Wednesday, April 23, 2014
Research Heroes: Richard Thaler
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:
- A viewpoint.
- A lever.
- A result.
Sunday, April 6, 2014
Panel Data Analysis -- Random Effects vs. Fixed Effects
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
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#!