Why use OLS when it is assumed there is heteroscedasticity?












2












$begingroup$


So I'm slowly going through the Stock and Watson book and I'm a bit confused on how to deal with the issue of homoscedacity/heteroscedacity. Specifically, it is mentioned that economic theory tells us that there's no reason for us to assume that errors will be homoscedastic, so their advice is that we assume heteroscedasticity and always use the heteroscedastic robust standard errors when performing our regression analysis. The way I'm being taught this material, in STATA for example, is that we just run the reg command, always sure to include r for robust standard error.



My question(s) is this: if our default position is to assume heteroscedacticity, then is it also correct that OLS is no longer the best unbiased linear estimator as one of the Gauss-Markov assumptions is violated? And if this is the case, is it also correct that GLS would be the BLUE estimator? Lastly, if both of these assumptions are correct, why would we not just run GLS regressions as our default and not OLS?



Thanks.






least-squares heteroscedasticity generalized-least-squares blue







share|cite|improve this question









$endgroup$








  • 3




    $begingroup$
    It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
    $endgroup$
    – whuber♦
    Nov 26 '18 at 15:42












  • $begingroup$
    Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
    $endgroup$
    – anguyen1210
    Nov 26 '18 at 15:49










  • $begingroup$
    GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
    $endgroup$
    – Matthew Gunn
    Nov 27 '18 at 5:50












  • $begingroup$
    @MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
    $endgroup$
    – Heteroskedastic Jim
    Nov 28 '18 at 1:02


















2












$begingroup$


So I'm slowly going through the Stock and Watson book and I'm a bit confused on how to deal with the issue of homoscedacity/heteroscedacity. Specifically, it is mentioned that economic theory tells us that there's no reason for us to assume that errors will be homoscedastic, so their advice is that we assume heteroscedasticity and always use the heteroscedastic robust standard errors when performing our regression analysis. The way I'm being taught this material, in STATA for example, is that we just run the reg command, always sure to include r for robust standard error.



My question(s) is this: if our default position is to assume heteroscedacticity, then is it also correct that OLS is no longer the best unbiased linear estimator as one of the Gauss-Markov assumptions is violated? And if this is the case, is it also correct that GLS would be the BLUE estimator? Lastly, if both of these assumptions are correct, why would we not just run GLS regressions as our default and not OLS?



Thanks.






least-squares heteroscedasticity generalized-least-squares blue







share|cite|improve this question









$endgroup$








  • 3




    $begingroup$
    It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
    $endgroup$
    – whuber♦
    Nov 26 '18 at 15:42












  • $begingroup$
    Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
    $endgroup$
    – anguyen1210
    Nov 26 '18 at 15:49










  • $begingroup$
    GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
    $endgroup$
    – Matthew Gunn
    Nov 27 '18 at 5:50












  • $begingroup$
    @MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
    $endgroup$
    – Heteroskedastic Jim
    Nov 28 '18 at 1:02
















2












2








2





$begingroup$


So I'm slowly going through the Stock and Watson book and I'm a bit confused on how to deal with the issue of homoscedacity/heteroscedacity. Specifically, it is mentioned that economic theory tells us that there's no reason for us to assume that errors will be homoscedastic, so their advice is that we assume heteroscedasticity and always use the heteroscedastic robust standard errors when performing our regression analysis. The way I'm being taught this material, in STATA for example, is that we just run the reg command, always sure to include r for robust standard error.



My question(s) is this: if our default position is to assume heteroscedacticity, then is it also correct that OLS is no longer the best unbiased linear estimator as one of the Gauss-Markov assumptions is violated? And if this is the case, is it also correct that GLS would be the BLUE estimator? Lastly, if both of these assumptions are correct, why would we not just run GLS regressions as our default and not OLS?



Thanks.






least-squares heteroscedasticity generalized-least-squares blue







share|cite|improve this question









$endgroup$




So I'm slowly going through the Stock and Watson book and I'm a bit confused on how to deal with the issue of homoscedacity/heteroscedacity. Specifically, it is mentioned that economic theory tells us that there's no reason for us to assume that errors will be homoscedastic, so their advice is that we assume heteroscedasticity and always use the heteroscedastic robust standard errors when performing our regression analysis. The way I'm being taught this material, in STATA for example, is that we just run the reg command, always sure to include r for robust standard error.



My question(s) is this: if our default position is to assume heteroscedacticity, then is it also correct that OLS is no longer the best unbiased linear estimator as one of the Gauss-Markov assumptions is violated? And if this is the case, is it also correct that GLS would be the BLUE estimator? Lastly, if both of these assumptions are correct, why would we not just run GLS regressions as our default and not OLS?



