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E-raamat: RATS Handbook to Accompany Introductory Econometrics for Finance

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(City University London)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Nov-2008
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511451690
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RATS Handbook to Accompany Introductory Econometrics for FinanceSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Nov-2008
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511451690
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Written to complement the second edition of best-selling textbook Introductory Econometrics for Finance, this book provides a comprehensive introduction to the use of the Regression Analysis of Time Series (RATS) software for modelling in finance and beyond. It provides numerous worked examples with carefully annotated code and detailed explanations of the outputs, giving readers the knowledge and confidence to use the software for their own research and to interpret their own results. A wide variety of important modelling approaches are covered, including such topics as time-series analysis and forecasting, volatility modelling, limited dependent variable and panel methods, switching models and simulations methods. The book is supported by an accompanying website containing freely downloadable data and RATS instructions.

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An introduction to the use of the Regression Analysis of Time Series (RATS) software for modelling in finance and beyond.
List of figures
viii
List of screenshots
ix
Preface xi
Introduction
1(21)
Description
1(1)
Ratsdata
2(1)
Accomplishing simple tasks in RATS
2(1)
Further reading
2(1)
Other sources of information and programs
3(1)
Opening the software
3(2)
Types of RATS files
5(1)
Reading (loading) data in RATS
6(2)
Reading in data on UK house prices
8(3)
Mixing and matching frequencies and printing
11(1)
Transformations
11(1)
Computing summary statistics
12(2)
Plots
14(3)
Comment lines
17(1)
Printing results
18(1)
Saving the instructions and results
18(1)
Econometric tools available in RATS
18(2)
Outline of the remainder of this book
20(2)
The classical linear regression model
22(12)
Hedge ratio estimation using OLS
22(6)
Standard errors and hypothesis testing
28(2)
Estimation and hypothesis testing with the CAPM
30(4)
Further development and analysis of the classical linear regression model
34(9)
Conducting multiple hypothesis tests
34(2)
Multiple regression using an APT-style model
36(3)
Stepwise regression
39(2)
Constructing reports
41(2)
Diagnostic testing
43(28)
Testing for heteroscedasticity
44(7)
A digression on SMPL
51(1)
Using White's modified standard error estimates
52(1)
Autocorrelation and dynamic models
53(4)
Testing for non-normality
57(1)
Dummy variable construction and use
58(4)
Testing for multicollinearity
62(1)
The RESET test for functional form
63(2)
Parameter stability tests
65(6)
Formulating and estimating ARMA models
71(15)
Getting started
72(7)
Forecasting using ARMA models
79(4)
Exponential smoothing models
83(3)
Multivariate models
86(20)
Setting up a system
86(3)
A Hausman test
89(3)
VAR estimation
92(4)
Selecting the optimal lag length for a VAR
96(4)
Impulse responses and variance decompositions
100(6)
Modelling long-run relationships
106(14)
Testing for unit roots
106(2)
Testing for cointegration and modelling cointegrated variables
108(5)
Using the systems-based approach to testing for cointegration
113(7)
Modelling volatility and correlation
120(25)
Estimating EWMA models
120(1)
Testing for ARCH-effects
121(2)
GARCH model estimation
123(5)
Estimating GJR and EGARCH models
128(4)
Tests for sign and size bias
132(3)
The GARCH(1,1)-M model
135(2)
Forecasting from GARCH models
137(3)
Multivariate GARCH models
140(5)
Switching models
145(15)
Dummy variables for seasonality
145(4)
Markov switching models
149(4)
Threshold autoregressive models
153(7)
Panel data
160(8)
Setting up the panel
160(3)
Estimating fixed or random effects panel models
163(5)
Limited dependent variable models
168(7)
Reading in the data
169(1)
The logit and probit models
170(5)
Simulation methods
175(19)
Simulating Dickey-Fuller critical values
176(3)
Pricing Asian options
179(4)
Simulating the price of an option using a fat-tailed process
183(3)
VAR estimation using bootstrapping
186(8)
Appendix: sources of data in this book 194(1)
References 195(4)
Index 199
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.
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