Econometrics II
STYLIANOS ARVANITIS
The course aims to the introduction of elements of the probabilistic theory of stochastic processes and time series models, with a view towards statistical and econometric analysis. In this respect, various models of stationary and non-stationary time series along with relevant inferential procedures will be examined.
Hence several already studied notions from probability theory and statistical inference are considered as prerequisites. Those include the concepts of probability space, probability measure, random element, expectation, modes of convergence of sequences of random elements, relevant LLNs and CLTs, the theory of M-estimation (with their studied examples of the OLSE, the MLE, the GMME) and of the relevant statistical tests, their basic limit theory.
Numerical implementations will also be examined during the tutorials. Hence familiarity with the Matlab programming environment is also prerequisite.
The course aims to the introduction of elements of the probabilistic theory of stochastic processes and time series models, with a view towards statistical and econometric analysis. In this respect, various models of stationary and non-stationary time series along with relevant inferential procedures will be examined.
Hence several already studied notions from probability theory and statistical inference are considered as prerequisites. Those include the concepts of probability space, probability measure, random element, expectation, modes of convergence of sequences of random elements, relevant LLNs and CLTs, the theory of M-estimation (with their studied examples of the OLSE, the MLE, the GMME) and of the relevant statistical tests, their basic limit theory.
Numerical implementations will also be examined during the tutorials. Hence familiarity with the Matlab programming environment is also prerequisite.
The course aims to the introduction of elements of the probabilistic theory of stochastic processes and time series models, with a view towards statistical and econometric analysis. In this respect, various models of stationary and non-stationary time series along with relevant inferential procedures will be examined.
Hence several already studied notions from probability theory and statistical inference are considered as prerequisites. Those include the concepts of probability space, probability measure, random element, expectation, modes of convergence of sequences of random elements, relevant LLNs and CLTs, the theory of M-estimation (with their studied examples of the OLSE, the MLE, the GMME) and of the relevant statistical tests, their basic limit theory.
Numerical implementations will also be examined during the tutorials. Hence familiarity with the Matlab programming environment is also prerequisite.
