# Search results

1. ### Entity fixed effects vs. time fixed effects - opposite results

I'm analyzing some panel data that show how the percentage of the workforce as well as median wages have changed over time (hryear4) across major industry groupings (prmjind1). The data look like this: Row │ hryear4 prmjind1 prcnt_minority median_wage wage_10 median_age │ Int64...
2. ### Absorbing features in panel data regression

Unfortunately, I don't know enough to answer your questions - I'm trying to learn this kind of econometric modelling and have very little knowledge/experience in this area :). In this case, my response variable and my predictor variables have all steadily increased over the 11-year period and...
3. ### Absorbing features in panel data regression

I'm using the Julia programming language to do some regression analysis on some panel data that I have. The package that I'm using I believe is modeled after Stata and I'm hoping someone can help me understand what the absorb feature does. I have some simple panel data that measure the median...
4. ### Linear probability model - Question about specification

That probably explains why I'm getting such terrible results in my attempts to model the same data that the presenter used. In this case, the source data indicate whether a respondent to a survey is an independent contractor vs. an employee, and only about 7% of all the respondents identified as...
5. ### Linear probability model - Question about specification

I saw a presentation at work a couple of days ago on a linear probability model and I jotted down the model specification so that I could later attempt to recreate it and I'm confused about the model specification. I do not have any experience with this type of model and I'm not a...
6. ### Probability that it rains today or tomorrow

Oh, wow! Thank you very much for the help!!
7. ### Probability that it rains today or tomorrow

I'm working through this nice online textbook on probability and statistics and I'm struggling to understand one of the examples: It seems to me that item #3 is incompatible with items #1 and #2, is it not? I thought through it like this: The set of all possible outcomes is (today_rain...
8. ### What kind of distribution to model new business success

Nice! This is perfect!
9. ### What kind of distribution to model new business success

BLS data show that the probability of a new business remaining in business after 1 year is 80%, after 2 years it's 70%, after 5 years it drops to 50% and the probability of still being around after 10 years is 30%. What kind of distribution can I use to model this? I've been searching online for...
10. ### Which distance metric should I use for county clustering?

I've thought about weighting them but it's not clear just yet how to do so. The other thing that I worry about with these variables is the possibility that they correlate with one another. Does that even matter in this context? For example, if average educational attainment correlates with...
11. ### Which distance metric should I use for county clustering?

I'm trying to cluster U.S. counties based on the following characteristics: Median wages Unemployment rate Average educational attainment Population Many clustering algorithms require the calculation of a distance matrix but I'm having trouble evaluating the pros and cons of the different...
12. ### How do I fit a distribution to these data?!?!

I actually have two different datasets - the second one is similar to this one, just with lower mean, median, etc. I want to be able to make some probability statements about the two processes that generated these data. For example, under process A, the probability of a measurement being >...
13. ### How do I fit a distribution to these data?!?!

I am trying to fit a distribution to some data that consist of measurements of dollar amounts. The range is basically 0 to 300,000 (this range encompasses more than 99% of all measurements), although there are measurements that exceed this. The summary stats for the data look like this: Summary...
14. ### Beta distribution - mean vs mode

As I understand it, the mean is the expected value over the long-run and I would have thought that the expected value would be where the likelihood is maximized (in the case of a unimodal distribution, at the mode....)
15. ### Beta distribution - mean vs mode

I'm new to probability distributions and I'm trying to understand something about the Beta distribution. Suppose I am practicing kicking field goals and I want to understand the probability of successful attempts. If I create a Beta distribution like this: Beta(20,10) Where 20 is the number of...