📚Important concepts

It is generally difficult to define a complete profile in terms of background knowledge required for this minor. The teachers in this program have collected a list of topics in statistics, which they identify as crucial for a successful start into the first courses:

• Random variables and realizations

• Probability distribution functions (PDF) and cumulative distribution functions (CDF)

• Moments: mean, variance, skewness, kurtosis

• Quantiles of a distribution

• Covariance, correlation and dependence

• Multivariate random variables (covariance and correlation matrix)

• Conditioning: conditional distributions, conditional mean, variance and covariance

• Law of total expectation

• Estimation: ordinary least squares, maximum likelihood, method of moments

• Inference: hypothesis testing, p-values, confidence intervals

• Asymptotics: convergence in probability, convergence in distribution, law of large numbers,

  central limit theorem, asymptotic normality

It is highly advisable that you are familiar with (most of) them as background knowledge. The following pages explain intuitively why these concepts are important to get a smooth start into your first econometrics courses. For a formal treatment of the concepts, you can find appropriate references in the Literature section.

You might also want to visit the website of dr. K. Moussa, who is teaching in the minor programme. He has created notes on programming, probability theory and statistics that are tailor-made for the courses in period 1 of the minor.