Applied Econometrics - ECO00014H

« Back to module search

  • Department: Economics and Related Studies
  • Module co-ordinator: Prof. Takashi Yamagata
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2019-20

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2019-20 to Summer Term 2019-20

Module aims

To develop skills needed to apply econometric techniques in the following contexts: (i) the implementation of instrumental variable methods when regressors are endogenous; (ii) the use of binary choice models to model probabilities in applied economics; (iii) the estimation and interpretation of models designed for panel data; (iv) forecasting using stationary ARMA models and evaluating forecast performance; (v) the investigation of the time series properties of economic data and the implications of these properties for least squares analysis; and (vi) cointegration analyses when a single equation model is under scrutiny and the derivation of associated error correction schemes when variables are cointegrated.
To develop skills needed to interpret applied econometric results in the following contexts: (i) the analysis of regression models in the presence of omitted variables; (ii) the application of linear probability, logit and probit models; (iii) relationships estimated using linear panel data models; (iv) the outcomes of a battery of diagnostic checks after estimation; (v) testing for unit roots in economic variables by means of Dickey-Fuller tests; and (vi) empirical analyses based upon either the Granger-Engle two-step method or the Autoregressive Distributed Lag model.

Module learning outcomes

On completing the module a student will be able to:

  • Read and understand more of the econometric evidence published in academic journals and books. Understanding is extended beyond the second year Econometrics for Economists module by covering new topics such as: instrumental variable methods; binary choice models; and panel data (in which there are both cross-section and time series dimensions); forecasting using stationary dynamic ARMA models and evaluating forecast performance; nonstationary time series variables in regression; integration and cointegration (which are very important in modern applied macroeconomics).
  • Use standard econometric software (seminar work will involve the use of popular econometrics packages with various data sets that are provided via links on the VLE page)
  • Formulate economic hypotheses in testable ways and to understand which methods are appropriate for carrying out statistical tests

Assessment

Task Length % of module mark
Essay/coursework
Essay
N/A 15
Essay/coursework
Essay
N/A 15
University - closed examination
Applied Econometrics
3 hours 70

Special assessment rules

None

Additional assessment information

Re-assessment (see also below): the arrangements described below refer to students who resit without mitigating circumstances. For students who resit with mitigating circumstances, the arrangements are instead as follows:

2-hour closed exam in summer reassessment period, 70% weight

Empirical Project 1 with deadline extension (if mitigating circumstances cover the period during which the projects are required to be carried out), 15% weight

Empirical Project 2 with deadline extension (if mitigating circumstances cover the period during which the projects are required to be carried out), 15% weight

Reassessment

Task Length % of module mark
Essay/coursework
Essay
N/A 15
Essay/coursework
Essay
N/A 15
University - closed examination
Applied Econometrics
3 hours 70

Module feedback

The projects will be marked within six weeks of submission in accordance with university rules.

Indicative reading

Main References:

  • Wooldridge, J.M. (2009). Introductory Econometrics (4th edition), South-Western.
  • Enders, W. (2010). Applied Econometric Time Series (3rd ed). Wiley

Other important references include:

  • Greene, W.H. (2008). Econometric Analysis (6th edition), Prentice and Hall.
  • Gujarati, D.N. (2003). Basic Econometrics (4th edition), McGraw-Hill, New York.
  • Patterson, K. (2000). An Introduction to Applied Econometrics: A Time-Series Approach. Macmillan.
  • Stock, J H. & Watson, M W. (2006). Introduction to Econometrics. Pearson International ed.



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.