A researcher wants to assess the impact of school location on CSEC performances in Guyana. Suppose she categorises schools by Administrative Regions, how many dummy variables must be present in the model specification? The first difference of the logarithmic transformation is equivalent to
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A researcher wants to assess the impact of school location on CSEC performances in Guyana. Suppose she categorises schools by Administrative Regions, how many dummy variables must be present in the model specification?
The first difference of the logarithmic transformation is equivalent to
a.The elasticity models
b.The difference between a variable and its lag
c.The growth rate of the series
d.The autoregressive model
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- tate whether the following statements are true or false with a brief explanation: a) Logit model is estimated by minimising the sum of the squares residuals of the model. b) In difference-in-differences analysis, the assumption of ‘parallel trends’ is generally testable. c) Suppose you have estimated a model Y = 0.2 – 0.7D + 2X + 0.4X*D. Y and X are continuous variables and D is a dummy variable. If D=1, the marginal effect of X on Y is always larger, and therefore the predicted Y is always larger, than in the case where D=0. d) The first order autoregressive model can be stationary or non-stationary. e) The bias in Instrumental Variables estimator depends on the number of observations.( TRUE OR FALSE help me find the true or false questions ) 1. In economic statistics and Econometrics, we do the same thing.( ) 2. As same in regression analysis, variables in relation analysis are all random variables.( ) 3. Known as residual, "i is an estimate of u , the random disturbance term.( ) 4. The slope coefficient of the log-log model measures the elasticity of Y with respect to X.( ) 5. In regression of standardized variables, the intercept term is always zero.( ) 6. The underlying theory may suggest a particular functional form.( ) 7. The disturbance term u is assumed to follow normal distribution.( ) 8. White test is used to check if there exists multicollinearity in the disturbance term of a regression function.( ) 9. Dummy variable can be used to test the stability of a regression model just as the function of the Chow Test.( ) 10. Where there is autocorrelation in the u , the OLS estimators are not BLUE estimators any more.( )From a local town record, information is gathered about workers' wages and their educational achievements. You are interested in exploring a possible relationship between the rate of change in workers' wages and the number of education years completed. a. Write down a model (not an estimated equation) that implies percentage changes (or relative changes) in wage due to changes years of education. Indicate which parameter measures the relative change in worker's wage
- A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?In general, what is true about the relationship between the Sum of Squared Residuals in the restricted and unrestricted model? a. SSRr = R-squared * SSRur b. SSRr < SSRur c. SSRr > SSRur d. SSRr = SSRurWhat is event modeling? Multiple Choice The event model adjusts data for each of the events identified to distort the trend and seasonal patterns of the time series. The event model adds a growth factor to the moving average as a way of adjusting for the event. The event model adds a smoothing constant for the events identified as important in the historical data. The event model initializes events so they can be selected to initialize or warm up the model.
- Consider the following Model (notation is standard):C=c(y-τ) I=i(r) y = C+I+G Md/P = L(y, r) Md= Ms=My = f(n) n = h(W/P) f´(n) = W/P Calculate the effects of a change in τ on C, I, r, y and P.True or False? WLS is preferred to OLS when an important variable has been omitted from the model.Suppose that an economist has been able to gather data on the relationship between demand and price for a particular product. After analyzing scatterplots and using economic theory, the economist decides to estimate an equation of the form Q= aPb, where Q is quantity demanded and P is price. An appropriate regression analysis is then performed, and the estimated parameters turn out to be a = 1000 and b = - 1.3. Now consider two scenarios: (1) the price increases from $10 to $12.50; (2) the price increases from $20 to $25. a. Do you predict the percentage decrease in demand to be the same in scenario 1 as in scenario 2? Why or why not? b. What is the predicted percentage decrease in demand in scenario 1? What about scenario 2? Be as exact as possible.
- An independent variable in a model is also called:a. explained variable.b. unexplained variable.c. explanatory variable.d. dependent variable.A realtor was investigating the price of real estate based on the size of the house in square feet x1 and if the house was within walking distance of an "A" rated public school. The indicator variable is defined as x = 1 if the house is within walking distance of an "A" rated public school and x = 0 if the house is NOT within walking distance of an "A" rated public school. If there was interaction in the regression problem, an appropriately fit regression model would have…? a) A different slope and different y-intercept for those within walking distance and those not. b) A different y-intercept for those that were within walking distance and those that were not; the slope would not change. c) A different slope, but not a different y-intercept for those within walking distance and those not. d) Cannot be determinedPlease no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…