Question 7. Given that Y₁ = Bat + et t=1,2,...,T and et is found to be stationary. c) Consider, Y, is a (Tx 4) data matrix. Suppose you find that Y, is I(1), and proceed to estimate the VECM with 2 lags using the Johansen's full-information MLE approach. AY₁ =D+IIY₁-1+B₁AY₁-1+B₂AY₁-2 +2₂ You realise that the Johansen's approach is not a straight forward estimation where all the parameters are not estimated jointly. List the steps taken to estimate the parameters in the model.
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- A researcher fitted following OLS regression using time series data from 1973 t0 2020 (Bar)BD =-3.7 + 0.08BD lag(t-1), -2.2LnER lag(t), + 42LnEXP lag(t)-33LnRE lag(t), +10 LnPl lag(t), R²=0.99 DW=1.4 RSS=4.5 Where BD is budget deficit as a percentage of GDP, ER, EXP, RE and Pl are Exchange rate, government expenditures, government revenues and per capita income, respectively. Ln shows natural log and "t" stands for time. i :-Interpret above results ii :- Is there any problem of Autocorrelation in above model? How do you knowQuestion 1) Which of the following can cause the usual OLS t statistics to be invalid (that is, not to have t distribu- tions under HO)? (i) Heteroskedasticity. (ii) A sample correlation coefficient of 95 between two independent variables that are in the model. (iii) Omitting an important explanatory variable Question 2) Which of the following can cause OLS estimators to be biased? (i) Heteroskedasticity. (ii) Omitting an important variable. (iii) A sample correlation coefficient of .95 between two independent variables both included in the model.what is stationary and unit root test mean in time sires econometrics? Clearly explain in détail why logarthmic and dummy variables are regularly used in Time series Econometrics ?
- We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows, Dependent Variable: CO2 Method: Least Squares Date: 04/20/17 Time: 09.46 Sample: 1990 2013 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. GDP ENERGY POP 2.002813 0.022114 -0.734352 0.203927 6.458672 0.011872 0.328388 0.293686 0,310097 1.862670 -2.236233 0.694371 0.7597 0.0773 0,0369 0.4954 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.825079 Mean dependentyar 0.798841 0.048515 Akaike info criterion 0.047074 Schwarz criterion 40.75460 Hannan-Quinn.criter. 31.44583 Durbin-Wats on stat 0.000000 3.625982 0.108170 -3.062883 -2.866541 -3.010793 1.410912 S.D. dependent yar a Write down the economie function for the above estimation by using the information obtained from above table| b- Write down the economic model for the above…Fixed effects model with three time periods Consider the following general fixed effects model for use with a panel data set with three time periods (T = 3): yir = 51 +82 d2: +63 d31 + ẞ1x11 + ... + ẞkxisk + Vit where, for t=1, 2, 3, and time period 1 is the base year: yir = value of dependent variable y for i, in year t, in pounds d2, 1 in the second time period (t = 2), and 0 otherwise d3, =1 in the third time period (t = 3), and 0 otherwise Xirl,..., Xink value of explanatory variables for individual i at time f Vir = composite error consisting of the unobserved, time-constant effect (a) and the idiosyncratic error (uir) If there are 106 cross-sectional units in your balanced panel data set, then the total number of observations you have in your data set is For the first time period (1), the intercept is Which of the following would cause pooled OLS estimation of the model to yield biased and consistent estimators? Check all that apply. Measurement error in any explanatory variable…ANOVA Sigmficance F 0,046 df SS MS F 130433116.219 130433116.219 4.083 Regression Residual Total 113 3609911959.86s 31946123.539 114 3740345076.087 Cosfficrent Standard Error Stát Pvalne 1535.215 Intercept Age 10725.802 6.987 0,000 69.964 34.625 2.021 0.046 Which of the following statements is the best explanation of the R? Select one O'A3.5% of the accident damage can be explained by the age of the driver. B. 3.5% of the variation in accidernt damage can be eaplained by variation in the age of the drver. CC3.5% of the coefficients r stat and p value can be explained by the age of the dtver. D.3.5% of the total errar can be eiplained by the SSE Scanned with CamScanner
- O Given are five observations for two variables, x and y. Excel File: data14-25.xlsx 6 9 13 20 Yi 18 9. 26 23 The estimated regression equation is y = 7. 6+ 0. 9x. a. What is the value of the standard error of the estimate (to 4 decimals)? b. Test for a significant relationship by using the t test. Use a = 0.05. What is the value of the t test statistic (to 2 decimals)? What is the p-value? Use Table 2 of Appendix B. Select your answer- What is your conclusion (a = 0.05)? - Select your answer - c. Use the F test to test for a significant relationship. Use a = 0.05. Compute the value of the F test statistic (to 2 decimals). What is the p-value? Use Table 4 of Appendix B. - Select your answer- What is your conclusion? - Select your answer- 21wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]A certain standardized test measures students' knowledge in English and math. The English and math scores for 10 randomly selected students were recorded and analyzed. The results are shown in the computer output. Predictor Coef SE Coef t-ratio Constant -124.13 78.712 0.046 Math 1.223 0.1966 6.220 0.000 S = 34.55 R-Sq = 82.8% R-Sq (Adj) = 83.5% Which of the following represents the standard deviation of the residuals? O 1.223 34.55 78.712 124.13
- OA large city hospital conducted a study to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees. A sample of 10 employees was selected and the following data were collected. D Excel file: data14-13.xlsx Distance to Work Number of Days (miles) Absent 8. 8. 9. 8 6. 10 12 14 14 4 18 If required, enter negative values as negative numbers. a. Select the correct scatter diagram for these data. A. Number of Days Absent 10+ 8+ . 6- 2+ 8. 10 12 14 16 18 20 Distance to Work (miles) 4.A car company wants to know the monthly sales made in ($000), based on the brand of vehicle. The data collected was entered on a MINITAB spreadsheet for analysis. Exhibit II below was subsequently generated. Exhibit 2 Model N Mean Median Tri.Mean Std Dev S.E. Mean Hyundai 23 109 135 107.64 * 1.34 Toyota 27 165 124 143.65 9.5 ** Determine the values of * and **. Give the unbiased point estimate for the average monthly earning from Hyundai Give the unbiased point estimate for the variance of monthly earning from Hyundai Assuming normality, construct and interpret a 90% confidence interval for the average monthly earning from Toyota You are required to test at the 5% level of significance the hypothesis that the average monthly earnings on Hyundai Vehicles is equal to $110,000 versus the alternative that it is different from $110,000.(A) Describe the direction and the form of the relationship aside from the outliers it is moderatley strong. (B) Explain why the direction and form of this relationship make sense.