## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #large-data #instrumental-variables #hausman Interpretation of the Hausman test (overidentification in relation to IV&#39…

### Bounty: 50

I am using survey data with a huge amount of observations, such as the World Value Surveys. Large sample sizes are obviously very nice, but I have have encountered some downsides as well.

To give an example, in almost every econometric model I specify, about 90% of the variables is highly significant. So I will have to decide whether, in addition to an estimate being statistically significant, it is also economically significant, which is not always an easy thing to do.

The biggest issue is however, that when resorting to Instrumental Variables, the Hausman test for over identification is always very, very, very significant. See to this extent THIS POST.

How do I deal with with this consequence of large sample sizes?

The only thing I can think of is to reduce the sample size. This however seems a very arbitrary way to get the test statistic down.

Get this bounty!!!

## #StackBounty: #r #stata #instrumental-variables #endogeneity #hausman What are the differences between tests for overidentification in …

### Bounty: 50

I am using 2SLS for my research and I want to test for overidentification. I started out with the Hausman test of which I have a reasonable grasp.

The problem I have is that from the Hausman and the Sargan Test I am getting very different results.

The Sargan test is done by `ivmodel` from `library(ivmodel)`. I copied the Hausman test from “Using R for Introductory Econometrics” page 226, by Florian Heiss.

``````[1] "############################################################"
[1] "***Hausman Test for Overidentification***"
[1] "############################################################"
[1] "***R2***"
[1] 0.0031
[1] "***Number of observations (nobs)***"
[1] 8937
[1] "***nobs*R2***"
[1] 28
[1] "***p-value***"
[1] 0.00000015

Sargan Test Result:

Sargan Test Statistics=0.31, df=1, p-value is 0.6
``````

On top of this I am also using `ivtobit` from Stata, which provides a Wald test of exogeneity.

Lastly I read about a fourth which is the `Hansen J statistic`.

What is the difference between all of these tests?

Get this bounty!!!