EC50162 FINANCIAL ECONOMETRICS
Lecturer
Dr Bruce Morley
Room: 3E 4.30, Ext: (38) 6497, Email:
B.Morley@bath.ac.uk
Aims and Objectives of the course
The aim of this course is to provide students with the knowledge necessary to handle
modern time series and panel data techniques. Both univariate and multivariate
models are considered with and without the stationary assumption. The goals of the
course are threefold: (1) develop a comprehensive set of tools and techniques for
analysing various forms of univariate and multivariate time series and for
understanding the current literature in applied time series econometrics in the areas of
Accounting and Finance; (2) survey the current research topics in time series
econometrics in the areas of Accounting and Finance; (3) demonstrate how to use
econometric software EVIEWS for problems in time series econometrics. On this
course I will always attempt to provide simple examples that illustrate how the
theoretical results are used and applied in practice.
Organisation of the course
The theoretical material, methodology and practical examples is to be covered in 22
lectures. Additionally there will be 6 computer practicals with N. Hashimzade and B.
Morley, where students will apply the econometric theory using the econometric
software EVIEWS and a further two classes of class based tutorials.
Assessment
2 Hour exam (70%) Coursework (30%)
Submission deadline for coursework is Noon on Friday 2nd May.
Textbooks
The main text for the course is:
C.Brooks, Introductory Econometrics for Finance, Cambridge (2002).
Other useful text books include:
K.Cuthbertson, Quantitative Financial Economics, Wiley, 1996.
D. Gujarati, Basic Econometrics, 4th Edition, McGraw-Hill, 2002
J. Johnston and J. DiNardo, Econometric Methods, 4th Edition, 1997.
W. Enders, Applied Econometrics Time Series, John Wiley, 2004.
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and Forecasting
Wiley.
Dougherty, C. Introduction to Econometrics, (2007), Oxford.
J. Wooldridge, Econometric Analysis of Cross Section and Panel data. (2002). MIT
Press, Cambridge.
Dougherty and Gujarati are good general textbooks but do not cover the topics in
depth. However both cover panel data (only Dougherty’s 3rd edition), which Brooks
does not, although the Wooldridge book does cover Panel data in some depth.
Johnston and DiNardo is the latest edition of a “standard” in the subject, but is much
more detailed and takes a matrix algebra approach. Enders is a more advanced
textbook specifically concerned with time series econometrics.
In addition to textbook resources you should make sure that you are familiar with the
use of basic statistical tables. The Brooks, Dougherty, Gujarati and the Johnston
textbooks contain the essential tables at the back.
Content of the course
Lecture 1. Panel data Estimation
-
Reasons for using panel data
Fixed and random effects models
Hausmann Tests
Reading:
1 Gujarati Basic Econometrics Chapter 16
2 Dougherty Introduction to Econometrics Chapter 14
3 Wooldridge Econometric Analysis of Cross Section and panel data. Chapter
10 and 11.
4 Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 7.
5 Fung, K.C, Iiazaka, H. and S. Parker, (2002), ‘Determinants of U.S. and
Japanese Direct Investment in China’, Journal of Comparative Economics, pp.
567-578.
Lecture 2 Stationary Univariate Models, ARIMA modelling and Forecasting
Techniques
 Difference and Stationary Equations
 Autocorrelation Coefficients
 ARIMA models and Box-Jenkins Methodology,
 Model Selection
 Out-of-sample forecasting techniques
 Measures of forecast performance
Reading:
1
Enders, W. (1995) Applied Econometric Time Series. Wiley. Chapters 1 & 2.
2
Philip Hans Franses (1998) Time Series Models for Business & Economic
Forecasting Cambridge University Press. Chapter 3.
3
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 5
4
Gujarati, D. Basic Econometrics, chapter 22.
Gil-Alana, L.A. (2001), ‘The persistence of unemployment in the USA and
Europe in terms of fractionally ARIMA models’. Applied economics. 33, pp.
1263-1269.
Leitch, G. and J. Tanner, (1991), ‘Economic forecast evaluation: profit versus
the conventional error measures’, American Economic Review, 81, pp. 580590.
McNees, S. (1986), ‘Forecasting accuracy of alternative techniques: A
comparison of US Macroeconomic forecasts’, Journal of Business and
Economic Statistics’, 4, pp. 5-15.
5
6
7
Lecture 3 Non-stationary Univariate Models
 Deterministic and Stochastic Trend Models
 Unit Root Tests
 Engle Granger approach to Cointegration
Reading:
1
Gujarati, D. Basic Econometrics, Chapter 21.
2
Enders, W. (1995) Applied Econometric Time Series. Wiley. Chapter 4.
3
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 3.
4
Johnston, J. and J. DiNardo (1997) Econometric Methods 4th Edition, McGraw
Hill International Editions. Chapter 7.
5
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 7
6
Engle, R. and Granger, C. (1987) “Co-integration and Error Correction:
Representation, Estimation and Testing, Econometrica, 55, pp. 251-76.
6
Perron P.P. (1988) "Trends and Random Walks in Macroeconomic Time
Series: Further Evidence from a New Approach" Journal of Economic
Dynamics and Control. (12) pp 297 - 332.
Lecture 4 An Introduction to Panel Data Models, Dynamic Panels and Panel
Unit Root Tests.



