told me at the beginning of my career how to go about combining economics and psychology. There was no road map. Mostly trial and error with lots of errors.
I most admire academically… My greatest inspiration came from Kahneman and Tversky, my mentors who became my friends and collaborators. Danny is still a source of inspiration, going full speed at 78. They were a fantastic team because of their complementary skills, but they were both perfectionists in their different ways. I owe my career to them directly, but in some ways so does the entire field.
The best research project I have worked on during my career… This is like asking a parent to name his favorite child. Not fair. So I will fudge and name more than one. I think mental accounting is probably my best “idea”. Save More Tomorrow is my most important practical application. The book Nudge has reached the widest audience and had the most impact. But truth be told my favorite single paper is the one with Cade Massey on the NFL draft called the “Loser’s Curse”. The great thing about being an academic is that you can write a paper on anything. Certainly the paper that was the most fun to work on (or not work on) was the one with Eldar Shafir called “Invest Now, Drink Later, Spend Never”. Eldar and I didn’t work on it for a solid week in Venice one year. It is about the mental accounting of wine consumption. I still devote a lot of time to that problem!
The worst research project I have worked on during my career… There is nothing in print that I would want to take back. I have abandoned lots of projects. I believe in ignoring sunk costs. As I tell my MBA students: “Ignore sunk costs. Assume everyone else doesn’t.”
The most amazing or memorable experience when I was doing research… I gave a talk on the idea for Save More Tomorrow to a large (>500) group of 401(k) plan administrators in 1996 or so, thinking that at least one one of them would think enough of the idea to try it. Then nothing happened for several years. Very frustrating. Then out of the blue Shlomo Benartzi told me that someone he knew had tried it without even telling us, and the results were fantastic. That was exciting because once we could show people that the idea worked, it was (relatively) easy to get others to try it. Now it is used by millions of people, but we had to get the first employer to try it or we would still be wondering if it would really work.
The one story I always wanted to tell but never had a chance… No such thing. But I am putting all those stories into a book I am working on, so stay tuned. The working subtitle is “The Stories of Behavioral Economics”.
A research project I wish I had done… My phd thesis was on the value of saving a life. The idea was to estimate how much you had to pay people to get them to accept a small increase in risk. So, I did an econometrics exercise regressing wages on occupational mortality rates. But the really clean study to do, as suggested by my buddy Richard Zeckhauser, would be to get people to play Russian Roulette, with a machine gun with many, many chambers (say 10,000). Then tell people there are 5 bullets in the gun, how much would you pay to remove one, or accept to add one. For some reason, no human subject committee has ever been willing to approve this project. Can’t imagine why! (Before I get into trouble, this was intended as a joke.)
If I wasn’t doing this, I would be… Less happy. I feel lucky to have found a way to make a living that is so much fun to do. Who knows what else, but I did think about going to law school instead of economics graduate school. I don’t think I would have been a great lawyer though. I suffer from a diplomacy deficiency.
The biggest challenge for our field in the next 10 years is… I see two. First, JDMers need to learn to get out of the lab some of the time (and journal editors need to encourage such risky activity by applying appropriately different standards to field experiments). The stuff we study is too important and useful for it to be limited to the lab.
Second, I fear that the science-lab model in which increasing numbers of grad students are added to shrinkingly important papers in order to supply graduate students with enough publications to go on the job market. I think this trend stifles creativity and does not encourage students to do enough thinking on their own. More generally, I think psychologists are just publishing too many small papers. Look at the number of papers Kahneman and Tversky wrote that created and defined the field we now call judgment and decision making. The judgment stuff was really 3 papers plus the Science recapitulation. Then came prospect theory. Four blockbusters that led to a Nobel Prize. Not enough for tenure these days! Amos had a line about people that he felt wrote too many papers: “he publishes his waste basket”. I don’t think he would approve of the current state of affairs.
My advice for young researchers at the start of their career is… Work on your own ideas, not your advisor’s ideas (or at least in addition to her ideas). And spend more time thinking and less time reading. Too much reading leads people to think of small variations on existing studies. Admittedly my strategy of writing the paper first and only then reading the literature (or, more likely, letting the referees tell me what they think I should have read) is an extreme one, but it is better than trying to read everything. Try writing the first paper on some topic, not the tenth, and never the 50th.

