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We want to look at potential predictors of movie revenues. Model 1: OLS, using observations l-609...
I have a model from Gretl which I'm trying to copy the latex code for so l can paste in a web browser. However, when...
I have a model from Gretl which I'm trying to copy the latex
code for so l can paste in a web browser. However, when l submit
the code I'm getting weird results and the table is not coming out
how l would like. How do l get the latex code from the Gretl model
to format exactly the same on this web browser.
Latex Code from copying the output:
\begin{center}
Model 1: OLS, using observations 1--60\\
Dependent variable: l\_Y\\...
a bivariate regression of the form: Y i = β o+β 1X i + u i...
a bivariate regression of the form:
Y i = β o+β 1X i + u
i
What economic meaning, if any, does the coefficient
β1 have in your model? What does the estimated value of
this parameter indicate about the relationship between X and Y?
Model 2: OLS, using observations 1965-2000 (T 36) Dependent variable: C coefficient std. error t-ratio p-value 4.851 2.68e-05 xx 4.17e-40 const 252.253 51.9974 Yd 0.959305 0.0121514 78.95 Mean dependent var 3672.531 S.D. dependent var 1223.338...
1. Propose any one interaction hypothesis among the set of independent variables for each of the ...
1. Propose any one interaction hypothesis among the set of
independent variables for each of the two models and provide
rationales for your proposition.
2. Test whether your proposition is supported by the data
ependent Variable SALARY Method: Least Squares Date: 03/28/19 Time: 17:11 Sample: 1 447 Included observations: 447 Variable Coefficient Std. Error t-Statistic Prob TOTCOMP_MEYU TENURE_MEYU AGE_MEYU SALES_MEYU PROFITS_MEYU ASSETS_MEYU 857.5376 596.2939 1.438112 0.1511 0.014302 0.002179 6.564320 0.0000 27.40055 9.066757 3.022090 0.0027 7.034349 10.952730.642246 0.521 0.013978 0.006320 2.211800...
Attached are the results of a diagnostic test on an estimated model, autocorrelation, heteoskedasticity and non-normal...
Attached are the results of a diagnostic test on an estimated
model, autocorrelation, heteoskedasticity and non-normality
respectivey, can you please comment on the results and state the
conclusion for each test using a 5% significance level
Breusch-Godfrey Serial Correlation LM Test F-statistic Obs R-squared 0.7659 0.7612 0.458959 Prob. F(4,438) 1.861565 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 05/22/19 Time: 22:02 Sample: 1982M01 2019M02 Included observations: 446 Presample missing value lagged residuals set to zero. Coefficient Std....
Predict the value of a traditional style house with 2500 square feet of area, that is 20 years old, with 3 bedrooms and two bathrooms, which is owner occupied at the time of sale, with a fireplace, an...
Predict the value of a traditional style house with 2500 square
feet of area, that is 20 years old, with 3 bedrooms and two
bathrooms, which is owner occupied at the time of sale, with a
fireplace, and not on the waterfront. Provide the “corrected
predictor”. (Prediction in the log-linear model.) need help with
the corrected predictor.
Sample: 1 1080 Included observations: 1080 Variable Coefficient Std. Error t-Statistic Prob 3.971283 0.045870 86.57653 0.0000 0.030016 0.001388 21.62198 0.0000 0.031281 0.016548 1.890282...
An interpretation is needed for the below E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View Proc...
An interpretation is needed for the below
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View Proc Object Print Name Freeze Estimate Forecast Stats Resids Dependent Variable: GDPPERCAPITA Method: Least Squares Date: 01/19/19 Time: 21:27 Sample (adjusted): 2 264 Included observations: 142 after adjustments Variable Coefficient Std. Error t-Statistic Prob EDUEXPENSES 3409.799982.7287 3.469726 0.0007 60.62503 50.33194 1.204504 0.2305 248.8894 62.51844 3.981056 0.0001 299.3805 136.4002 2.194869 0.0299 529.2544297.0670 1.781599 0.0771 VALUEADDAGRI 840.2738 141.5672 -5.935512 0.0000 2227.235 7946.208 0.280289 0.7797 EXPORTS FDINFLOWS GSAVING...