Thanks.






least-squares heteroscedasticity generalized-least-squares blue




least-squares heteroscedasticity generalized-least-squares blue






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked Nov 26 '18 at 15:35









anguyen1210anguyen1210

133




133








  • 3




    $begingroup$
    It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
    $endgroup$
    – whuber♦
    Nov 26 '18 at 15:42












  • $begingroup$
    Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
    $endgroup$
    – anguyen1210
    Nov 26 '18 at 15:49










  • $begingroup$
    GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
    $endgroup$
    – Matthew Gunn
    Nov 27 '18 at 5:50












  • $begingroup$
    @MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
    $endgroup$
    – Heteroskedastic Jim
    Nov 28 '18 at 1:02
















  • 3




    $begingroup$
    It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
    $endgroup$
    – whuber♦
    Nov 26 '18 at 15:42












  • $begingroup$
    Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
    $endgroup$
    – anguyen1210
    Nov 26 '18 at 15:49










  • $begingroup$
    GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
    $endgroup$
    – Matthew Gunn
    Nov 27 '18 at 5:50












  • $begingroup$
    @MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
    $endgroup$
    – Heteroskedastic Jim
    Nov 28 '18 at 1:02










3




3




$begingroup$
It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
$endgroup$
– whuber♦
Nov 26 '18 at 15:42






$begingroup$
It would be good to get some clarification about your meaning of "GLS." My understanding of GLS is that you have to provide specific information about the error variances. What do you have in mind doing in the general (and by far most common) case when there is no such information directly available?
$endgroup$
– whuber♦
Nov 26 '18 at 15:42














$begingroup$
Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
$endgroup$
– anguyen1210
Nov 26 '18 at 15:49




$begingroup$
Hi, I'm not sure what to clarify. I'm not very familiar with the Generalized Least Squares method, but in your comment, is that the answer? Namely, that even assuming that our errors are heteroscedastic, we do an OLS regression anyways, because to do a GLS we need information on the error terms that we don't have? Sorry, this is all quite new to me, and I'm sure I've not phrased the question well. Thanks to all for their comments.
$endgroup$
– anguyen1210
Nov 26 '18 at 15:49












$begingroup$
GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
$endgroup$
– Matthew Gunn
Nov 27 '18 at 5:50






$begingroup$
GLS as a method has more pedagogic value than practical. One almost never sees GLS used in papers because researchers typically don't know $operatorname{Cov}[ epsilon mid X] = Omega$. You could assume some structure on $Omega$ and estimate the rest, but such a procedure (i.e. FGLS) can have big problems with robustness! Use a poor $Omega$ and your estimates will be worse than OLS. To me, GLS is interesting mostly from the standpoint of developing a deeper understanding of linear algebra and OLS.
$endgroup$
– Matthew Gunn
Nov 27 '18 at 5:50














$begingroup$
@MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
$endgroup$
– Heteroskedastic Jim
Nov 28 '18 at 1:02






$begingroup$
@MatthewGunn but GLS with a compound symmetry correlation structure (assuming exchangeability of responses within cases) is identical to a random intercept multilevel model. So in this sense, an identical model is regularly used. Also, in the case where you are analyzing reasonably normal data from a randomized control trial with not small n, I see no reason not to use it since you can simultaneously model heterogeneous variances by the groups. I think the reason it is not common has a lot to do with researchers ignoring substantive questions about variances.
$endgroup$
– Heteroskedastic Jim
Nov 28 '18 at 1:02












2 Answers
2






active

oldest

votes


















5












$begingroup$

Because GLS is BLUE if you know the form of heteroskedasticity (and correlated errors). If you misspecify the form of heteroscedasticity, GLS estimates will lose their nice properties.



Under heteroscedasticity, OLS remains unbiased and consistent, but you lose efficiency.



So unless you're certain of the form of heteroscedasticity, it makes sense to stick with unbiased and consistent estimates from OLS. Then adjust inference for heteroskedasticity using robust standard errors which are valid asymptotically if you don't know the form of heteroscedasticity.



A hybrid approach is to do your best at specifying the form of heteroskedasticity but still apply robust standard errors for inference. See Resurrecting weighted least squares (PDF).



