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• Half-title
• Title page
• Copyright information
• Contents in Brief
• Detailed Contents
• Figures
• Tables
• Boxes
• Screenshots
• Preface to the Fourth Edition
• Acknowledgements
• Outline of the Remainder of this Book
• 1 Introduction and Mathematical Foundations
• 1.1 What is Econometrics?
• 1.2 Is Financial Econometrics Different from ‘Economic Econometrics’?
• 1.3 Steps Involved in Formulating an Econometric Model
• 1.4 Points to Consider When Reading Articles in Empirical Finance
• 1.5 Functions
• 1.5.1 Introduction to Functions
• 1.5.2 Straight Lines
• 1.5.3 Polynomial Functions
• 1.5.4 Powers of Numbers or of Variables
• 1.5.5 The Exponential Function
• 1.5.6 Logarithms
• 1.5.7 Inverse Functions
• 1.5.8 Sigma Notation
• 1.5.9 Pi Notation
• 1.5.10 Functions of More than one Variable
• 1.6 Differential Calculus
• 1.6.1 Differentiation: the Fundamentals
• 1.6.2 Derivatives of Products and Quotients
• 1.6.3 Higher Order Derivatives
• 1.6.4 Differentiation of Functions of Functions Using the Chain Rule
• 1.6.5 Partial Differentiation
• 1.6.6 Functions that Cannot be Differentiated
• 1.6.7 Derivatives in Use in Finance
• 1.6.8 Integration
• 1.7 Matrices
• 1.7.1 Operations with Matrices
• 1.7.2 The Rank of a Matrix
• 1.7.3 The Inverse of a Matrix
• 1.7.4 The Trace of a Matrix
• 1.7.5 The Eigenvalues of a Matrix
• 2 Statistical Foundations and Dealing with Data
• 2.1 Probability and Probability Distributions
• 2.1.1 The Central Limit Theorem
• 2.1.2 Other Statistical Distributions
• 2.2 A Note on Bayesian versus Classical Statistics
• 2.3 Descriptive Statistics
• 2.3.1 Measures of Central Tendency
• 2.3.2 Measures of Spread
• 2.3.3 Higher Moments
• 2.3.4 Measures of Association
• 2.3.5 An Example of How to Calculate Summary Statistics
• 2.3.6 Useful Algebra for Means, Variances and Covariances
• 2.4 Types of Data and Data Aggregation
• 2.4.1 Time-Series Data
• 2.4.2 Cross-Sectional Data
• 2.4.3 Panel Data
• 2.4.4 Continuous and Discrete Data
• 2.4.5 Cardinal, Ordinal and Nominal Numbers
• 2.5 Arithmetic and Geometric Series
• 2.6 Future Values and Present Values
• 2.6.1 Future Values
• 2.6.2 Present Value
• 2.6.3 Internal Rate of Return
• 2.7 Returns in Financial Modelling
• 2.7.1 Real versus Nominal Series and Deflating Nominal Series
• 2.8 Portfolio Theory Using Matrix Algebra
• 2.8.1 The Mean–Variance Efficient Frontier in Excel
• 3 A Brief Overview of the Classical Linear Regression Model
• 3.1 What is a Regression Model?
• 3.2 Regression versus Correlation
• 3.3 Simple Regression
• 3.3.1 What are [hat(alpha)] and [hat(beta)] Used For?
• 3.4 Some Further Terminology
• 3.4.1 The Data Generating Process, the Population Regression Function and the Sample Regression Func
• 3.4.2 Linearity and Possible Forms for the Regression Function
• 3.4.3 Estimator or Estimate?
• 3.5 The Assumptions Underlying the Classical Linear Regression Model
• 3.6 Properties of the OLS Estimator
• 3.6.1 Consistency
• 3.6.2 Unbiasedness
• 3.6.3 Efficiency
• 3.6.4 More on Unbiasedness and Efficiency
• 3.7 Precision and Standard Errors
• 3.7.1 Estimating the Variance of the Error Term (σ[sup(2)])
• 3.7.2 Some Comments on the Standard Error Estimators
• 3.8 An Introduction to Statistical Inference
• 3.8.1 Hypothesis Testing: Some Concepts
• 3.8.2 The Probability Distribution of the Least Squares Estimators
• 3.8.3 A Note on the t and the Normal Distributions
• 3.8.4 The Test of Significance Approach (Box 3.5)
• 3.8.5 The Confidence Interval Approach to Hypothesis Testing (Box 3.6)
• 3.8.6 The Test of Significance and Confidence Interval Approaches Always Give the Same Conclusion
• 3.8.7 Some More Terminology
• 3.8.8 Classifying the Errors That Can be Made Using Hypothesis Tests
• 3.9 A Special Type of Hypothesis Test: The t-ratio
• 3.10 An Example of a Simple t-test of a Theory in Finance: Can US Mutual Funds Beat the Market?
