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The insheet command works equally well on files which use tabs as separators. Stata examines the file and determines whether commas or tabs are being used as separators and reads the file appropriately. Now that the file has been read into Stata, you can save it. Dec 25, 2013 Project files can be listed in various ways, backed up, and shared with others. Project can also clean up the project directory by moving any file not linked to the project to an archive directory. A toy project that runs some examples from the version 12 Stata reference manuals is included in the ancillary files below.
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Given the high number of requests, my colleagues and I have decided to provide a brief tutorial on how to get started with xsmle. First of all, you have to install the command by typing net install xsmle, all from(It is fundamental to use the option all because when you install a user-written package without using this option, the ancillary files (in this case 'product.dta' and 'usaww.spmat') won't be downloaded, while with the all option all the ancillary files will always (conditionally on the presence of an internet connection) be downloaded to 1) your current working directory OR 2) the directory you specified for ancillary files using the net set other command. Now, lets assume that you haven't changed your 'ancillary files' directory and that your Stata current working directory is the Desktop. Then, you should now find on your Desktop two new files: 'product.dta' and 'usaww.spmat'.
Lets assume that you haven't changed your 'ancillary files' directory and that your Stata current. I have a question regarding the ancillary files of xsmle.
These files contain informations on public capital productivity in 48 US states observed over 17 years, as well as the spatial weights matrix for the US states (Munnel, 1990). The examples reported in the xsmle help file make use of these files, also available in R through the Ecdat package. The first step is to load the 'product.dta' into memory by typing use product.dta, clear Then, you have to do the same for the spatial weights matrix contained in the 'usaww.spmat' file. Autocom cdp keygen 2017 and software. Being an spmat file, this can be done by typing spmat use W using 'usaww.spmat' At this stage, after computing the logarithm of the dependent and independent variables, we have all the ingredients to estimate a spatial panel data model. For instance, the syntax to estimate a Spatial AutoRegressive (SAR) model with random effects is xsmle lngsp lnpcap lnpc lnemp unemp, wmat(W) model(sar) Notice that the dataset 'product.dta' has been already declared to be a panel.
In general, you need to declare your dataset to be a panel dataset by using the xtset command. References Munnell AH (1990). “Why Has Productivity Growth Declined? Productivity and Public Investment.” New England Economic Review, 1990, 3–22. When I use the command xsmle lngsp lnpcap lnpc lnemp unemp, wmat(W) model(sar) with your product.dta file, I get the error message: This is version 12.1 of Stata. Factor variables are allowed starting from version 11 invalid syntax r(198); I have also tried it with my own data and get the error: initial values not feasible r(1400); I have run other panel data models in Stata using the same data without this error (eg negative binomial regression w random effects) I am not sure what to do about either of these error message. Could you please advise?
Belotti, I am really happy with your wonderful command xsmle. I would like to use unbalanced panel data since when you deleted some individuals you may cause a distortion in the model with this omission in the spatial model.
I know you mentioned to use mi prefix with your command but i still have somme problems. Here is the error message i got *: 3200 conformability error _xsmle_est(): - function returned error: - function returned error an error occurred when mi estimate executed xsmle on m=1 r(3200); Do you have any advice? Also, i would like to know which estimation method you use to estimate the different models.
Since i will use multiple imputation i would like to be sure how the estimation method of xsmle will interact with the imputation data. Thank you for your help and your time. Regards, Denis. Dear Federico, at first thanks a lot for your nice statapackage, it seems like a real powerhouse. Lister Petter Engine Serial Numbers. However, I have the same problem like biswajit mohanty. I standardized the weight matrix, updated the routine and use 'from(rho=0.1)' as done by Elhorst in his spatial two regime panel package.
However, I am not achieving any convergence. (He stops after 100 Iterations and uses backed up values after 3 Iterations). Any suggestions how to solve this problem?
Do you already have some experiences with the performance of huge panels? I have n=400 and 9 time periods and n=2000 and 9 time periods. Kind regards.
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Dear Thorsten, Large panels are not a problem, as long as you can wait for the matrix inversion. Something you can try is changing the optimization algorithm using the -technique()- option. I would try something like - technique(bfgs) - together with -difficult-. See -mazimize_options- for more details. What kind of model are you estimating? Wacom serial number. Rho=0.1 is already the starting value for some models (e.g. SAR and SDM with -dlag-) so you could try something different but I would check the -tecnique()- option first.
Dear Federico and Andrea, I wish to thank you for sharing the command xsmle. To me, this looks like an important step forward. Here I am writing to ask for help, after having carefully read (i) the help of the command, (ii) the comments and responses on this page, and (iii) the comments and responses on the page 'Spatial panel data models using Stata'. In fact, I am still stuck with a problem.
Ancillary Files Stata Help Center
Similarly to other users, the iteration does not proceed toward convergence when I run the following command on my dataset: -xsmle y x, wmat(W) model(sdm) dlag fe- I have tried a number of alternative specifications, as you suggested above. First, I have added the option -difficult-, but nothing changed. Then, I have tried to add also the option -technique(bfgs). But the outcome turned out being: [.] BFGS stepping has contracted, resetting BFGS Hessian Iteration 73: Log-likelihood = -25695.972 Iteration 74: Log-likelihood = -25695.972 (backed up) Iteration 75: Log-likelihood = -25695.972 (backed up) Iteration 76: Log-likelihood = -25695.972 (backed up) Iteration 77: Log-likelihood = -25695.972 (backed up) cannot compute an improvement -- flat region encountered r(430); Also, I have tried by reducing the tolerance range, using as an option -tolerance(1e-4). And again, convergence is not reached. In my case too, the panel is big, as N=691 and T=12.
![R open stata file R open stata file](https://www.thelancet.com/cms/attachment/72acc3cc-f093-4381-8a17-53954cda7cb1/gr4_lrg.jpg)
Again, thank you for your work and help. Dear Andrea, first thanks a lot for you help and my apologies that I needed some time to reply. I was working around a bit and when I am estimating a static SDM model with the leeyu transformation as well as time and individual FE, I get the same coeffcients and t-values like the Elhorst routine. So this is very good, thanks a lot! But my log-likelihood (xsmle: -22033; Elhorst: -24000) and my R² (xsmle: within 0.33; Elhorst: 0.9) differ strongly.
Ancillary Files Stata Help Download
Do you have any explanation, why this might occur? Thanks a lot in advance again! Best, Thorsten. Man Vs Wild 720p download free. full Episode.
Dear Federico, I have followed your instruction to impute the missing values in my dataset. However I experienced some problems when I wish to run the 'xsmle' command with 'mi estimate'. Mi estimate: xsmle price wind hydro thermal geothermal, wmat(W) model(sdm) fe type(ind) panelsubmatrix(): 3301 subscript invalid _xsmle_est(): - function returned error: - function returned error an error occurred when mi estimate executed xsmle on m=1 r(3301); Can you please give me some advice? Selena Sheng.