1. Make a drawing with explanations illustrating how to perform out-of-sample forecasting evaluation with time series data. Give at least two reasons for why using out-of-sample validation might be a good idea

2, You are working with binary outcome variables and have used the ROC curve to evaluate your model. Explain the elements of the confusion matrix, and illustrate how an optimal ROC curve would look like. What would the ROC curve look like if the predicted outcomes were purely random?

3, A high R2 , estimated in-sample, might not lead to good out-of-sample performance. But, does a model that predicts well out-of-sample have a good in-sample fit? Why/Why not?

4, Exemplify, with equations, the estimators for the out-of-sample bias and Root Mean Squared Forecast Error. Explain clearly what the elements in the equations are. Explain, in words, what these scoring rules actually measure.

5, Explain the bias-variance trade-off. If you want to estimate the elasticity of demand for a product you are selling, would you be more concerned about biased estimates, or of having a high variance?

6, What is the purpose of Monte Carlo algorithms? Explain, in generic terms, how you would perform a Monte Carlo experiment based on a linear regression model.