Cities often want to determine how much additional law enforcement will decrease their murder rates. A simple model with cross-section data at the provincial level to address this question is (1) murdpc=Bo+Bipolpc+Bzincpc+Bspvty+u where murdpc is murders per capita, polpc is number of police officers per capita, incpc is income per capita and pvty is the percent of people in the city that are below a poverty line and u is the error term. Suppose that the supply of polpc is a function of murdpc described by the following equation
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- Using Y as the dependent variable and X1, X2, X3, X4 and X5 as the explanatoryvariables, formulate an econometric model for data that is (i) time series data (ii)cross-sectional data and (iii) panel data – (Hint: please specify the specific model herenot its general form).Demand can be estimated with experimental data, time-series data, or cross-section data. In this case, cross-section data appear in the Excel file. Soft drink consumption in cans per capita per year is related to six-pack price, income per capita, and mean temperature across the 48 contiguous states in the United States. Questions Howmanycans/capita/yearonsoftdrinkshouldbeforastateinwhich6-packprice=$1.95,Income/Capita=$23,500, and Mean Temp= 68 F ? Now omit the price and temperature from the regression equation. Should a marketing plan for soft drinks be designed that relocates most canned drink machines into low-income neighborhoods ? Why or why not ? TABLE 1. SOFT DRINK DEMAND DATA State Cans/Capita/Yr 6-Pack Price ($) Income/Capita ($1,000) Mean Temp. (F) Alabama 200 2.19 11.7 66 Arizona 150 1.99 15.3 62 Arkansas 237 1.93 9.9 63 California 135 2.59 22.5 56 Colorado 121 2.29 17.1 52 Connecticut 118 2.49 24.3 50 Delaware 217 1.99 25.2 52 Florida 242…In 2017, Philadelphia launched a sweetened beverage tax of 1.5 cents per ounce, raising the cost of a 2-liter soda bottle from about $1.50 to $2.50. One year later, the Philadelphia mayor wants to evaluate if this "sugar tax" improves the health status of Philadelphia Propose ONE method (i.e. difference-in-difference, instrumental variables, or regression discontinuity) to address these questions. write down its implementation details (the type of data you need, potential sources to get the data, equations) its pros and cons Only Typing answer please I need ASAP
- (Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?I dont understand why my question is being rejected so i'll resend this one. The data is attached in the image. Please help me with these questions. Thank you in advanced!! a. How many observations are included in the data? Is the data balanced?b. Is the above result estimated from the fixed effects model or the random effects model?c. Explain the meaning of the estimate coefficient of the variable ???Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.
- 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: CO2Method: Least SquaresDate: 04/20/17 Time: 09:46Sample: 1990 2013Included observations: 24 Write down the economic function for the above estimation by using the information obtained from above table b- Write down the economic model for the above estimation by using the information obtained from above table . c- Write down the econometric model for the above estimation by using the information obtained from above table d- Write down the least squares line by using the information obtained from above tablee- Explain what each coefficient of the least squares line indicatesExplain what multicollinearity . What are the main problems that multicollinearity creates for OLS estimation results ? Give two ways to detect multicollinearity problem and the hypothesis that are tested ?The population of a town has been growing, following the equation P=800t+5500P=800t+5500, where t is years after 2010. The number of restaurants in the town has been growing according to the equation R=2t+40R=2t+40.Complete an equation for the number of restaurants per capita (per person)Restaurants per capita: How many restaurants per capita does this model predict for the year 2017?
- Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Do you think other relevant explanatory variables should also be included? Name any two such variables and explain why they should be included in the regression.The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?Problem 5. Based on a sample of 21 Monitoring the Future respondents (see table below), we present their racial/ethnic background and the number of school days missed in the past 4 weeks. White Black Hispanic 4 1 4 5 2 3 3 2 5 4 1 1 4 3 5 4 4 2 6 3 2 Complete the 5-step model for these data, and set alpha at 0.05. If alpha were set at 0.01, would your decision change? Explain.