Friday, April 11, 2014

David Romer's Rules for Making It Through Graduate School and Finishing Your Dissertation

"Out in Five"




  • Don't clutter up your life with other activities; just write.
  • Don't carry out a thorough and comprehensive search of the literature; just write.
  • Don't attempt to make sure that every page you write shows the full extent of your professional skills; just write.
  • Don't write a well-organized, well-integrated, unified dissertation; just write.
  • Don't think profound thoughts that shake the intellectual foundations of the discipline; just write.
  • If you don't have a paper started by the spring of your third year, be alarmed.
  • If you don't have a paper largely drafted by the fall of your fourth year, panic.
  • Have three new ideas a week while you are getting started.
  • Don't try to game the profession, work on what interests you.
  • Good papers in economics have three characteristics:
    • viewpoint.
    • lever.
    • result.

Sunday, April 6, 2014

Panel Data Analysis -- Random Effects vs. Fixed Effects

y_it=beta x_it + a_i + u_it

The unobserved factors affect the dependent variable consist of two types: a_i (constant over time), and u_it (varying over time). a_i is called an unobserved effect or a fixed effect. u_it is called the idiosyncratic error or time varying error.

Fixed effects estimation uses a transformation (time-demeaned data) to remove the unobserved effect a_i. Fixed-effects transformation is also called the within transformation. A pooled OLS estimator uses the time variation in y and x within each cross-sectional observation. It is based on the time-demeaned variables and is called the fixed effects estimator or the within estimator.

In the fixed effects model, any explanatory variable that is constant over time for all i gets swept away by the fixed effect transformation, therefore, we cannot include variables such as gender or a city's distance from a river. The fixed effects estimation is adequate if we want to draw inferences only about the examined individuals.

When we assume that the unobserved effect a_i in the above model is uncorrelated with each explanatory variable in all periods Cov(x_it,a_i)=0 , we have a random effects model. This model is adequate, if we want to draw inferences about the whole population, not only the examined sample.

The fixed effects estimator subtracts the time averages from the corresponding variable. The random effects transformation subtracts a fraction of that time average, where the fraction depends on sigma_u^2, sigma_a^2, and the number of time periods, T.

In practice, it is usually informative to compute the pooled OLS estimates. Comparing the three sets of estimates can help us determine the nature of the biases caused by leaving the unobserved effect a_i, entirely in the error term (as does pooled OLS) or partially in the error term (as does the RE transformation.) Remember, however, the pooled OLS standard errors and test statistics are generally invalid: they ignore the often substantial serial correlation in the composite errors, v_it=a_i + u_it.

One can used Hausman test. A failure to reject means either that the RE and FE estimates are sufficiently close so that it does not mater which is used, or the sample variation is so large in the FE estimates that one cannot conclude practically significant differences are statistically significant.

Using FE is mechanically the same as allowing a different intercept for each cross-sectional unit. FE is almost always much more convincing than RE for policy analysis using aggregated data.


Panel data analysis can also be used to analyze clustered data. Depending on the nature of the clustered data, FE or RE can be used.

xttobit -- Linear Regression Model with Panel-level Random Effects for censored data

xttobit fits random-effects tobit models. There is no command for a parametric conditional fixedeffects model, as there does not exist a sufficient statistic allowing the fixed effects to be conditioned out of the likelihood.


Panel data analysis has three more-or-less independent approches:
1. Independently pooled panels: there are no unique attributes of individuals within the measurement set, and no universal effects across time
2. Random effects model: there are unique, time constant attributes of individuals that are the results of random variation and do not correlate with the individual regressors. This model is adequate if we want to draw inferences about the whole population, not only the examined sample
3. Fixed effects model (or first differenced models): there are unique attributes of individuals that are not the results of random variation and that do not vary across time. Adequate if we want to draw inferences only about the examined individuals. Also known as "least squares dummy variable model"


y_{it}=x_{it}beta + u_i + e_it

u_i is the random effects
e_it is the individual effect

. xi: xttobit Contribution Period Belief AmountInvested Cutoff RandomDraw realized econm
> ajor econyear if Treatment==4, ll(0) ul(20)


Random-effects tobit regression                 Number of obs      =       320
Group variable: Subject                         Number of groups   =        16

Random effects u_i ~ Gaussian                   Obs per group: min =        20
                                                               avg =      20.0
                                                               max =        20