Arellano-Bond Dynamic Panel Models
Panel Unit Root tests
Panel Cointegration
Reading:
1
2
3
4
5
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 7.
Enders, W. (1995) Applied Econometric Time Series. Wiley. Pp. 225-228.
Bond, S. (2002) “Dynamic panel data models: a guide to micro data methods
and practice”, Portuguese Journal of Economics 1, pp. 141-163.
Cheung, L. and Y. Kwan. (2000), “What are the determinants of the location
of foreign direct investment: The Chinese experience”, 51, pp. 379-400.
Beck, T and R. Levine (2004), “Stock Markets, Banks and Growth: Panel
Evidence” Journal of banking and Finance, 28, pp. 423-442.
Lecture 5 Simultaneous Equation and Vector Autoregression Models
-
The problem of Simultaneity
Two stage Least Squares
Vector Autoregression (VAR) Models
Reading:
1
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 6
2
Dougherty, C. (2006) Introduction to Econometrics, Oxford, Chapter 9.
3
Gujarati, D. Basic Econometrics, Chapters 18,19,20.
4
Sims, C.A. (1980), "Macroeconomics and Reality" Econometrica, 48, pp 1 48.
Lecture 6 Granger Causality and Impulse Response Functions
-
VARs and Granger Causality
Impulse Response Functions
Lag selection criteria
Reading:
1
Gujarati, D. Basic Econometrics, Chapters 18,19,20.
2
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 6
3
Friedman, B. and K. Kuttner (1992) “Money, Income, Prices and Interest
Rates” American Economic Review (82) pp 472 – 492.
Lecture 7 Cointegrating VARs
- Johansen’s Maximum Likelihood Methodology
- Vector Error Correction Models
- Multiple Cointegrating Vectors
1
2
3
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 7
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 5.
Ericsson, N.R., D.F. Hendry, G.E. Mizon (1998) "Exogeneity, Cointegration
and Economic Policy Analysis" Journal of Business and Economic Statistics.
(16) No.4, pp 370 - 387.
Lecture 8 Maximum Likelihood Estimation and Modelling Volatility
- Maximum Likelihood
- Volatility Evaluation
- Introduction to Autoregressive Conditional Heteroskedasticity (ARCH)
.
1
Dougherty, C. (2006) Introduction to Econometrics, Oxford, Chapter 10 pp.
312-21
2
3
4
5
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 8
Enders, W. (1995) Applied Econometric Time Series. Wiley. Chapter 7.
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 8.
Engle, R.F. (1982) Autoregressive Conditional Heteroskedasticity with
Estimates of the Variance of United Kingdom Inflation, Econometrica 50,
pp.987-1007
Lecture 9 Autoregressive Conditional Heteroskedastistic Models (ARCH)




ARCH and GARCH Models
TGARCH and Exponential GARCH Models
GARCH in mean models
Volatility Forecasting
Reading:
1.
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 8
2.
Enders, W. (1995) Applied Econometric Time Series. Wiley. Chapter 7.
3
Harris, R.I.D and R. Sollis (2003) Applied Time Series Modelling and
Forecasting Wiley. Chapter 8.
4
Chu, S-H and Freund, S. (1996) Volatility Estimation for Stock Index
Options: A GARCH Approach, Quarterly Review of Economics and Finance,
36, pp. 431-50
6
Engle, R.F., Lilien, D.M. and Robins, R.P. (1987) Estimating Time Varying
Risk Premia in the Term Structure: The ARCH-M Model, Economtrica, 55(2)
pp. 391-407
Lecture 10 Introduction to Nonlinear Models (only if time permits)



Types of Non-Linear Model
Testing for Non-Linearity
Threshold Autoregressive (TAR) Models
Reading:
1
Brooks, C. (2002) Introductory Econometrics for Finance, 1st Edition,
Cambridge. Chapter 8, pp.437-440
2
Enders, W. and C.W.J. Granger (1998) "Unit Root Tests and Asymmetric
Adjustment With an Example Using the Term Structure of Interest Rates"
Journal of Business and Economic Statistics (16) 3 pp 304 – 311.
3
Philip Hans Franses (1998) Time Series Models for Business & Economic
Forecasting Cambridge University Press.
Lecture 11 Revision