Modeling is all about tradeoffs and resources. If you are convinced there is nothing to be learned from modeling the form of heteroscedasticity, then specifying its form is a waste of time. I would argue that there is usually something to be learned in empirical applications. But tradition/convention nudges us away from studying heteroscedasticity, looking at the variances, since all they are is "error".






share|cite|improve this answer











$endgroup$





















    0












    $begingroup$

    OLS is still unbiased when the data are correlated (provided the mean model is true). The net effect of heteroscedasticity is that it offsets the errors, so that the 95% CI for the regression is, at times, too tight and at other times too wide. Even still, you can correct the standard errors by using the sandwich or heteroscedasticity consistent standard error (HC) estimator. Technically, this is not "ordinary least squares" but it results in the same effect summary measures: a slope, interecept, and 95% CIs for their values, just no global test, or F-tests, and no validity to prediction intervals.






    share|cite|improve this answer









    $endgroup$













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      2 Answers
      2






      active

      oldest

      votes








      2 Answers
      2






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      5












      $begingroup$

      Because GLS is BLUE if you know the form of heteroskedasticity (and correlated errors). If you misspecify the form of heteroscedasticity, GLS estimates will lose their nice properties.



      Under heteroscedasticity, OLS remains unbiased and consistent, but you lose efficiency.



      So unless you're certain of the form of heteroscedasticity, it makes sense to stick with unbiased and consistent estimates from OLS. Then adjust inference for heteroskedasticity using robust standard errors which are valid asymptotically if you don't know the form of heteroscedasticity.



      A hybrid approach is to do your best at specifying the form of heteroskedasticity but still apply robust standard errors for inference. See Resurrecting weighted least squares (PDF).



      Modeling is all about tradeoffs and resources. If you are convinced there is nothing to be learned from modeling the form of heteroscedasticity, then specifying its form is a waste of time. I would argue that there is usually something to be learned in empirical applications. But tradition/convention nudges us away from studying heteroscedasticity, looking at the variances, since all they are is "error".






      share|cite|improve this answer











      $endgroup$


















        5












        $begingroup$

        Because GLS is BLUE if you know the form of heteroskedasticity (and correlated errors). If you misspecify the form of heteroscedasticity, GLS estimates will lose their nice properties.



        Under heteroscedasticity, OLS remains unbiased and consistent, but you lose efficiency.



        So unless you're certain of the form of heteroscedasticity, it makes sense to stick with unbiased and consistent estimates from OLS. Then adjust inference for heteroskedasticity using robust standard errors which are valid asymptotically if you don't know the form of heteroscedasticity.



        A hybrid approach is to do your best at specifying the form of heteroskedasticity but still apply robust standard errors for inference. See Resurrecting weighted least squares (PDF).



        Modeling is all about tradeoffs and resources. If you are convinced there is nothing to be learned from modeling the form of heteroscedasticity, then specifying its form is a waste of time. I would argue that there is usually something to be learned in empirical applications. But tradition/convention nudges us away from studying heteroscedasticity, looking at the variances, since all they are is "error".






        share|cite|improve this answer











        $endgroup$
















          5












          5








          5





          $begingroup$

          Because GLS is BLUE if you know the form of heteroskedasticity (and correlated errors). If you misspecify the form of heteroscedasticity, GLS estimates will lose their nice properties.



          Under heteroscedasticity, OLS remains unbiased and consistent, but you lose efficiency.



          So unless you're certain of the form of heteroscedasticity, it makes sense to stick with unbiased and consistent estimates from OLS. Then adjust inference for heteroskedasticity using robust standard errors which are valid asymptotically if you don't know the form of heteroscedasticity.



          A hybrid approach is to do your best at specifying the form of heteroskedasticity but still apply robust standard errors for inference. See Resurrecting weighted least squares (PDF).



          Modeling is all about tradeoffs and resources. If you are convinced there is nothing to be learned from modeling the form of heteroscedasticity, then specifying its form is a waste of time. I would argue that there is usually something to be learned in empirical applications. But tradition/convention nudges us away from studying heteroscedasticity, looking at the variances, since all they are is "error".






          share|cite|improve this answer











          $endgroup$



          Because GLS is BLUE if you know the form of heteroskedasticity (and correlated errors). If you misspecify the form of heteroscedasticity, GLS estimates will lose their nice properties.



          Under heteroscedasticity, OLS remains unbiased and consistent, but you lose efficiency.



          So unless you're certain of the form of heteroscedasticity, it makes sense to stick with unbiased and consistent estimates from OLS. Then adjust inference for heteroskedasticity using robust standard errors which are valid asymptotically if you don't know the form of heteroscedasticity.



          A hybrid approach is to do your best at specifying the form of heteroskedasticity but still apply robust standard errors for inference. See Resurrecting weighted least squares (PDF).