An interpretation is needed for the below picture E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View...
An interpretation is needed for the below picture
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View Proc Object Print Name Freeze Estimate Forecast Stats Resids Dependent Variable: GDPPERCAPITA Method: Least Squares Date: 01/19/19 Time: 21:40 Sample (adjusted): 2 264 Included observations: 142 after adjustments Variable Coefficient Std. Error t-Statistic Prob EDUEXPENSES FDINFLOWS GSAVING UNEMPR 3430.904 984.1997 3.485983 0.0007 285.7443 54.60948 5.232504 0.0000 321.8211 135.3456 2.377772 0.0188 557.7184 296.6160 1.880271 0.0622 VALUEADDAGRI 898.3994 133.3089 6.739232 0.0000 4784.332 7670.051 0.623768 0.5338 R-squared...
An interpretation for Heteroskedasticity for below picture E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. View Proc Object...
An interpretation for Heteroskedasticity for below picture
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. View Proc Object Print Name Freeze Estimate Forecast Stats Resids Heteroskedasticity Test: Breusch-Pagan-Godfrey X F-statistic Obs R-squared Scaled explained SS 5.112724 Prob. F(4,137) 18.44402 Prob. Chi-Square(4) 37.67378 0.0007 0.0010 0.0000 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID 2 Method: Least Squares Date: 01/19/19 Time: 22:00 Sample: 2 264 Included observations: 142 Variable Coefficient Std. Error t-Statistic Prob 4.54E+08 2.09E+08 2.170543 0.0317 EDUEXPENSES 85458316 30075552 2.841455 0.0052 805579.71666856....
The following show the results of regression: Housing Sold = b0 + b1 permit +b2 price...
The following show the results of regression: Housing Sold = b0 + b1 permit +b2 price + b3 employment Dependent Variable: SOLD , Method: Least Squares Date: 03/15/20 Time: 14:59 Included observations: 108 Variable Coefficient Std. Error t-Statistic Prob. C -61520.76 167763.0 -0.366712 0.7146 PERMIT 15.98282 .280962 12.47721 0.0000 PRICE ...
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E,...
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E, and the estimated model: Model 1: OLS, using observations 1959-1995 (T = 37) Dependent variable: c t-ratio p-value const time Disposable Income Coefficient Std. Error 2707.84 385.254 80.9122 13.6539 0.508123 0.0460444 Mean dependent var Sum squared resid R-squared F(2, 34) Log-likelihood Schwarz criterion rho 11328.65 304975.4 0.998650 12577.63 -219.3165 449.4657 0.551018 S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson...
I have a model from Gretl which I'm trying to copy the latex
code for so l can paste in a web browser. However, when l submit
the code I'm getting weird results and the table is not coming out
how l would like. How do l get the latex code from the Gretl model
to format exactly the same on this web browser.
Latex Code from copying the output:
\begin{center}
Model 1: OLS, using observations 1--60\\
Dependent variable: l\_Y\\...
a bivariate regression of the form:
Y i = β o+β 1X i + u
i
What economic meaning, if any, does the coefficient
β1 have in your model? What does the estimated value of
this parameter indicate about the relationship between X and Y?
Model 2: OLS, using observations 1965-2000 (T 36) Dependent variable: C coefficient std. error t-ratio p-value 4.851 2.68e-05 xx 4.17e-40 const 252.253 51.9974 Yd 0.959305 0.0121514 78.95 Mean dependent var 3672.531 S.D. dependent var 1223.338...
1. Propose any one interaction hypothesis among the set of
independent variables for each of the two models and provide
rationales for your proposition.
2. Test whether your proposition is supported by the data
ependent Variable SALARY Method: Least Squares Date: 03/28/19 Time: 17:11 Sample: 1 447 Included observations: 447 Variable Coefficient Std. Error t-Statistic Prob TOTCOMP_MEYU TENURE_MEYU AGE_MEYU SALES_MEYU PROFITS_MEYU ASSETS_MEYU 857.5376 596.2939 1.438112 0.1511 0.014302 0.002179 6.564320 0.0000 27.40055 9.066757 3.022090 0.0027 7.034349 10.952730.642246 0.521 0.013978 0.006320 2.211800...