                                                Wald chi2(8)       =    372.07
Log likelihood  = -643.86478                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
Contribution |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      Period |  -.3714078   .0859276    -4.32   0.000    -.5398229   -.2029927
      Belief |   .8871966   .1082666     8.19   0.000      .674998    1.099395
AmountInve~d |   .4972489   1.099413     0.45   0.651    -1.657561    2.652059
      Cutoff |  -.1031474   .0144602    -7.13   0.000    -.1314888   -.0748059
  RandomDraw |   .0298226   .0128297     2.32   0.020     .0046769    .0549683
    realized |    1.19617   1.115385     1.07   0.284    -.9899439    3.382284
   econmajor |  -.1037135   4.025793    -0.03   0.979    -7.994123    7.786696
    econyear |   -1.11204   1.565532    -0.71   0.478    -4.180426    1.956345
       _cons |   4.846538   4.027966     1.20   0.229    -3.048129    12.74121
-------------+----------------------------------------------------------------
    /sigma_u |   4.153189   .9255023     4.49   0.000     2.339238     5.96714
    /sigma_e |     4.6622   .2652641    17.58   0.000     4.142292    5.182109
-------------+----------------------------------------------------------------
         rho |   .4424506   .1130577                      .2400784    .6614899
------------------------------------------------------------------------------

  Observation summary:        94  left-censored observations
                             185     uncensored observations

                              41 right-censored observations

The output includes the overall and panel-level variance components (labeled sigma e and sigma u, respectively) together with rho, which is the percent contribution to the total variance of the panel-level variance component.

When rho is zero, the panel-level variance component is unimportant, and the panel estimator is not different from the pooled estimator. A likelihood-ratio test of this is included at the bottom of the output. This test formally compares the pooled estimator (tobit) with the panel estimator.

Saturday, April 5, 2014

The danger of high blood sugur

Diabetes, is a group of metabolic diseases in which a person has high blood sugar.

Diabetes is due to either the pancreas not produce enough insulin, or because cells of the body do not respond properly to the insulin that is produced. There are three main types of diabetes mellitus:

1. Type 1 DM results from the body's failure to produce insulin.

2. Type 2 DM results from insulin resistance, a condition in which cells fail to use insulin properly, sometimes also with an absolute insulin deficiency.

Consumption of sugar-sweetened drinks in excess is associated with an increased risk. The type of fats in the diet are also important, with saturated fats and trans fatty acids increasing the risk, and polyunsaturated and monounsaturated fat decreasing the risk. Eating lots of white rice appears to also play a role in increasing risk. A lack of exercise is believed to cause 7% of cases.

Why are high blood sugar levels bad for you? Glucose is precious fuel for all the cells in your body -- when it's present at normal levels. But persistently high sugar levels behave like a slow-acting poison.

High sugar levels slowly erode the ability of cells in the pancreas to make insulin. The pancreas overcompensates, though, and insulin levels remain overly high. Gradually, the pancreas is permanently damaged.

All the excess sugar is modified in the blood. The elevated sugar in the blood causes changes that lead to atherosclerosis, a hardening of the blood vessels.

Because high sugar levels are everywhere, the body can be damaged anywhere. Damage to blood vessels, in particular, means no area is safe from too much sugar. High sugar levels and damaged blood vessels cause the multitude of complications that can come with diabetes

3. Gestational diabetes, is the third main form and occurs when pregnant women without a previous diagnosis of diabetes develop a high blood glucose level.

Artificial sweetener increases the likelihood of gaining weight

Experiments have found that sweet taste, regardless of its caloric content, enhances your appetite. Aspartame has been found to have the most pronounced effect, but the same applies for other artificial sweeteners, such as acesulfame potassium and saccharin.

The reason why glucose or sucrose (table sugar) tends to lead to lower food consumption compared to non-caloric artificial sweeteners is because the calories contained in natural sweeteners trigger biological responses to keep your overall energy consumption constant. This was again evidenced in a study

In essence, real sugar allows your body to accurately determine that it has received enough calories, thereby activating satiety signaling. Without the calories, your appetite is activated by the sweet taste, but as your body keeps waiting for the calories to come, sensations of hunger remain.

http://articles.mercola.com/sites/articles/archive/2012/12/04/saccharin-aspartame-dangers.aspx#!

Friday, April 4, 2014

The Key for a Good Experimental Paper

Have a strong alternative hypothesis. Write about this alternative hypothesis in the introduction. An experimental design is interesting only when multiple outcomes can happen. Get the readers interested in the multiple outcomes.

Irwin–Hall distribution

In probability and statistics, the Irwin–Hall distribution, named after Joseph Oscar Irwin and Philip Hall, is probability distribution for a random variable defined as sum of a number of independent random variables, each having a uniform distribution.[1] For this reason it is also known as the uniform sum distribution.