Відео

Humble Pakistani

@@drbilalmehmood206 is pdf for this series available?

@@drbilalmehmood206 is pdf available for this series?

Regards from Pakistan

Glad you think so!

welcome.

Thanks

it has good reviews. it can be used. why it is always better to check every new packet and give feedback to the seller. I do the same and more importantly the best thing is to make it at home, if possible.

Thanks a lot

Feeling of sand to a little extent is ok as the powder can have thick particles but it should be actual sand. If company hasn't washed the leaves properly, sand and dust can creep into the powder. Still the thick particles and sand are different in mouth. One can feel the subtle difference by using the tongue. Finally the best option is to grow and make it at home. Else buy from a reputed seller.

Plot 121 Road No:23, Prashasan Nagar, Hyderabad, Telangana 500033, India

Thanks a lot 😊

Thanks. CC (capital costs) and O&M (Operation & Maintenance) costs (actually whole of the data), are likely to be from J Price Gittinger's "Economic Analysis of Agricultural Projects". Not sure as it's been a while but it's likely.

Welcome

You are most welcome

When using Driscoll-Kraay estimator, you don't always need to take the first difference if your data has a trend or isn't stable. But, it's a good idea to fix the stability issue first by using either of these three options: 1. Remove the trend by taking the first difference or detrending. 2. Transform the data to make it stable (e.g., logging or normalizing). 3. Use a different method that can handle unstable data (e.g., Hodrick-Prescott filter or Canova-Hansen test).

​@@drbilalmehmood206 I have already transformed the data into logarithmic form. But there is still a stationery problem. Only 1 variable is stationary at the level in the 5 explanatory variables [1%] while 2 variables are stationary at level considering 10% significance level. The data is also suffering from CD, hetero and autocorrelation. Need your guidance on what will be the appropriate procedure for the estimation [t<n]. thanks!

When the data is not stationary then Difference Regression can be used. And for cross sectional dependence, heteroskedasticity and auto correlation there are many remedial measures and advanced regression techniques. to cope with t<n, bias corrected and jacknife estimators are used.

Thank you Mr. Aakash. In India, there are many students who study Mathematical Economics. I hope you'll share this channel with them so they learn and comment too. Regards.

Hi, introducing difference operator should not be led by temptation of desired results. Rather by logic and the logic is correct that they need to be differenced. And using the d. operator is correct syntax to do it. Good Luck

Okay thanks.

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

corr(ar1): checks for first-order autocorrelation (relationship between a value and its immediate previous value). corr(psar1): checks for partial autocorrelation, (relationship between a value and its previous value, while controlling for the effects of intermediate values). In simple terms, Wald Chi-Square tell you if a variable is important in explaining the behavior of your data. A high value means the variable is important, while a low value means it's not as important. A high Wald Chi-Square value (e.g., > 10) indicates that the variable(s) have a significant effect on the model. while a low Wald Chi-Square value (e.g., < 3) indicates that the variable(s) do not have a significant effect on the model.

Yes, it is considered as one of the reputed brands in Indian Subcontinent, however, its wise to insist on freshness of the product, no matter the brand.

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 for your comment. Yes, indeed it depends upon the elasticity of substitution (σ). If: σ=∞ then we have Perfect Substitute inputs σ=1 then we have Cobb-Douglas inputs σ=0 then we have Perfect Complements(inputs). It can be proved by substituting values of Substitution Parameter (𝝆) in the formula σ={1/(1+𝝆)}, as follows: 𝝆=-1 𝝆=0 𝝆=∞

Thanks for asking: Two points asked. 1. No strict requirement on the time dimension (T) for using xtscc. However, it’s essential to consider the balance between T and the cross-sectional dimension (N). If T is much smaller than N, the standard pooled cross-sectional estimator may be inappropriate. Hence, xtscc becomes particularly useful. 2. When using a fixed effects model, both GLS (Generalized Least Squares) and Driscoll and Kraay standard error models are reliable options, but they serve different purposes. GLS is a more efficient estimator that accounts for heteroscedasticity and autocorrelation in the residuals, providing more accurate parameter estimates. Driscoll and Kraay standard error model, on the other hand, is a robust standard error estimator that accounts for cross-sectional dependence and heteroscedasticity, providing more accurate inference. If you're concerned about efficient estimation, GLS might be a better choice. If you're concerned about robust inference, Driscoll and Kraay might be a better choice. However, if you're dealing with a large panel dataset, Driscoll and Kraay is often the preferred option due to its ability to handle cross-sectional dependence.