• 3.11 Can UK Unit Trust Managers Beat the Market?
• 3.12 The Overreaction Hypothesis and the UK Stock Market
• 3.12.1 Motivation
• 3.12.2 Methodology
• 3.12.3 Conclusions
• 3.13 The Exact Significance Level
• Appendix 3.1 Mathematical Derivations of CLRM Results
• 3A.1 Derivation of the OLS Coefficient Estimator in the Bivariate Case
• 3A.2 Derivation of the OLS Standard Error Estimators for the Intercept and Slope in the Bivariate Ca
• 4 Further Development and Analysis of the Classical Linear Regression Model
• 4.1 Generalising the Simple Model to Multiple Linear Regression
• 4.2 The Constant Term
• 4.3 How are the Parameters (the Elements of the β Vector) Calculated in the Generalised Case?
• 4.4 Testing Multiple Hypotheses: The F-test
• 4.4.1 The Relationship Between the t- and the F-Distributions
• 4.4.2 Determining the Number of Restrictions, m
• 4.4.3 Hypotheses that Cannot be Tested with Either an F- or a t-Test
• 4.4.4 A Note on Sample Sizes and Asymptotic Theory
• 4.5 Data Mining and the True Size of the Test
• 4.6 Qualitative Variables
• 4.7 Goodness of Fit Statistics
• 4.7.1 R[sup(2)]
• 4.7.2 Problems with R[sup(2)] as a Goodness of Fit Measure
• 4.7.3 Adjusted R[sup(2)]
• 4.8 Hedonic Pricing Models
• 4.9 Tests of Non-Nested Hypotheses
• 4.10 Quantile Regression
• 4.10.1 Background and Motivation
• 4.10.2 Estimation of Quantile Functions
• 4.10.3 An Application of Quantile Regression: Evaluating Fund Performance
• Appendix 4.1 Mathematical Derivations of CLRM Results
• Appendix 4.2 A Brief Introduction to Factor Models and Principal Components Analysis
• 5 Classical Linear Regression Model Assumptions and Diagnostic Tests
• 5.1 Introduction
• 5.2 Statistical Distributions for Diagnostic Tests
• 5.3 Assumption (1): E(u[sub(t)])=0
• 5.4 Assumption (2): var(u[sub(t)]) = σ[sup(2)] < ∞
• 5.4.1 Detection of Heteroscedasticity
• 5.4.2 Consequences of Using OLS in the Presence of Heteroscedasticity
• 5.4.3 Dealing with Heteroscedasticity
• 5.5 Assumption (3): cov(u[sub(i)],u[sub(j)]) = 0 for i [neq] j
• 5.5.1 The Concept of a Lagged Value
• 5.5.2 Graphical Tests for Autocorrelation
• 5.5.3 Detecting Autocorrelation: The Durbin–Watson Test
• 5.5.4 Conditions Which Must be Fulfilled for DW to be a Valid Test
• 5.5.5 Another Test for Autocorrelation: The Breusch–Godfrey Test
• 5.5.6 Consequences of Ignoring Autocorrelation if it is Present
• 5.5.7 Dealing with Autocorrelation
• 5.5.8 Dynamic Models
• 5.5.9 Why Might Lags be Required in a Regression?
• 5.5.10 The Long-Run Static Equilibrium Solution
• 5.5.11 Problems with Adding Lagged Regressors to ‘Cure’ Autocorrelation
• 5.5.12 Autocorrelation in Cross-Sectional Data
• 5.6 Assumption (4): The x[sub(t)] are Non-Stochastic
• 5.7 Assumption (5): The Disturbances are Normally Distributed
• 5.7.1 Testing for Departures from Normality
• 5.7.2 What Should be Done if Evidence of Non-Normality is Found?
