Using Lewis’ Two-Sector Model, explain why Kuznets’ hypothesis on income-per- capita and Gini can have inverted U-shape.
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Using Lewis’ Two-Sector Model, explain why Kuznets’ hypothesis on income-per- capita and Gini can have inverted U-shape.
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- In economics we are often faced with causal problems where the endogeneity arises because of simultaneity. A classic example is that if you are interested in estimating a demand curve, the issue is what you observe in the data are equli- birum and prices and quantity which is not only a function of demand but also a function of supply. In this excercise we will generalize the problem of simultaneity bias. Consider a situation where we are interested in the effect of Ti on Yi, i.e. obtaining a consistent estimate of α. But Yi also effects Ti. Let Xi be some exogenous covariates affecting both Yi and Ti. Let us imagine you have an exogenous variable Z which only effects Ti but does not directly effect Yi. In particular, consider the following structural equations: Yi = αTi+Xi′β+ui (1) Ti = ρYi+Xi′γ+Ziδ+νi (2) where E(ui | Xi,Zi) = 0 and E(vi | Xi,Zi) = 0 (a) Show why you cannot you use OLS to estimate α consistently in model 1? (b) Solve for the reduced form equation for Ti…7The Excel file (Forecasting Assignment Data) contains quarterly motorcycle shipments for Harley-Davidson from 2000 through 2006. Forecast Harley-Davidson quarterly motorcycle shipments for 2007 using 1) Holt’s Method 2) Winter’s Method 3) Linear Trend Model 4) Quadratic Trend Model 5) Quadratic Trend with Seasonal Indices Model In addition, make a recommendation of which forecasting method you think Harley-Davidson should use and why. Year Quarter Period DemandAt 2000 1 1 49,057 2 2 53,329 3 3 48,077 4 4 54,129 2001 1 5 54,154 2 6 60,161 3 7 56,611 4 8 63,535 2002 1 9 64,669 2 10 65,540 3 11 67,474 4 12 65,970 2003 1 13 70,608 2 14 76,025 3 15 67,458 4 16 77,056 2004 1 17 74,090 2 18 82,034 3 19 80,578 4 20 80,587 2005 1 21 76,716 2 22 77,128 3 23 87,585 4 24 87,588 2006 1 25 79,506 2 26 79,796 3 27 97,046 4 28 92,848
- 2. In your own words, please describe the difference between the regression equation ŷ = b + b₁x and the regression equation y = P + ₁x (Section 10-3).Discuss two extensions to the original GARCH (p,q) model and explain additional characteristics of financial data they might be able to capture.Suppose we obtain data on prices of big-screen televisions and estimate the following model: In(Price) = 4.06 + 0.06 * Size +0.23 * Wide + 0.34 * Plasma + 0.21 * LCD+0.09 * Memory where the dependent variable has been transformed, Size is the screen size measured in inches, Wide is a dummy variable equal to one if the television is a widescreen, Plasma is a dummy variable equal to one if the television is a Plasma screen, LCD is equal to one if the television is an LCD screen, and Memory is a dummy variable equal to one if the television has any memory card slots. What is the estimated price of a 42" Widescreen Plasma television with 2 memory card slots? Select one: O a. 7.24 O b. 7.33 1525.38 O d. 1394.09 • e 1881.83 Clear my choice
- The R-squared of a regression of an asset’s excess returns against a multi-factor asset pricing model indicates the proportion of the asset’s total volatility that is non-systematic. True False(b) Table 1 shows consumables usage data from a leading manufacturer. Forecast the demand for August, September and October using: i) a three-month moving average ii) a four-month weighted moving average using 50 percent of the actual usage for the most recent month, 20 percent of two months ago, 15 percent of three months ago and 15 percent of four months ago. Using an example, explain what are the benefits of using the weighted moving average method of forecasting instead? Month February 2023 March 2023 April 2023 May 2023 June 2023 July 2023 Consumables Used 884 892 972 990 956 880A study in the univeristy of massachusetts amherst published in the may 2007 journal of marriage and family found that married women do about one fewer hour of housework a week for every $7500 they earn as a full time workers outside of the home, regardless of their husband's income A) what would be the numerical value of the slope coefficient in the regression model that predicts womens housework hours from their income? what does the sign of the slope (positive or negative) tell us about the relationship between these variables B) Suppose Lynette's salary is $30,000 greater than Gabriells. what would you predict the difference in hours of housework they each do.
- 8. In a regression model if you drop one insignificant variable then A. R ^ 2 will decrease but SSE will increase. B. R ^ 2 will increase but SSE will decrease. C. R ^ 2 will increase and SSE also will increase. D. R ^ 2 will decrease and SSE also will decrease. E. NoneSquare Feet Sum of Bedrooms and Bathrooms Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 1,610 5 70 227,900 800,000 2,146 6 59 284,900 700,000 816 4 70 149,900 FO000 600,000 2,183 6.5 48 309,900 40000 1,046 5.5 64 134,900 500.000 20000 5,183 10.5 21 440,000 400,000 • 2 000 1,150 4 62 150,000 1,000 4,000 5.000 6,000 2000 0 300,000 1,068 70 154,900 4000 0 5,570 7 50 700,000 200,000 6000 0 2,449 6. 53 257,000 100,000 BO00 0 1,950 59 239,900 1000 00 2,630 7.5 73 349,900 Square Feet 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 2,732 7.5 20 339,900 1,908 5 46 289,000 3,666 6.5 17 399,900 Sum of Bedrooms and Bathrooms Residual Plot Sum of Bedrooms and Bathrooms Line Fit Plot 80000 1,878 7 19 290,000 800,000 2,172 62 278,000 60000 700,000 40000 600,000 SUMMARY OUTPUT 20000 500,000 400,000 Regression Statistics 12 2000 0 Multiple R 0.949366054 300,000 R Square 0.901295904 4000 0 200,000 Adjusted R Square 0.878518035 6000 0 100,000 Standard Error 47571.46177…