Attached are the results of a diagnostic test on an estimated
model, autocorrelation, heteoskedasticity and non-normality
respectivey, can you please comment on the results and state the
conclusion for each test using a 5% significance level
Breusch-Godfrey Serial Correlation LM Test F-statistic Obs R-squared 0.7659 0.7612 0.458959 Prob. F(4,438) 1.861565 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 05/22/19 Time: 22:02 Sample: 1982M01 2019M02 Included observations: 446 Presample missing value lagged residuals set to zero. Coefficient Std....
Predict the value of a traditional style house with 2500 square
feet of area, that is 20 years old, with 3 bedrooms and two
bathrooms, which is owner occupied at the time of sale, with a
fireplace, and not on the waterfront. Provide the “corrected
predictor”. (Prediction in the log-linear model.) need help with
the corrected predictor.
Sample: 1 1080 Included observations: 1080 Variable Coefficient Std. Error t-Statistic Prob 3.971283 0.045870 86.57653 0.0000 0.030016 0.001388 21.62198 0.0000 0.031281 0.016548 1.890282...
An interpretation is needed for the below
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View Proc Object Print Name Freeze Estimate Forecast Stats Resids Dependent Variable: GDPPERCAPITA Method: Least Squares Date: 01/19/19 Time: 21:27 Sample (adjusted): 2 264 Included observations: 142 after adjustments Variable Coefficient Std. Error t-Statistic Prob EDUEXPENSES 3409.799982.7287 3.469726 0.0007 60.62503 50.33194 1.204504 0.2305 248.8894 62.51844 3.981056 0.0001 299.3805 136.4002 2.194869 0.0299 529.2544297.0670 1.781599 0.0771 VALUEADDAGRI 840.2738 141.5672 -5.935512 0.0000 2227.235 7946.208 0.280289 0.7797 EXPORTS FDINFLOWS GSAVING...
An interpretation is needed for the below picture
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. X View Proc Object Print Name Freeze Estimate Forecast Stats Resids Dependent Variable: GDPPERCAPITA Method: Least Squares Date: 01/19/19 Time: 21:40 Sample (adjusted): 2 264 Included observations: 142 after adjustments Variable Coefficient Std. Error t-Statistic Prob EDUEXPENSES FDINFLOWS GSAVING UNEMPR 3430.904 984.1997 3.485983 0.0007 285.7443 54.60948 5.232504 0.0000 321.8211 135.3456 2.377772 0.0188 557.7184 296.6160 1.880271 0.0622 VALUEADDAGRI 898.3994 133.3089 6.739232 0.0000 4784.332 7670.051 0.623768 0.5338 R-squared...
An interpretation for Heteroskedasticity for below picture
E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. View Proc Object Print Name Freeze Estimate Forecast Stats Resids Heteroskedasticity Test: Breusch-Pagan-Godfrey X F-statistic Obs R-squared Scaled explained SS 5.112724 Prob. F(4,137) 18.44402 Prob. Chi-Square(4) 37.67378 0.0007 0.0010 0.0000 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID 2 Method: Least Squares Date: 01/19/19 Time: 22:00 Sample: 2 264 Included observations: 142 Variable Coefficient Std. Error t-Statistic Prob 4.54E+08 2.09E+08 2.170543 0.0317 EDUEXPENSES 85458316 30075552 2.841455 0.0052 805579.71666856....
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E, and the estimated model: Model 1: OLS, using observations 1959-1995 (T = 37) Dependent variable: c t-ratio p-value const time Disposable Income Coefficient Std. Error 2707.84 385.254 80.9122 13.6539 0.508123 0.0460444 Mean dependent var Sum squared resid R-squared F(2, 34) Log-likelihood Schwarz criterion rho 11328.65 304975.4 0.998650 12577.63 -219.3165 449.4657 0.551018 S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson...
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