• 5.8 Multicollinearity
• 5.8.1 Measuring Near Multicollinearity
• 5.8.2 Problems if Near Multicollinearity is Present but Ignored
• 5.8.3 Solutions to the Problem of Multicollinearity
• 5.9 Adopting the Wrong Functional Form
• 5.9.1 What if the Functional Form is Found to be Inappropriate?
• 5.10 Omission of an Important Variable
• 5.11 Inclusion of an Irrelevant Variable
• 5.12 Parameter Stability Tests
• 5.12.1 The Chow Test
• 5.12.2 The Predictive Failure Test
• 5.12.3 Backward versus Forward Predictive Failure Tests
• 5.12.4 How Can the Appropriate Sub-Parts to Use be Decided?
• 5.12.5 The QLR Test
• 5.12.6 Stability Tests Based on Recursive Estimation
• 5.13 Measurement Errors
• 5.13.1 Measurement Error in the Explanatory Variable(s)
• 5.13.2 Measurement Error in the Explained Variable
• 5.14 A Strategy for Constructing Econometric Models and a Discussion of Model-Building Philosophies
• 5.15 Determinants of Sovereign Credit Ratings
• 5.15.1 Background
• 5.15.2 Data
• 5.15.3 Interpreting the Models
• 5.15.4 The Relationship Between Ratings and Yields
• 5.15.5 What Determines How the Market Reacts to Ratings Announcements?
• 5.15.6 Conclusions
• 6 Univariate Time-Series Modelling and Forecasting
• 6.1 Introduction
• 6.2 Some Notation and Concepts
• 6.2.1 A Strictly Stationary Process
• 6.2.2 A Weakly Stationary Process
• 6.2.3 A White Noise Process
• 6.3 Moving Average Processes
• 6.4 Autoregressive Processes
• 6.4.1 The Stationarity Condition
• 6.4.2 Wold’s Decomposition Theorem
• 6.5 The Partial Autocorrelation Function
• 6.5.1 The Invertibility Condition
• 6.6 ARMA Processes
• 6.6.1 Sample acf and pacf Plots for Standard Processes
• 6.7 Building ARMA Models: The Box–Jenkins Approach
• 6.7.1 Information Criteria for ARMA Model Selection
• 6.7.2 Which Criterion Should be Preferred if they Suggest Different Model Orders?
• 6.7.3 ARIMA Modelling
• 6.8 Examples of Time-Series Modelling in Finance
• 6.8.1 Covered and Uncovered Interest Parity
• 6.8.2 Covered Interest Parity
• 6.8.3 Uncovered Interest Parity
• 6.9 Exponential Smoothing
• 6.10 Forecasting in Econometrics
• 6.10.1 Why Forecast?
• 6.10.2 The Difference Between In-Sample and Out-of-Sample Forecasts
• 6.10.3 Some More Terminology: One-Step-Ahead versus Multi-Step-Ahead Forecasts and Rolling versus Re
• 6.10.4 Forecasting with Time-Series versus Structural Models
• 6.10.5 Forecasting with ARMA Models
• 6.10.6 Forecasting the Future Value of an MA(q) Process
• 6.10.7 Forecasting the Future Value of an AR(p) Process
• 6.10.8 Determining Whether a Forecast is Accurate or Not
• 6.10.9 Statistical versus Financial or Economic Loss Functions
• 6.10.10 Finance Theory and Time-Series Analysis
• 7 Multivariate Models
• 7.1 Motivations
• 7.2 Simultaneous Equations Bias
• 7.3 So how can Simultaneous Equations Models be Validly Estimated?
• 7.4 Can the Original Coefficients be Retrieved from the πs?
• 7.4.1 What Determines Whether an Equation is Identified or Not?
• 7.4.2 Statement of the Order Condition
• 7.5 Simultaneous Equations in Finance
• 7.6 A Definition of Exogeneity
• 7.6.1 Tests for Exogeneity
• 7.7 Triangular Systems
• 7.8 Estimation Procedures for Simultaneous Equations Systems
• 7.8.1 Indirect Least Squares (ILS)
• 7.8.2 Estimation of Just Identified and Overidentified Systems using 2SLS
• 7.8.3 Instrumental Variables
• 7.8.4 What Happens if IV or 2SLS are Used Unnecessarily?
