Advanced and Specialized Statistics with Stata

Course details

Stata is agile, easy to use, and fast, with the ability to load and process up to 120,000 variables and over 20 billion observations. In this course, take a deeper dive into the popular statistics software. Instructor Franz Buscha explores advanced and specialized topics in Stata, from panel data modeling to interaction effects in regression models. Franz demonstrates several sophisticated data management functions and visualization techniques to complement the basic Stata operations that you may have already mastered. Plus learn about Monte Carlo simulations, count data analysis, survival analysis, and more.

Learning objectives
Explain how to loop over the variables in a data set using given parameters.
Identify the command used to examine all the coefficients returned by a regression output.
Summarize the effect of using a numerical multiplier to change the marker size in a scatterplot.
Recall how to properly express a standard normal probability density function.
Recognize what information the regression offers in a given situation.
Define “continuous polynomial interaction.”
Explain the importance of using the reshape command for wide-form data when setting up panel data.
Identify the variable that will have the largest standard deviation after running summary statistics for a data set of panel data.
Name the linear panel estimator that assumes regression may be correlated to error terms.
Explain the purpose of the Hausman test.

Concepts

0. Introduction

01 - Specialized statistics with Stata
02 - What you should know

1. More on Data Management

03 - Formatting the display of variables
04 - Date and time variables
05 - Repeating commands by looping over variables
06 - Repeating commands by looping over numbers
07 - Repeating commands by looping within loops
08 - Accessing results saved from Stata commands
09 - Challenge - More on data management
10 - Solution - More on data management

2. More on Visualization Techniques

11 - Changing the look of markers
12 - Changing graph colors
13 - Graphing by groups
14 - Controlling legends
15 - Adding text and textboxes
16 - Sizing graphs
17 - Combining graphs
18 - How to use jitter
19 - How to draw custom functions
20 - Challenge - More on visualization techniques
21 - Solution - More on visualization techniques

3. Interaction Effects in Regression Models

22 - What is an interaction effect
23 - How to use margins and marginsplot
24 - Continuous polynomial interactions
25 - Continuous by continuous interactions
26 - Categorical by categorical interactions
27 - Categorical by linear interactions
28 - Challenge - Interaction effects
29 - Solution - Interaction effects

4. Panel Data Modeling

30 - Setting up panel data
31 - Setting up panel data demo
32 - Panel data descriptives
33 - Panel data descriptives demo
34 - Panel data dynamics
35 - Panel data dynamics demo
36 - Linear panel estimators
37 - Linear panel estimators demo
38 - Random or fixed effects
39 - The Hausman test demo
40 - Nonlinear panel data estimators
41 - Nonlinear panel data estimators demo
42 - Challenge - Panel data modeling
43 - Solution - Panel data modeling

5. Random Numbers and Simulation

44 - Drawing pseudorandom numbers
45 - Data generating process (DGP)
46 - Violating estimator assumptions
47 - Monte Carlo simulation
48 - Challenge - Simulation
49 - Solution - Simulation

6. Count Modeling

50 - Features of count data
51 - Poisson model
52 - Negative binomial models
53 - Truncated models
54 - Zero-inflated models
55 - Challenge - Count modeling
56 - Solution - Count modeling

7. Survival Analysis

57 - What is survival data
58 - Setting up survival data
59 - Summary statistics
60 - Nonparametric analysis
61 - Cox proportional hazards model
62 - Diagnostics for Cox models
63 - Parametric proportional hazards models
64 - Challenge - Survival analysis
65 - Solution - Survival analysis

Conclusion

66 - Next steps