Hi Ben, Generally, the model is not identified if there are more variables than observations, which means imputations will be poorly behaved. You could run this data through Amelia with a ridge prior (that is, setting the "empri" option to a relatively high number). This would help identify the model by shrinking the covariances toward zero. Another option would be to run a preliminary factor analysis on the listwise deleted data and find the subset of important variables (that is, those with high factor loadings) and then use those variables in the imputation. Last, you could eschew Amelia completely and implement a Bayesian analysis with relatively strong priors to identify the model. In this option, you could handle the factor analysis and the missing data together. Hope that helps. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://www.mattblackwell.org On Thursday, January 26, 2012 at 11:26 AM, Ben Highton wrote: Hi Dan, Thanks for your response. In this case, though, I don't think the solution will work because the I don't have repeated measurements on the same variables through time. The data is fundamentally cross-sectional, not time-series cross-sectional. Best, Ben On Wed, Jan 25, 2012 at 11:51 AM, Dan Matisoff <dmatisof(a)umail.iu.edu> wrote: You can longitudinalize the data... so for each year, you'll get 51 more observations (which will also help generate improved imputations) On Jan 25, 2012, at 2:30 PM, Ben Highton wrote: Hi fellow Amelia users, I have a dataset with 51 observations (one for each American state + the District of Columbia) and nearly 150 variables.  Is there a way to do imputations when variables>observations?  (One of my analysis goals is to factor analyze the variables to attempt and identify what I believe to be the 2-4 dimensions in the data.) Because I primarily use Stata, I am using AmeliaView.  An initial attempt to impute missing values produced the following error: "Amelia Error Code: 34  The number of observations in too low to estimate the number of parameters.  You can either remove some variables, reduce the order of the time polynomial, or increase the empirical prior." If anyone has advice for me, I'd appreciate it.  Thanks. Sincerely, Ben Highton -- Amelia mailing list served by HUIT [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia Amelia mailing list Amelia(a)lists.gking.harvard.edu https://lists.gking.harvard.edu/mailman/listinfo/amelia -- Amelia mailing list served by HUIT [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia Amelia mailing list Amelia(a)lists.gking.harvard.edu https://lists.gking.harvard.edu/mailman/listinfo/amelia -- Amelia mailing list served by HUIT [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia Amelia mailing list Amelia(a)lists.gking.harvard.edu https://lists.gking.harvard.edu/mailman/listinfo/amelia