Econometric Theory

As Taught Autumn Semester 2011



Professor S Leybourne, School of Economics, University of Nottingham

Module Title: Econometric Theory

Module Code: L14003

Total Credits: 15

Level of Study: Post Graduate

Offering School: School of Economics

Frequency of Class: 1 x 2 hour lectures per week, 1 x 1 hour tutorial per week

Targeted Students: MSc students in the School of Economics.

The content presented here provides information for prospective students on module L14003 – ‘Econometric Theory’, offered by the School of Economics, University of Nottingham. The module convenor is Professor S Leybourne.

This module generalises and builds upon the econometric techniques applied to the multivariate regression models covered in undergraduate econometric courses, by introducing a number of new statistical and econometric concepts. Following a review of the classical linear regression model, students will cover alternative estimation methods (Maximum Likelihood estimation), some basic elements of asymptotic theory (the weak law of large numbers and the Central Limit Theorem), and the consistency and asymptotic normality of OLS. In the second part of the module, attention will switch to introductory econometric methods applicable to economic time series - for example, monthly or quarterly economic data. In particular, the model building approach for a single time series, based on the methodology of fitting and estimating ARMA models, will be introduced, and its application to forecasting discussed.

The following topics will be covered in the module:

Classical Linear Regression Model
Classical assumptions of the model; Ordinary least squares (OLS) estimation; Gauss-Markov Theorem; Hypothesis testing, Maximum likelihood (ML) estimation; Restricted OLS estimation.

Asymptotic Theory
Modes of convergence; The weak law of large numbers; The central limit theorem; Consistency and asymptotic normality of OLS.

Autogressive Moving Average (ARMA) Modelling.
Stochastic processes; AR, MA and ARMA models; Stationarity and Invertibility; Autocorrelation functions; Yule-Walker equations; Nonstationary processes.

Identification and Estimation of ARMA Models.
Sample and partial autocorrelation functions; Least squares estimation; prediction error decomposition; Conditional likelihood function; Maximum likelihood estimation.

Assessing Model Fit.
Tests for randomness of residuals; Information criteria.

Optimal forecasts; Forecast functions.