• 7.8.5 Other Estimation Techniques
• 7.9 An Application of a Simultaneous Equations Approach to Modelling Bid–Ask Spreads and Trading A
• 7.9.1 Introduction
• 7.9.2 The Data
• 7.9.3 How Might the Option Price/Trading Volume and the Bid–Ask Spread be Related?
• 7.9.4 The Influence of Tick-Size Rules on Spreads
• 7.9.5 The Models and Results
• 7.9.6 Conclusions
• 7.10 Vector Autoregressive Models
• 7.10.1 Advantages of VAR Modelling
• 7.10.2 Problems with VARs
• 7.10.3 Choosing the Optimal Lag Length for a VAR
• 7.10.4 Rules of Thumb for VAR Lag Length Selection
• 7.10.5 Cross-Equation Restrictions for VAR Lag Length Selection
• 7.10.6 Information Criteria for VAR Lag Length Selection
• 7.11 Does the VAR Include Contemporaneous Terms?
• 7.12 Block Significance and Causality Tests
• 7.12.1 Restricted VARs
• 7.13 VARs with Exogenous Variables
• 7.14 Impulse Responses and Variance Decompositions
• 7.15 VAR Model Example: The Interaction Between Property Returns and the Macroeconomy
• 7.15.1 Background, Data and Variables
• 7.15.2 Methodology
• 7.15.3 Results
• 7.15.4 Conclusions
• 7.16 A Couple of Final Points on VARs
• 8 Modelling Long-Run Relationships in Finance
• 8.1 Stationarity and Unit Root Testing
• 8.1.1 Why are Tests for Non-Stationarity Necessary?
• 8.1.2 Two Types of Non-Stationarity
• 8.1.3 Some More Definitions and Terminology
• 8.1.4 Testing for a Unit Root
• 8.1.5 Testing for Higher Orders of Integration
• 8.1.6 Phillips–Perron (PP) Tests
• 8.1.7 Criticisms of Dickey–Fuller- and Phillips–Perron-Type Tests
• 8.2 Tests for Unit Roots in the Presence of Structural Breaks
• 8.2.1 Motivation
• 8.2.2 The Perron (1989) Procedure
• 8.2.3 An Example: Testing for Unit Roots in EuroSterling Interest Rates
• 8.2.4 Seasonal Unit Roots
• 8.3 Cointegration
• 8.3.1 Definition of Cointegration (Engle and Granger, 1987)
• 8.3.2 Examples of Possible Cointegrating Relationships in Finance
• 8.4 Equilibrium Correction or Error Correction Models
• 8.5 Testing for Cointegration in Regression: A Residuals-Based Approach
• 8.6 Methods of Parameter Estimation in Cointegrated Systems
• 8.6.1 The Engle–Granger 2-Step Method
• 8.6.2 The Engle and Yoo 3-Step Method
• 8.7 Lead–Lag Relationships Between Spot and Futures Markets
• 8.7.1 Background
• 8.7.2 Forecasting Spot Returns
• 8.7.3 Conclusions
• 8.8 Testing for and Estimating Cointegration in Systems Using the Johansen Technique based on VARs
• 8.8.1 Tests for Cointegration with Mixed Orders of Integration
• 8.8.2 Hypothesis Testing using Johansen
• 8.9 Purchasing Power Parity
• 8.10 Cointegration Between International Bond Markets
• 8.10.1 Cointegration Between International Bond Markets: A Univariate Approach
• 8.10.2 Cointegration Between International Bond Markets: A Multivariate Approach
• 8.10.3 Cointegration in International Bond Markets: Conclusions
• 8.11 Testing the Expectations Hypothesis of the Term Structure of Interest Rates
• 9 Modelling Volatility and Correlation
• 9.1 Motivations: An Excursion into Non-Linearity Land
• 9.1.1 Types of Non-Linear Models
• 9.1.2 Testing for Non-Linearity
• 9.1.3 Chaos in Financial Markets
• 9.1.4 Neural Network Models
• 9.2 Models for Volatility
• 9.3 Historical Volatility
• 9.4 Implied Volatility Models
• 9.5 Exponentially Weighted Moving Average Models
• 9.6 Autoregressive Volatility Models
• 9.7 Autoregressive Conditionally Heteroscedastic (ARCH) Models
