{"product_id":"analysis-of-financial-data-9780470013212","title":"Analysis of Financial Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAnalysis of Financial Data teaches basic methods and techniques of data analysis to finance students.   It covers many of the major tools used by the financial economist i.e. regression and time series methods including discussion of nonstationary models, multivariate concepts such as cointegration and models of conditional volatility.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOrganization of the book 3\u003c\/p\u003e \u003cp\u003eUseful background 4\u003c\/p\u003e \u003cp\u003eAppendix 1.1: Concepts in mathematics used in this book 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Basic data handling 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of financial data 9\u003c\/p\u003e \u003cp\u003eObtaining data 15\u003c\/p\u003e \u003cp\u003eWorking with data: graphical methods 16\u003c\/p\u003e \u003cp\u003eWorking with data: descriptive statistics 21\u003c\/p\u003e \u003cp\u003eExpected values and variances 24\u003c\/p\u003e \u003cp\u003eChapter summary 26\u003c\/p\u003e \u003cp\u003eAppendix 2.1: Index numbers 27\u003c\/p\u003e \u003cp\u003eAppendix 2.2: Advanced descriptive statistics 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Correlation 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding correlation 33\u003c\/p\u003e \u003cp\u003eUnderstanding why variables are correlated 39\u003c\/p\u003e \u003cp\u003eUnderstanding correlation through \u003ci\u003eXY\u003c\/i\u003e-plots 40\u003c\/p\u003e \u003cp\u003eCorrelation between several variables 44\u003c\/p\u003e \u003cp\u003eCovariances and population correlations 45\u003c\/p\u003e \u003cp\u003eChapter summary 47\u003c\/p\u003e \u003cp\u003eAppendix 3.1: Mathematical details 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 An introduction to simple regression 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRegression as a best fitting line 50\u003c\/p\u003e \u003cp\u003eInterpreting OLS estimates 53\u003c\/p\u003e \u003cp\u003eFitted values and \u003ci\u003eR\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e: measuring the fit of a regression model 55\u003c\/p\u003e \u003cp\u003eNonlinearity in regression 61\u003c\/p\u003e \u003cp\u003eChapter summary 64\u003c\/p\u003e \u003cp\u003eAppendix 4.1: Mathematical details 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Statistical aspects of regression 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhich factors affect the accuracy of the estimate \u003ci\u003eβ\u003c\/i\u003eˆ? 70\u003c\/p\u003e \u003cp\u003eCalculating a confidence interval for \u003ci\u003eβ\u003c\/i\u003e 73\u003c\/p\u003e \u003cp\u003eTesting whether \u003ci\u003eβ\u003c\/i\u003e =0 79\u003c\/p\u003e \u003cp\u003eHypothesis testing involving \u003ci\u003eR\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e: the \u003ci\u003eF\u003c\/i\u003e-statistic 84\u003c\/p\u003e \u003cp\u003eChapter summary 86\u003c\/p\u003e \u003cp\u003eAppendix 5.1: Using statistical tables for testing whether \u003ci\u003eβ\u003c\/i\u003e =0 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Multiple regression 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRegression as a best fitting line 93\u003c\/p\u003e \u003cp\u003eOrdinary least squares estimation of the multiple regression model 93\u003c\/p\u003e \u003cp\u003eStatistical aspects of multiple regression 94\u003c\/p\u003e \u003cp\u003eInterpreting OLS estimates 95\u003c\/p\u003e \u003cp\u003ePitfalls of using simple regression in a multiple regression context 98\u003c\/p\u003e \u003cp\u003eOmitted variables bias 100\u003c\/p\u003e \u003cp\u003eMulticollinearity 102\u003c\/p\u003e \u003cp\u003eChapter summary 105\u003c\/p\u003e \u003cp\u003eAppendix 6.1: Mathematical interpretation of regression coefficients 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Regression with dummy variables 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSimple regression with a dummy variable 112\u003c\/p\u003e \u003cp\u003eMultiple regression with dummy variables 114\u003c\/p\u003e \u003cp\u003eMultiple regression with both dummy and non-dummy explanatory variables 116\u003c\/p\u003e \u003cp\u003eInteracting dummy and non-dummy variables 120\u003c\/p\u003e \u003cp\u003eWhat if the dependent variable is a dummy? 121\u003c\/p\u003e \u003cp\u003eChapter summary 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Regression with lagged explanatory variables 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAside on lagged variables 125\u003c\/p\u003e \u003cp\u003eAside on notation 127\u003c\/p\u003e \u003cp\u003eSelection of lag order 132\u003c\/p\u003e \u003cp\u003eChapter summary 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Univariate time series analysis 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe autocorrelation function 140\u003c\/p\u003e \u003cp\u003eThe autoregressive model for univariate time series 144\u003c\/p\u003e \u003cp\u003eNonstationary versus stationary time series 146\u003c\/p\u003e \u003cp\u003eExtensions of the AR(1) model 149\u003c\/p\u003e \u003cp\u003eTesting in the AR( \u003ci\u003ep\u003c\/i\u003e) with deterministic trend model 152\u003c\/p\u003e \u003cp\u003eChapter summary 158\u003c\/p\u003e \u003cp\u003eAppendix 9.1: Mathematical intuition for the AR(1) model 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Regression with time series variables 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTime series regression when \u003ci\u003eX \u003c\/i\u003eand \u003ci\u003eY \u003c\/i\u003eare stationary 162\u003c\/p\u003e \u003cp\u003eTime series regression when \u003ci\u003eY \u003c\/i\u003eand \u003ci\u003eX \u003c\/i\u003ehave unit roots: spurious regression 167\u003c\/p\u003e \u003cp\u003eTime series regression when \u003ci\u003eY \u003c\/i\u003eand \u003ci\u003eX \u003c\/i\u003ehave unit roots: cointegration 167\u003c\/p\u003e \u003cp\u003eTime series regression when \u003ci\u003eY \u003c\/i\u003eand \u003ci\u003eX \u003c\/i\u003eare cointegrated: the error correction model 174\u003c\/p\u003e \u003cp\u003eTime series regression when \u003ci\u003eY \u003c\/i\u003eand \u003ci\u003eX \u003c\/i\u003ehave unit roots but are not cointegrated 177\u003c\/p\u003e \u003cp\u003eChapter summary 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Regression with time series variables with several equations 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGranger causality 184\u003c\/p\u003e \u003cp\u003eVector autoregressions 190\u003c\/p\u003e \u003cp\u003eChapter summary 203\u003c\/p\u003e \u003cp\u003eAppendix 11.1: Hypothesis tests involving more than one coefficient 204\u003c\/p\u003e \u003cp\u003eAppendix 11.2: Variance decompositions 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Financial volatility 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVolatility in asset prices: Introduction 212\u003c\/p\u003e \u003cp\u003eAutoregressive conditional heteroskedasticity (ARCH) 217\u003c\/p\u003e \u003cp\u003eChapter summary 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Writing an empirical project \u003c\/b\u003e223\u003c\/p\u003e \u003cp\u003eDescription of a typical empirical project 223\u003c\/p\u003e \u003cp\u003eGeneral considerations 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Data directory 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 231\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402253214039,"sku":"9780470013212","price":44.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470013212.jpg?v=1730479843","url":"https:\/\/bookcurl.com\/products\/analysis-of-financial-data-9780470013212","provider":"Book Curl","version":"1.0","type":"link"}