#StackBounty: #stata #clustered-standard-errors When and why we should do single cluster? A challenge

Bounty: 50

From reading this discussion, there is one sentence

Standard errors should be clustered at the city level, i.e. the level
of aggregation
at which the treatment occurs

In Dasgupta, 2019‘s paper, the aggregation level is country (laws have impact on all firms in a country).
I am quite new to cluster standard error but I saw the paper of Dasgupta, 2019, in some cases, he did cluster for the country (table 11), in some cases, he did the cluster for industry (table 8), in some cases, he did cluster for firm (table 12).

I am quite curious about this way of clustering. It is because from this discussion , it seems that the old "rules of them" is 30-40 groups. However, in Dasgupta’s paper case, he has 64 countries and hundreds of thousands of firms, so why he need to cluster for firms and countries?


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#StackBounty: #stata #clustered-standard-errors Why standard error being clustered at industry or firm level when the aggregation level…

Bounty: 50

From reading this discussion, there is one sentence

Standard errors should be clustered at the city level, i.e. the level
of aggregation
at which the treatment occurs

In Dasgupta, 2019‘s paper, the aggregation level is country (laws have impact on all firms in a country).
I am quite new to cluster standard error but I saw the paper of Dasgupta, 2019, in some cases, he did cluster for the country (table 11), in some cases, he did the cluster for industry (table 8), in some cases, he did cluster for firm (table 12).

Is there any suggestion for understanding cluster and double cluster for a novice then?


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#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!

#StackBounty: #r #generalized-linear-model #stata #quasi-likelihood How flexible is Stata's ivpois? Could I use it for a (quasi) bi…

Bounty: 50

According to this post on statalist, Stata’s ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.

The problem mentioned in the post is:

"I have a database with counts as dependent variable. This variable suffers from over-dispersion problem."

Wooldridge mentions:

"I would strongly recommend trying IVPOIS, too. Regrettably, this command name is a misnomer. It should be called something like IVEXPON, as it works for any exponential model with multiplicative error. It does not care whether the EEV is continuous or discrete, so it produces consistent estimators under much weaker assumptions."

My thought was, if it works for any exponential model, does that mean it also works for my model, which is technically a quasibinomial, but according to this fine post, that boils down to a binomial with robust standard errors?

Hence my question: How flexible is Stata’s ivpois? Could I use it for a (quasi) binomial distribution?

Background: How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second


Get this bounty!!!