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Final exam EBA 35302

创建日期：2024-05-05 14:58:00

所属分类：数学mathematics

标签： EBA 35302

Time series data

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Final exam EBA 35302 autumn

The exam contains 6 main blocks and there is a total of 100 points available. Given the current Covid-19
situation and exam format, the assignment deliberately contains many questions. Do your best to answer
as many of them as possible. Write short and precise answers rather than long answers. You do
not need to provide a reference list.
The answer paper must be written and prepared individually. Collaboration with others is not per-
mitted and is considered cheating. All answer papers are automatically subject to plagiarism control.
Students may also be called in for an oral consultation as additional verification of an answer paper.
Good luck!
1. (10 points) True or false
Read the statements below. Only answer whether they are true or false
(a) (1 points) Using cross-validation routines is always better than relying on simple information
criteria such as BIC or AIC.
(b) (1 points) The p-value reflects the probability that a parameter is zero.
(c) (1 points) More data observations will by construction make the standard errors of the esti-
mated parameters smaller.
(d) (1 points) The Ridge estimator incorporates regularization, but does not allow for variable
selection.
(e) (1 points) You can use the OLS estimator for binary outcome data, but it is generally better
to use the LASSO.
(f) (1 points) The RMSE is typically used as a measure for out-of-sample forecasting accuracy.
(g) (1 points) The LASSO estimator is the best linear unbiased estimator you can use.
(h) (1 points) When you do not know the true data generating process you can construct confidence
intervals around your point estimates using Bootstrapping routines.
(i) (1 points) When the penalization weight is infinitely large the LASSO estimator is identical
to the OLS estimator.
(j) (1 points) The scale of the variables you use in a OLS regression do affect the parameter
estimates, but not the significance level.
2. (20 points) Scoring rules and uncertainty
(a) (5 points) Make a drawing with explanations illustrating how to perform out-of-sample fore-
casting evaluation with time series data. Give at least two reasons for why using out-of-sample
validation might be a good idea
(b) (3 points) 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?
(c) (3 points) 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?
(d) (3 points) 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.
1
(e) (3 points) 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?
(f) (3 points) 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

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