Thank you for your query Miss specification error can be addressed by using a number of treatments: First one is to consult the theory and c if theory suggests a nonlinear relationship. You can also try some other new variables that are more relevant. You can check for other forms of model for example logarithmic model exponential model etc. You should also be mindful of any problems of the data like heteroscedasticity, auto-correlation etc.
If I put log, there is misspecification error. If no log, the model is stable. However, the variable should put log to get small value and also based on previous studies. What’s your suggestion, Dr.?
This shows that non-log model may be suitable for your situation. But its suitable in this case, that your variables are not having very large values (high variance).
Dear Professor Bilal, I really appreciate your effort. I would like to ask if the way of testing the misspecification differs when I run fixed (or random) effect model? or it is the same as it is in this video? Maybe I got confused from watching many videos in this regard as I saw many different approach to test the omitted variables. Moreover, I would like to ask you if the GLS estimation method is appropriate when I have random-effects model with problems like heteroskedasticity and autocorrelation, but my N is greater than the T. I hope I can get your email if you do not mind. Thank you in advance.
Testing for misspecification in fixed or random effects models is similar to the approach in the video, but with some modifications. For fixed effects models, you can use the Hausman test to check for misspecification. For random effects models, you can use the Breusch-Pagan Lagrange multiplier test (LM test) to check for misspecification. Regarding GLS estimation: If you have a random effects model with heteroskedasticity and autocorrelation, and N > T, GLS estimation is appropriate. GLS can handle heteroskedasticity and autocorrelation, and is suitable for panel data with a large number of cross-sectional units (N) and a smaller number of time periods (T). Please note that it's important to check the assumptions of the GLS estimation method, such as normality of errors and homoscedasticity, to ensure reliable results.
@@drbilalmehmood206 Appreciate your sincere response. Thank you Sir. I do not know how to test for the assumptions of the GLS estimation method, as I think that supposedly this technique (GLS) overcome problems of Hetero and serial correlation.
GLS handles heteroskedasticity and serial correlation. Its Assumptions include linear relationship, no perfect multicollinearity, errors with normal pattern, consistent variance of errors. Better to check Heteroskedasticity using Breusch-Pagan LM and Serial Correlation using Durbin-Watson or LM test. Moreover, GLS can handle big N and small T.
@@drbilalmehmood206 I really appreciate your response. I hope I can contact you over and over. I sent you an email 2 days ago. Please keep posting more videos in the same direction. You save lives with your beneficial science.
Thank you so much for your kind words! I truly appreciate your support and encouragement. It's heartwarming to know that the content I create has a positive impact. Rest assured, I'll continue sharing valuable information and creating content in the same direction. If you have any more questions or need assistance, feel free to reach out anytime. Keep learning and stay curious! bilalmehmood.dr@gmail.com
Respected Dr, In my analysis _hat is coming significant (0.000) whereas _hatsq is coming insignificant. Can i show the results this way. Is there any problem with my model specification. Kind regards.
The significance of _hat (0.000) and insignificance of _hatsq suggests that the linear term is significant, but the quadratic term is not. This is not uncommon, especially when exploring non-linear relationships. Potential Solutions: 1. Remove the quadratic term (_hatsq) and if it's not significant, it might not be contributing meaningfully to the model. 2. Reconsider the functional form by explore alternative non-linear specifications, such as a logarithmic or interaction term. 3. Check for multicollinearity by using Variance Inflation Factor (VIF) to ensure that the linear and quadratic terms are not highly correlated.
Why can we rejct the hypothesis for the ovtest? is it because it is less than .01? so if it were for instance .05 we would fail to reject? Also, why for the linktest is .166 insignificant? would .002 for hat be significant as well as .516 for hatsq, would that be significant too?
Answer -1: "Statistical significance is at three levels. 1) 1% (less than 0.01), 2) 5% (less than 0.05) and 1) 10% (less than 0.1). 1% and 5% are undisputed, while some authors/statisticians, don't prefer 10% level of significance. So for (less than 0.01 or less than 0.05), the null hypothesis can be, undisputedly, rejected." Answer -2: "p-value=.166 is greater than 0.01(1%) and 0.05(5%) and even 0.1(10%). So its statistically insignificant at all levels." Answer -3: would .002 for hat be significant, "Yes, statistically significant" Answer -4: as well as .516 for hatsq, would that be significant too?; "No, statistically insignificant."
Thank you for your query Miss specification error can be addressed by using a number of treatments: First one is to consult the theory and c if theory suggests a nonlinear relationship. You can also try some other new variables that are more relevant. You can check for other forms of model for example logarithmic model exponential model etc. You should also be mindful of any problems of the data like heteroscedasticity, auto-correlation etc.
Thank you sir for useful information
Welcome
Thank you sir. When you find that there is a Mis-specifacation in your model, what are the measures to correct them.