          Modeling is all about tradeoffs and resources. If you are convinced there is nothing to be learned from modeling the form of heteroscedasticity, then specifying its form is a waste of time. I would argue that there is usually something to be learned in empirical applications. But tradition/convention nudges us away from studying heteroscedasticity, looking at the variances, since all they are is "error".







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Nov 26 '18 at 17:08

























          answered Nov 26 '18 at 15:52









          Heteroskedastic JimHeteroskedastic Jim

          3,3141624




          3,3141624

























              0












              $begingroup$

              OLS is still unbiased when the data are correlated (provided the mean model is true). The net effect of heteroscedasticity is that it offsets the errors, so that the 95% CI for the regression is, at times, too tight and at other times too wide. Even still, you can correct the standard errors by using the sandwich or heteroscedasticity consistent standard error (HC) estimator. Technically, this is not "ordinary least squares" but it results in the same effect summary measures: a slope, interecept, and 95% CIs for their values, just no global test, or F-tests, and no validity to prediction intervals.






              share|cite|improve this answer









              $endgroup$


















                0












                $begingroup$

                OLS is still unbiased when the data are correlated (provided the mean model is true). The net effect of heteroscedasticity is that it offsets the errors, so that the 95% CI for the regression is, at times, too tight and at other times too wide. Even still, you can correct the standard errors by using the sandwich or heteroscedasticity consistent standard error (HC) estimator. Technically, this is not "ordinary least squares" but it results in the same effect summary measures: a slope, interecept, and 95% CIs for their values, just no global test, or F-tests, and no validity to prediction intervals.






                share|cite|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  OLS is still unbiased when the data are correlated (provided the mean model is true). The net effect of heteroscedasticity is that it offsets the errors, so that the 95% CI for the regression is, at times, too tight and at other times too wide. Even still, you can correct the standard errors by using the sandwich or heteroscedasticity consistent standard error (HC) estimator. Technically, this is not "ordinary least squares" but it results in the same effect summary measures: a slope, interecept, and 95% CIs for their values, just no global test, or F-tests, and no validity to prediction intervals.






                  share|cite|improve this answer









                  $endgroup$



                  OLS is still unbiased when the data are correlated (provided the mean model is true). The net effect of heteroscedasticity is that it offsets the errors, so that the 95% CI for the regression is, at times, too tight and at other times too wide. Even still, you can correct the standard errors by using the sandwich or heteroscedasticity consistent standard error (HC) estimator. Technically, this is not "ordinary least squares" but it results in the same effect summary measures: a slope, interecept, and 95% CIs for their values, just no global test, or F-tests, and no validity to prediction intervals.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Nov 26 '18 at 16:45









                  AdamOAdamO

                  33.4k261140




                  33.4k261140






























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                      A CLEAN and SIMPLE way to add appendices to Table of Contents and bookmarks

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                      0 I found a lot of questions abount appendices and ToC. Many users want appendices to be grouped in an Appendix part, however some problems arise with ToC, hyperref, PDF viewer bookmarks, and so on. There are different solutions which require extra packages, command patching and other extra code, however none of them satisfies me. I almost found an easy way to accomplish a good result, where appendices are added to bookmarks in the right way and hyperref links point to the right page. However, the number of the "Appendix" part page is wrong (it's the number of appendix A). Is there any EASY way to fix that? This is a MWE: documentclass{book} usepackage[nottoc,notlot,notlof]{tocbibind} usepackage{hyperref} begin{document} frontmatter tableofcontents mainmatter part{First} chapter{...
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                      Calculate evaluation metrics using cross_val_predict sklearn

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                      1 In the sklearn.model_selection.cross_val_predict page it is stated: Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric. Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results? python scikit-learn cross-validation share | improve this question edited Nov 28 '18 at 17:52 desertnaut 20.3k 7 43 79 ...
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                      Insert data from modal to MySQL (multiple modal on website)

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                      0 I have problem with inserting data to MySQL from modal. My modal: <a href="#" class="badge badge-pill badge-success">6 komentarzy</a> <a href="#" class="badge badge-pill badge-danger">brak komentarzy</a> <a data-toggle="modal" href="#add_desk_comm_{$desk_['desk_id']}" data-target="#add_desk_comm_{$desk_['desk_id']}" class="ediiit">(dodaj)</a> <div class="modal fade" id="add_desk_comm_{$desk_['desk_id']}" tabindex="-1" role="dialog" aria-labelledby="edit_printer" aria-hidden="true"> <div class="modal-dialog" role="document"> ...
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