Return to Answer

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. As you've identified, there's essentially two reasons that can happen.

1. Multicollinearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression -- the removed variables are completely or mostly determined by the remaining ones -- so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

2. More features than observations You speculate that this could be problem with this remark.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means to you (What is n? What is T? What is N?). But a regression with more columns than observations is likewise not identified by the data.

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. As you've identified, there's essentially two reasons that can happen.

1. Multicollinearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression -- the removed variables are completely or mostly determined by the remaining ones -- so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

2. More features than observations You speculate that this could be problem with this remark.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means to you (What are n, T, and N?). But a regression with more columns than observations is likewise not identified by the data.

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. As you've identified, there's essentially two reasons that can happen.

1. Multicollinearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression, so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

2. More features than observations You speculate that this could be problem with this remark.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means to you (What is n? What is T? What is N?). But a regression with more columns than observations is likewise not identified by the data.

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. As you've identified, there's essentially two reasons that can happen.

1. Multicollinearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression -- the removed variables are completely or mostly determined by the remaining ones -- so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

2. More features than observations You speculate that this could be problem with this remark.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means to you (What is n? What is T? What is N?). But a regression with more columns than observations is likewise not identified by the data.

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression, so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means, though, but a regression with more columns than observations is likewise not identified by the data.

Caveat: I haven't used the plm package.

It's impossible to recreate this error because we don't have access to your data. But if the matrix is computationally singular, then you have a problem of multicollonearity. As you've identified, there's essentially two reasons that can happen.

1. Multicollinearity. Even if the pairwise correlations are low, it's possible that more than two columns of IVs are highly collinear with another IV column. Another piece of evidence toward this conclusion is that excluding some variables removes the error: you're removing some of the collinear columns, so the estimators are uniquely identifiable given the available data. Removing highly collinear columns doesn't change the amount of information to the regression, so it's perfectly valid as a solution. It's purely a question which explanatory variables you are more interested in.

2. More features than observations You speculate that this could be problem with this remark.

Unbalanced panel / NAs? The data is unbalanced and there are NAs. Fixed effects output says: n=16, T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for the error?

I don't know what "n=16, T=18-40, N=455" means to you (What is n? What is T? What is N?). But a regression with more columns than observations is likewise not identified by the data.