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Econometrics lecture courses

This page serves as a course guide to the youtube lectures series which are on offer. There are further materials which allow for practical and theoretical practice of the concepts learned in class. In particular I offer problem sets (with worked answers) in *Gretl* – a free statistical software program.

I am currently offering three courses in econometrics:

Click here for course practice problems |

The first course is intented to be an introductory level lecture series, and assumes no prior knowledge of econometrics. It will cover the typical topics which are encountered in undergraduate courses in econometrics; the Gauss-Markov assumptions, diagnostic tests of regression specifications, time series, panel models and a short introduction to maximum likelihood estimators. See the video below for a short introduction to a 'full course in econometrics – undergraduate level', or visit the course page for this lecture series. Alternatively, visit the problem sets page to access course materials which allow the student to practice the concepts learned in class.

Another course which I am offering is a, 'graduate course in econometrics', which introduces the viewer to more advanced topics in econometric theory. This course assumes some of the knowledge from the introductory level course, although I will attempt to keep this assumed knowledge to a minimum. The course does however assume that individuals taking the course have a solid grasp of the following: linear algebra, probability distributions of continuous random variables. In time I also intend to post problem sets to accompany this course to the course page.

The final course which I am currently offering is called, 'The asymptotic behaviour of estimators'. This series is concerned with understanding how estimators introduced in the other two courses behave in the limit that the sample size approaches the population size. In the other two courses we will often state the behavious of estimators as given, but here we will derive the asymptotic properties of estimators. This is of utmost importance since the majority of statistical tests rely on these aymptotic properties for inference. In this course we shall cover the following topics: the weak law of large numbers, the strong law of large numbers, the central limit theorem, characteristic functions, moment generating functions, OLS estimator aymptotic distributions, GLS estimator asymptotic distributions, the premise for constructing statistical tests, IV estimators, ML estimators, and GMM amongst other subjects. In time I shall add problem sets to accompany this lecture series. For updates about the course visit the course page. Also see the introductory video for this series below.

Another course which I am offering is a, 'graduate course in econometrics', which introduces the viewer to more advanced topics in econometric theory. This course assumes some of the knowledge from the introductory level course, although I will attempt to keep this assumed knowledge to a minimum. The course does however assume that individuals taking the course have a solid grasp of the following: linear algebra, probability distributions of continuous random variables. In time I also intend to post problem sets to accompany this course to the course page.

The final course which I am currently offering is called, 'The asymptotic behaviour of estimators'. This series is concerned with understanding how estimators introduced in the other two courses behave in the limit that the sample size approaches the population size. In the other two courses we will often state the behavious of estimators as given, but here we will derive the asymptotic properties of estimators. This is of utmost importance since the majority of statistical tests rely on these aymptotic properties for inference. In this course we shall cover the following topics: the weak law of large numbers, the strong law of large numbers, the central limit theorem, characteristic functions, moment generating functions, OLS estimator aymptotic distributions, GLS estimator asymptotic distributions, the premise for constructing statistical tests, IV estimators, ML estimators, and GMM amongst other subjects. In time I shall add problem sets to accompany this lecture series. For updates about the course visit the course page. Also see the introductory video for this series below.