<p> <br/></p><p>Preface ix<br/>Chapter 1 Introduction 1<br/>Organization of the book 3<br/>Useful background 4<br/>Appendix 1.1: Concepts in mathematics used in this book 4<br/>Chapter 2 Basic data handling 9<br/>Types of financial data 9<br/>Obtaining data 15<br/>Working with data: graphical methods 16<br/>Working with data: descriptive statistics 21<br/>Expected values and variances 24<br/>Chapter summary 26<br/>Appendix 2.1: Index numbers 27<br/>Appendix 2.2: Advanced descriptive statistics 30<br/>Chapter 3 Correlation 33<br/>Understanding correlation 33<br/>Understanding why variables are correlated 39<br/>Understanding correlation through XY-plots 40<br/>Correlation between several variables 44<br/>Covariances and population correlations 45<br/>Chapter summary 47<br/>Appendix 3.1: Mathematical details 47Chapter 4 An introduction to simple regression 49<br/>Regression as a best fitting line 50<br/>Interpreting OLS estimates 53<br/>Fitted values and R2: measuring the fit of a regression model 55<br/>Nonlinearity in regression 61<br/>Chapter summary 64<br/>Appendix 4.1: Mathematical details 65<br/>Chapter 5 Statistical aspects of regression 69<br/>Which factors affect the accuracy of the estimate bˆ? 70<br/>Calculating a confidence interval for b 73<br/>Testing whether b =0 79<br/>Hypothesis testing involving R2: the F-statistic 84<br/>Chapter summary 86<br/>Appendix 5.1: Using statistical tables for testing whether<br/>b =0 87<br/>Chapter 6 Multiple regression 91<br/>Regression as a best fitting line 93<br/>Ordinary least squares estimation of the multiple<br/>regression model 93<br/>Statistical aspects of multiple regression 94<br/>Interpreting OLS estimates 95<br/>Pitfalls of using simple regression in a multiple<br/>regression context 98<br/>Omitted variables bias 100<br/>Multicollinearity 102<br/>Chapter summary 105<br/>Appendix 6.1: Mathematical interpretation of<br/>regression coefficients 105<br/>Chapter 7 Regression with dummy variables 109<br/>Simple regression with a dummy variable 112<br/>Multiple regression with dummy variables 114<br/>Multiple regression with both dummy and non-dummy<br/>explanatory variables 116<br/>Interacting dummy and non-dummy variables 120<br/>What if the dependent variable is a dummy? 121<br/>Chapter summary 122<br/>Chapter 8 Regression with lagged explanatory variables 123<br/>Aside on lagged variables 125<br/>Aside on notation 127Selection of lag order 132<br/>Chapter summary 135<br/>Chapter 9 Univariate time series analysis 137<br/>The autocorrelation function 140<br/>The autoregressive model for univariate time series 144<br/>Nonstationary versus stationary time series 146<br/>Extensions of the AR(1) model 149<br/>Testing in the AR( p) with deterministic trend model 152<br/>Chapter summary 158<br/>Appendix 9.1: Mathematical intuition for the AR(1) model 159<br/>Chapter 10 Regression with time series variables 161<br/>Time series regression when X and Y are stationary 162<br/>Time series regression when Y and X have unit roots:<br/>spurious regression 167<br/>Time series regression when Y and X have unit roots:<br/>cointegration 167<br/>Time series regression when Y and X are cointegrated:<br/>the error correction model 174<br/>Time series regression when Y and X have unit roots<br/>but are not cointegrated 177<br/>Chapter summary 179<br/>Chapter 11 Regression with time series variables with<br/>several equations 183<br/>Granger causality 184<br/>Vector autoregressions 190<br/>Chapter summary 203<br/>Appendix 11.1: Hypothesis tests involving more than<br/>one coefficient 204<br/>Appendix 11.2: Variance decompositions 207<br/>Chapter 12 Financial volatility 211<br/>Volatility in asset prices: Introduction 212<br/>Autoregressive conditional heteroskedasticity (ARCH) 217<br/>Chapter summary 222<br/>Appendix A Writing an empirical project 223<br/>Description of a typical empirical project 223<br/>General considerations 225<br/>Appendix B Data directory 227<br/>Index 231</p>