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Meet the Author
Chris Brooks is Professor of Finance at the ICMA Centre, University of Reading, UK, where he also obtained his PhD. He has published over 60 articles in leading academic and practitioner journals including the Journal of Business, the Journal of Banking and Finance, the Journal of Empirical Finance, the Review of Economics and Statistics and the Economic Journal. He is associate editor of a number of journals including the International Journal of Forecasting. He has also acted as consultant for various banks and professional bodies in the fields of finance, econometrics and real estate.
Table of Contents
List of figures viii
List of screenshots ix
Preface xi
1 Introduction 1
1.1 Description 1
1.2 RATSDATA 2
1.3 Accomplishing simple tasks in RATS 2
1.4 Further reading 2
1.5 Other sources of information and programs 3
1.6 Opening the software 3
1.7 Types of RATS files 5
1.8 Reading (loading) data in RATS 6
1.9 Reading in data on UK house prices 8
1.10 Mixing and matching frequencies and printing 11
1.11 Transformations 11
1.12 Computing summary statistics 12
1.13 Plots 14
1.14 Comment lines 17
1.15 Printing results 18
1.16 Saving the instructions and results 18
1.17 Econometric tools available in RATS 18
1.18 Outline of the remainder of this book 20
2 The classical linear regression model 22
2.1 Hedge ratio estimation using OLS 22
2.2 Standard errors and hypothesis testing 28
2.3 Estimation and hypothesis testing with the CAPM 30
3 Further development and analysis of the classical linear regression model 34
3.1 Conducting multiple hypothesis tests 34
3.2 Multiple regression using an APT-style model 36
3.3 Stepwise regression 39
3.4 Constructing reports 41
4 Diagnostic testing 43
4.1 Testing for heteroscedasticity 44
4.2 A digression on SMPL 51
4.3 Using White's modified standard error estimates 52
4.4 Autocorrelation and dynamic models 53
4.5 Testing for non-normality 57
4.6 Dummy variable construction and use 58
4.7 Testing for multicollinearity 62
4.8 The RESET test for functional form 63
4.9 Parameter stability tests 65
5 Formulating and estimating ARMA models 71
5.1 Getting started 72
5.2 Forecasting using ARMA models 79
5.3 Exponential smoothing models 83
6 Multivariate models 86
6.1 Setting upa system 86
6.2 A Hausman test 89
6.3 VAR estimation 92
6.4 Selecting the optimal lag length for a VAR 96
6.5 Impulse responses and variance decompositions 100
7 Modelling long-run relationships 106
7.1 Testing for unit roots 106
7.2 Testing for cointegration and modelling cointegrated variables 108
7.3 Using the systems-based approach to testing for cointegration 113
8 Modelling volatility and correlation 120
8.1 Estimating EWMA models 120
8.2 Testing for ARCH-effects 121
8.3 GARCH model estimation 123
8.4 Estimating GJR and EGARCH models 128
8.5 Tests for sign and size bias 132
8.6 The GARCH(1,1)-M model 135
8.7 Forecasting from GARCH models 137
8.8 Multivariate GARCH models 140
9 Switching models 145
9.1 Dummy variables for seasonality 145
9.2 Markov switching models 149
9.3 Threshold autoregressive models 153
10 Panel data 160
10.1 Setting up the panel 160
10.2 Estimating fixed or random effects panel models 163
11 Limited dependent variable models 168
11.1 Reading in the data 169
11.2 The logit and probit models 170
12 Simulation methods 175
12.1 Simulating Dickey-Fuller critical values 176
12.2 Pricing Asian options 179
12.3 Simulating the price of an option using a fat-tailed process 183
12.4 VAR estimation using bootstrapping 186
Appendix Sources of data in this book 194
References 195
Index 199