​@@drbilalmehmood206 thank you so much for your answer. In my model, T=5, N=63 and there are heteroskedasticity, cross-sectional correlation and cross-sectional dependence. Is the Drisscoll-Kraay model appropriate? Thank you!

Compared to xtregar, xtscc a better (model) command to use, as it fulfills most of your data requirements (T<N) and issues (heteroskedasticity, auto-correlation and cross-sectional dependence). However, this is not the most suitable model. As there are others as well. That account for Endogeneity (e.g. GMM) and slope heterogeneity in addition to cross-sectional dependence (e.g. CS-ARDL). So upto you if you want upgrade your analysis, or continue with D.K regression. Good Luck.

@@drbilalmehmood206 I really appreciate your help, Sir. Thanks a lot.

Yes, you can use the "marginal effects" command in Stata. 1. Estimate the FGLS model: `fgls y x1 x2 x3, fml( formula )` 2. Calculate the marginal effects: `margins, dydx(*)` This will give you the marginal effects of each independent variable on the dependent variable. To simulate a 10 percent change in one of the independent variables, you can use the `at()` option: `margins, dydx(x1) at(x1=(1.10*x1))` This will calculate the marginal effect of a 10 percent increase in `x1` on the dependent variable `y`.

@@drbilalmehmood206 ty sir. I'll try it.

The Decision Rule Criterion (DRC) is still used in economic decision-making, but it's often combined with or incorporated into more comprehensive analytical frameworks. One such approach is general equilibrium models, which can generate various cost-benefit ratios and provide a more detailed analysis of economic policies and their impacts. These models consider the interdependencies between different sectors of the economy, offering a more nuanced understanding of policy effects. This integrated approach provides a more detailed and holistic view of economic decision-making.

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.

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.

taking logarithm depends upon the degree of variance of the given variable and is suitable when we need to linearize the variable.

Thanks Dr.

If I use log variable, so I dropped the command no log.

'no log' option in XTGLS command hides the log file of stata and it has nothing to do with logarithm.

Thanks Dr for the info

Yes, with your support and prayers. Keep sharing!

Welcome

wasalam. Garbage in Garbage out...remember this when analysing the data. make sure your data is clean and free of errors.

delete all variables in Variables window of stata with names like _est_MG, _est_mg, _est_PMG, _est_pmg, _est_rDFE and _est_DFE. Assuming that dependent variable is x and independent variable is y, then you can run all of the following commands to get results of MG, PMG and DFE, in on go: xtpmg d.y d.x, lr(l.y x) ec(EC) replace mg xtpmg d.y d.x, lr(l.y x) ec(EC) replace pmg hausman mg pmg, sigmamore xtpmg d.y d.x, lr(l.y x) ec(EC) replace dfe hausman mg DFE, sigmamore

@@drbilalmehmood206 Dear Dr Bilal, Thank you for your courteous response. Even after removing that variable, the same error message show : "invalid new variable name; variable name __ec is present in the list of predictors." and r(110).

you may consider sharing the data on my email

Welcome

waslam. it's leaves, flowers, pods and roots are edible but if you are talking about its wood (Lakree) then it's not to eat. make sure to use it's various parts in the specific way in order to avoid any hazard.

At time 7:25, the explanation of gx and gy is spoken. Also, if your purpose is prepare numerically then focus on the numerical example. It's a demanding topic but not too difficult to understand.

Thank you sir .Got the concept.

Dear Commenter, you are right about this discrepancy. But the gap of 5% is only a convention and not a rule of thumb. From the exam point of view, it is advisable to adhere to such conventions. Remember that 5% is the maximum gap and you can minimize it even to 1% in order to increase the precision of the interpretation.

thanks

Glad it was helpful!

Yes, you are right. By putting the value of Q as 2, the value of C should be 20. Thanks for correction.

pleasure

Glad you think so!

Wasalam. it is better to use some information criteria and as in the video it's guides that number of flags are automatically chosen on the basis of the information criterion that you choose. else you can run a regression and apply the information criterion like it is done in routine.