Suppose you are employed by the Central Bank and given the task to analyze saving behavior of the country. A researcher obtained the following regression results by using time series data with 92 observations. S= total savings, YD= disposable income, IR= interest rate Model 1: S =-2.5 + 0.15YD R²=? (-3.7) (75.2) Model 2: S= -3.2 +0.15YD+ 1.2IR R²=0.987 (-4.8) (59.5) (7.53) Model 3: S= -1.91 + 0.08YD - 0.081 Ŝ ² +0.0006 § ³ - 0.00002 § 4 R²=0.996 SSR = 47.54 (-6.1) (44.5) (-9.5) (8.11) (-0.7) Model 4: S= -2.44 + 0.11YD -0.042 § 2 R²=0.914 SSR = 57.54 (-2.1) (33.3) (1.7) a) Can you compute the R2 of Model 1? If YES, provide the answer. If NO, explain what additional information you would need? b) Discuss the economic intuition for the choice of independent variables in Model 2. Is the sign of estimated coefficients consistent with economic theory? Are the estimated relationships statistically meaningful? c) Can you test the hypothesis that sum of slope coefficients in Model 2 is equal to 2 at 10% significance level? If YES, provide the answer. If NO, explain what additional information you would need? d) Why do you think the researcher estimated multiple models? Which model should the researcher choose to work with? Explain your answer.

The data for this question is given in the file 1.Q1.xlsx(see image)  and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college   (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.

E3

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. what are the implications of Heteroscedasticity if this potential issue in your model?

You are given information on the use of public transportation in the data set within the image. Number of weekly riders                                     y -variable Price per week (of using public transportation) x-variable Population of city                                                 x-variable Monthly income of riders                                     x variable         Average parking rates per month x-variable   (a) Run a regression of the Number of weekly riders on Price per week, Population of city, Monthly income of riders, Average parking rates per month. Please copy and paste your regression results and do not submit your EXCEL worksheet.   (b) If the average parking rates increase by $1, what will be the change in the number of weekly riders? Will this change be a statistically significant change? Please explain.   (c) If the price per week (of using public transportation) increases by $1, what will be the change in the number of weekly riders? Will this change be…

The following question refers to this regression equation (standard errors for each of the estimated coefficients are in parenthesis). Q=8,400-8" P+5" A+ 4** Px +0.05**1, (1,732) (2.29) (1.36) (1.75) (0.15) Q = Quantity demanded P = Price 1,100 Advertising expenditures, in thousands = 20 P = price of competitor's good = 600/= average monthly income 10,000 What is the advertising elasticity of demand? Round your answer to two decimal places. Your Answer: The t-statistic is computed by dividing the regression coefficient by the standard error of the coefficient. dividing the regression coefficient by the standard error of the estimate. dividing the standard error of the coefficient by the regression coefficient. dividing the R2 by the F-statistic. none of the specified answers are correct.

A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 5 10 7 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R0.998868 R2 0.997738 Coefficient |14.88462 Interest Rate -1.61538 Intercept 1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain your reasoning.The dealership's vice- president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending…

A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (X1), the living area of the house in square feet (X2), and the number of bedrooms (X3). The following regression model was chosen using a data set of house statistics: y=88,399554791.3333x231,471.1372x3 The first house from the data set had the following values: Selling price $324,000 Age - 22 years Square Feet 2.000 Bedrooms 3 The residual for this house is 23,558 -41,480 10,216 -16,095 27

solve within 30 mins.

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Heteroscedasticity and give a reason