Archive for 2017

Estimation under omitted confounders, endogeneity, omitted variable bias, and related problems

Initial thoughts

Estimating causal relationships from data is one of the fundamental endeavors of researchers, but causality is elusive. In the presence of omitted confounders, endogeneity, omitted variables, or a misspecified model, estimates of predicted values and effects of interest are inconsistent; causality is obscured.

A controlled experiment to estimate causal relations is an alternative. Yet conducting a controlled experiment may be infeasible. Policy makers cannot randomize taxation, for example. In the absence of experimental data, an option is to use instrumental variables or a control function approach.

Stata has many built-in estimators to implement these potential solutions and tools to construct estimators for situations that are not covered by built-in estimators. Below I illustrate both possibilities for a linear model and, in a later post, will talk about nonlinear models. Read more…

Creating Excel tables with putexcel, part 2: Macro, picture, matrix, and formula expressions

In my last post, I showed how to use putexcel to write simple expressions to Microsoft Excel and format the resulting text and cells. Today, I want to show you how to write more complex expressions such as macros, graphs, and matrices. I will even show you how to write formulas to Excel to create calculated cells. These are important steps toward our goal of automating the creation of reports in Excel.

Before we begin the examples, Read more…

Categories: Programming Tags: ,

Creating Excel tables with putexcel, part 1: Introduction and formatting

For a long time, I have wanted to type a Stata command like this,

. ExcelTable race, cont(age height weight) cat(sex diabetes)
The Excel table table.xlsx was created successfully

and get an Excel table that looks like this:


So I wrote a program called ExcelTable for my own use Read more…

Categories: Programming Tags: ,