Two-step estimation problems can be solved using the **gmm** command.

When a two-step estimator produces consistent point estimates but inconsistent standard errors, it is known as the two-step-estimation problem. For instance, inverse-probability weighted (IPW) estimators are a weighted average in which the weights are estimated in the first step. Two-step estimators use first-step estimates to estimate the parameters of interest in a second step. The two-step-estimation problem arises because the second step ignores the estimation error in the first step.

One solution is to convert the two-step estimator into a one-step estimator. My favorite way to do this conversion is to stack the equations solved by each of the two estimators and solve them jointly. This one-step approach produces consistent point estimates and consistent standard errors. There is no two-step problem because all the computations are performed jointly. Newey (1984) derives and justifies this approach. Read more…

You can install the Stata manuals on your iPad. Here’s how: install GoodReader and copy the manuals from your computer to your iPad. It takes a few minutes and will cost you about $7 to purchase the app. Read more…

**gsem** is a very flexible command that allows us to fit very sophisticated models. However, it is also useful in situations that involve simple models.

For example, when we want to compare parameters among two or more models, we usually use **suest**, which combines the estimation results under one parameter vector and creates a simultaneous covariance matrix of the robust type. This covariance estimate is described in the *Methods and formulas* of **[R] suest** as the robust variance from a “stacked model”. Actually, **gsem** can estimate these kinds of “stacked models”, even if the estimation samples are not the same and eventually overlap. By using the option **vce(robust)**, we can replicate the results from **suest** if the models are available for **gsem**. In addition, **gsem** allows us to combine results from some estimation commands that are not supported by **suest**, like models including random effects. Read more…

I was recently talking with my friend Rebecca about simulating multilevel data, and she asked me if I would show her some examples. It occurred to me that many of you might also like to see some examples, so I decided to post them to the Stata Blog. Read more…

As stated in the documentation for **jackknife**, an often forgotten utility for this command is the detection of overly influential observations.

Some commands, like **logit** or **stcox**, come with their own set of prediction tools to detect influential points. However, these kinds of predictions can be computed for virtually any regression command. In particular, we will see that the **dfbeta** statistics can be easily computed for any command that accepts the **jackknife** prefix. **dfbeta** statistics allow us to visualize how influential some observations are compared with the rest, concerning a specific parameter.

We will also compute Cook’s likelihood displacement, which is an overall measure of influence, and it can also be compared with a specific threshold. Read more…

### Introduction

Today I want to show you how to create animated graphics using Stata. It’s easier than you might expect and you can use animated graphics to illustrate concepts that would be challenging to illustrate with static graphs. In addition to Stata, you will need a video editing program but don’t be concerned if you don’t have one. At the 2012 UK Stata User Group Meeting Robert Grant demonstrated how to create animated graphics from within Stata using a free software program called FFmpeg. I will show you how I create my animated graphs using Camtasia and how Robert creates his using FFmpeg. Read more…

In a previous blog entry, I talked about the new Stata 13 command **putexcel** and how we could use **putexcel** with a Stata command’s stored results to create tables in an Excel file.

After the entry was posted, a few users pointed out two features they wanted added to **putexcel**:

- Retain a cell’s format after writing numeric data to it.
- Allow
**putexcel** to format a cell.

In Stata 13.1, we added the new option **keepcellformat** to **putexcel**. This option retains a cell’s format after writing numeric data to it. **keepcellformat** is useful for people who want to automate the updating of a report or paper. Read more…