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define the concept of stationarity and discuss why the stationarity assumption is essential for the estimation and use of models in the time series framework.

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Exercise 1 Describe the probit model. For what type of dependent variables is this model suitable? What are the distributive assumptions on the stochastic component of the model? What is the typical estimation principle adopted to estimate the parameters? Do the parameters have a direct interpretation in terms of marginal effects?

Exercise 2 Define the concept of stationarity and discuss why the stationarity assumption is essential for the estimation and use of models in the time series framework.

Exercise 3 Consider the following regression equation:

E C X D X D X D ln | , ln ln i i i i i i i           0 1 2 3  

where C is household's consumption, X is its income, both in thousands of euro, and D is a dummy variable equal to one if the head of the household is unemployed and 0 otherwise. The available sample has 750 independent observations and is representative of the entire population. The (OLS) estimated function is:

with estimated variance-covariance matrix:

1. Consider a household whose head is employed, provide an estimate of the percentage variation of its consumption when its income increases by 1%. Test if is such variation is significantly different from zero.

2. Do the estimation results support the following statement? “The consumption elasticity to income of the household whose head is unemployed is higher than the corresponding elasticity when the head of the household is employed”. Use a proper test to answer.

3. Compute the predicted value and its confidence interval of the logarithm of consumption for a household whose head is employed and its income is euro 1000.

4. Test the hypotheses that the predicted value at 3. is equal to ln(1.1) and that the consumption elasticity to income equals 1 when the head of the household is employed 

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