Suppose we have the following causal model: Y a₁ + ₁ X + u, X + a₂ + B₂Y + v, where E(u) = E(v) = 0, cov(u, v) = 0. The best linear prediction of Y given X is: Y = a₁ + ₁X + error. Similarly, the best linear prediction of Y given X is: X = a + ₂Y + error. Which of the following statements is/are true?
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- (Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?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?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 OO
- 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).1. Suppose you observe (y, x₁,x2,...,k) for N observations. For each observation i, the model is yi Bo + B1x1i + B₂x2i + + BkXki + €į. Write the model out in matrix form. What are the dimensions of Y, X, B, €? =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?
- Consider the following model: yhat = 2.6+-0.9x² The prediction of y is yhat. What is the estimated marginal effect of x on y when x=2.7? PLZ MAKE SURE THIS IS RIGHT!!!Question 19 Lot's say you run a simulation and figure out that your sample bs one of the likely sampies that could have come from a DGP of 0. What would that mean? • A That is a good sign. Our model is good and probably explains a lot of variation. • B This is a bad sign for our model because a DGP of 0 means there is no relationship between the explanatory and outcome variable. • C This does not mean anything for our model. We have to run supernova to find out about how good our model is. • Dit depends on what our b Type Markdown and LaTeX: a Question 20 14 MacBook ProWith respect to a given product, describe the connection that exists between equalibrium/disequalibrium and prediction. cite an unique example
- In the linear model ,E (X*u) = a)X*u b) 0 c) u d) none of tha aboveEconometrics Thomas Eisensee and David Stromberg wanted to measure how much news coverage of a foreign disaster impacted the amount of disaster relief provided by the U.S. government. They argue that the simple relationship would be biased. Let X = Minutes of News Coverage and Y= Disaster Aid. Choose a variable X2 that could bias the simple relationship. This variable should impact the amount of coverage and impact the amount of aid for reasons other than purely news coverage. Eisensee and Stromberg introduce an instrument Z = During the Olympics. Explain how Z could satisfy the relevant and exogenous criteria. Explain how you could use Z to estimate the impact of X on Y free from X2 bias. Hint: you should mention two stages.Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?