Thank you for your query
Miss specification error can be addressed by using a number of treatments:
First one is to consult the theory and c if theory suggests a nonlinear relationship.
You can also try some other new variables that are more relevant.
You can check for other forms of model for example logarithmic model exponential model etc.
You should also be mindful of any problems of the data like heteroscedasticity, auto-correlation etc.
If I put log, there is misspecification error. If no log, the model is stable. However, the variable should put log to get small value and also based on previous studies. What’s your suggestion, Dr.?
This shows that non-log model may be suitable for your situation. But its suitable in this case, that your variables are not having very large values (high variance).
Thanks Dr.
Dear Professor Bilal, I really appreciate your effort. I would like to ask if the way of testing the misspecification differs when I run fixed (or random) effect model? or it is the same as it is in this video? Maybe I got confused from watching many videos in this regard as I saw many different approach to test the omitted variables. Moreover, I would like to ask you if the GLS estimation method is appropriate when I have random-effects model with problems like heteroskedasticity and autocorrelation, but my N is greater than the T. I hope I can get your email if you do not mind. Thank you in advance.
Testing for misspecification in fixed or random effects models is similar to the approach in the video, but with some modifications.
For fixed effects models, you can use the Hausman test to check for misspecification.
For random effects models, you can use the Breusch-Pagan Lagrange multiplier test (LM test) to check for misspecification.
Regarding GLS estimation:
If you have a random effects model with heteroskedasticity and autocorrelation, and N > T, GLS estimation is appropriate.
GLS can handle heteroskedasticity and autocorrelation, and is suitable for panel data with a large number of cross-sectional units (N) and a smaller number of time periods (T).
Please note that it's important to check the assumptions of the GLS estimation method, such as normality of errors and homoscedasticity, to ensure reliable results.
@@drbilalmehmood206 Appreciate your sincere response. Thank you Sir. I do not know how to test for the assumptions of the GLS estimation method, as I think that supposedly this technique (GLS) overcome problems of Hetero and serial correlation.
GLS handles heteroskedasticity and serial correlation.
Its Assumptions include linear relationship, no perfect multicollinearity, errors with normal pattern, consistent variance of errors.
Better to check Heteroskedasticity using Breusch-Pagan LM and Serial Correlation using Durbin-Watson or LM test.
Moreover, GLS can handle big N and small T.
@@drbilalmehmood206 I really appreciate your response. I hope I can contact you over and over. I sent you an email 2 days ago. Please keep posting more videos in the same direction. You save lives with your beneficial science.
Thank you so much for your kind words! I truly appreciate your support and encouragement. It's heartwarming to know that the content I create has a positive impact. Rest assured, I'll continue sharing valuable information and creating content in the same direction. If you have any more questions or need assistance, feel free to reach out anytime. Keep learning and stay curious!
bilalmehmood.dr@gmail.com
Respected Dr, In my analysis _hat is coming significant (0.000) whereas _hatsq is coming insignificant. Can i show the results this way. Is there any problem with my model specification. Kind regards.
The significance of _hat (0.000) and insignificance of _hatsq suggests that the linear term is significant, but the quadratic term is not. This is not uncommon, especially when exploring non-linear relationships.
Potential Solutions:
1. Remove the quadratic term (_hatsq) and if it's not significant, it might not be contributing meaningfully to the model.
2. Reconsider the functional form by explore alternative non-linear specifications, such as a logarithmic or interaction term.
3. Check for multicollinearity by using Variance Inflation Factor (VIF) to ensure that the linear and quadratic terms are not highly correlated.
Why can we rejct the hypothesis for the ovtest? is it because it is less than .01? so if it were for instance .05 we would fail to reject?
Also, why for the linktest is .166 insignificant? would .002 for hat be significant as well as .516 for hatsq, would that be significant too?
Answer -1: "Statistical significance is at three levels. 1) 1% (less than 0.01), 2) 5% (less than 0.05) and 1) 10% (less than 0.1). 1% and 5% are undisputed, while some authors/statisticians, don't prefer 10% level of significance. So for (less than 0.01 or less than 0.05), the null hypothesis can be, undisputedly, rejected."
Answer -2: "p-value=.166 is greater than 0.01(1%) and 0.05(5%) and even 0.1(10%). So its statistically insignificant at all levels."
Answer -3: would .002 for hat be significant, "Yes, statistically significant"
Answer -4: as well as .516 for hatsq, would that be significant too?; "No, statistically insignificant."
What if there is misspecification error, Dr? How to solve it? The hat is significant
Thank you for your query
Miss specification error can be addressed by using a number of treatments:
First one is to consult the theory and c if theory suggests a nonlinear relationship.
You can also try some other new variables that are more relevant.
You can check for other forms of model for example logarithmic model exponential model etc.
You should also be mindful of any problems of the data like heteroscedasticity, auto-correlation etc.