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Give examples of applications of the Maximum Likelihood and the Generalized Method of Moments in Economics
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- Suppose that the following binary dependent models. The model is based on the driving test of the 400 randomly selected driver’s license applicants. Y=1 if passed the test, or 0 otherwise, and X1 is years of experiences, X2 is the years of educations. Logit: P(Y=1/X) = F(0.563+ 0.040X1+ 0.057X2) What are the probabilities of passing the test for a person with 10 years of experiences and 10 years of educations in each model?Hello. May I ask how did you get the values? Were those only assumed values? Can you also explain how did you make the normal form thoroughly?Suppose that your current happiness is based on your current and past saving levels. Let s refers to the saving that you make each year. And please denote the current time period by t and it goes back to n years before. Define the equation for estimation happiness level. 2.
- Which of the following variables is considered random or probabilistic? O Micky Mantle's lifetime batting average O last week's sales data O future interest rates O historical stock pricesThe population of a country can be divided into the employed, the unemployed, and the persons who are out of the labor force (OLF). In any given month, the transition probabilities among the various categories are given by the following table. Moving into Employed Unemployed OLF 0.97 Moving from: Employed 0.02 0.01 Unemployed 0.35 OLF 0.60 0.05 0.02 0.04 0.94 The transition probabilities are interpreted as follows. In any given month, 2% of the employed become unemployed, 35% of the unemployed find jobs, and so on. (a) What is the steady state employment rate? (b) What is the steady state unemployment rate? (c) What is the steady state labor force participation rate?Exlplain Linear Conditionally Unbiased Estimators and the Gauss–Markov Theorem with its limitations?
- This is model question. I need100% accurate answer. Please review it.8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OOSuppose a country has 100 million inhabitants. The population can be divided into the employed, the unemployed, and the persons who are out of the labor force (OLF). In any given year, the transition probabilities among the various categories are given by Moving into: Employed Unemployed OLF Moving from: Employed Unemployed OLF 0.94 0.20 0.05 0.02 0.65 0.03 0.04 0.15 0.92 These transition probabilities are interpreted as follows. In any given year, 2 percent of the workers who are employed become unemployed; 20 percent of the workers who are unemployed find jobs, and so on. What will be the steady-state unemployment rate?
- Although 23 states barred the self-service sale of gasoline in 1968, most removed the bans by the mid-1970s. By 1992, self-service outlets sold nearly 80% of all US gas, and only New Jersey and Oregon continued to ban self- service sales. Using predictive value for self-service sales for New Jersey and Oregon Johnson and Romeo (2000) estimate that the ban in those two states raised the price by approximately $.03-$.05 per gallon. Why did the ban effect the price? Illustrate using a figure and explain. Show the welfare effects in your figure. Use a table to show who gains or loses.A law school is trying to gain a better understanding of the determinants of bar passage rates. Suppose that they the following facts were uncovered: 1. The probability with which a randomly selected graduate has received an A in Economic Foundations of Legal Studies (EFLS) is 0.3;2. The probability with which a randomly selected graduate has passed the bar exam is 0.81;3. The probability with which a randomly selected graduate has received an A in EFLS and has passed the bar exam is 0.27. Which of the following statements is true regarding the events X and Y, defined as follows: X = “a randomly selected graduate having received an A in EFLS”; andY = “a randomly selected student having passed the bar exam”? A) X and Y are independent events, and this can be verified by noting that P(X|Y) = 0.3.B) X and Y are not independent events, and this can be verified by noting that P(X|Y) = 0.3.C) X and Y are not independent events, and this can be verified by noting that P(X|Y) =.333.D) X and Y…Explain why in every econometric model there must be an error term, explain its function.