• 9.7.1 Another Way of Expressing ARCH Models
• 9.7.2 Non-Negativity Constraints
• 9.7.3 Testing for ‘ARCH Effects’
• 9.7.4 Limitations of ARCH(q) Models
• 9.8 Generalised ARCH (GARCH) Models
• 9.8.1 The Unconditional Variance Under a GARCH Specification
• 9.9 Estimation of ARCH/GARCH Models
• 9.9.1 Parameter Estimation Using Maximum Likelihood
• 9.9.2 Non-Normality and Maximum Likelihood
• 9.10 Extensions to the Basic GARCH Model
• 9.11 Asymmetric GARCH Models
• 9.12 The GJR model
• 9.13 The EGARCH Model
• 9.14 Tests for Asymmetries in Volatility
• 9.14.1 News Impact Curves
• 9.15 GARCH-in-Mean
• 9.16 Uses of GARCH-Type Models Including Volatility Forecasting
• 9.17 Testing Non-Linear Restrictions or Testing Hypotheses About Non-Linear Models
• 9.17.1 Likelihood Ratio Tests
• 9.18 Volatility Forecasting: Some Examples and Results from the Literature
• 9.19 Stochastic Volatility Models Revisited
• 9.19.1 Higher Moment Models
• 9.19.2 Tail Models
• 9.20 Forecasting Covariances and Correlations
• 9.21 Covariance Modelling and Forecasting in Finance: Some Examples
• 9.21.1 The Estimation of Conditional Betas
• 9.21.2 Dynamic Hedge Ratios
• 9.22 Simple Covariance Models
• 9.22.1 Historical Covariance and Correlation
• 9.22.2 Implied Covariance Models
• 9.22.3 Exponentially Weighted Moving Average Model for Covariances
• 9.23 Multivariate GARCH Models
• 9.23.1 The VECH model
• 9.23.2 The Diagonal VECH Model
• 9.23.3 The BEKK model
• 9.23.4 Model Estimation for Multivariate GARCH
• 9.24 Direct Correlation Models
• 9.24.1 The Constant Correlation Model
• 9.24.2 The Dynamic Conditional Correlation Model
• 9.25 Extensions to the Basic Multivariate GARCH Model
• 9.25.1 Asymmetric Multivariate GARCH
• 9.25.2 Alternative Distributional Assumptions
• 9.26 A Multivariate GARCH Model for the CAPM with Time-Varying Covariances
• 9.27 Estimating a Time-Varying Hedge Ratio for FTSE Stock Index Returns
• 9.27.1 Background
• 9.27.2 Notation
• 9.27.3 Data and Results
• 9.28 Multivariate Stochastic Volatility Models
• Appendix 9.1 Parameter Estimation Using Maximum Likelihood
• 10 Switching and State Space Models
• 10.1 Motivations
• 10.1.1 What Might Cause One-Off Fundamental Changes in the Properties of a Series?
• 10.2 Seasonalities in Financial Markets: Introduction and Literature Review
• 10.3 Modelling Seasonality in Financial Data
• 10.3.1 Slope Dummy Variables
• 10.3.2 Interactive Dummy Variables
• 10.4 Estimating Simple Piecewise Linear Functions
• 10.5 Markov Switching Models
• 10.5.1 Fundamentals of Markov Switching Models
• 10.6 A Markov Switching Model for the Real Exchange Rate
• 10.7 A Markov Switching Model for the Gilt–Equity Yield Ratio
• 10.8 Threshold Autoregressive Models
• 10.9 Estimation of Threshold Autoregressive Models
• 10.9.1 Threshold Model Order (Lag Length) Determination
• 10.9.2 Determining the Delay Parameter, d
• 10.10 Specification Tests in the Context of Markov Switching and Threshold Autoregressive Models: A
• 10.11 A SETAR Model for the French franc–German mark Exchange Rate
• 10.12 Threshold Models and the Dynamics of the FTSE 100 Index and Index Futures Markets
• 10.13 A Note on Regime Switching Models and Forecasting Accuracy
• 10.14 State Space Models and the Kalman Filter
• 10.14.1 Introduction to the State Space Formulation
• 10.14.2 Parameter Estimation for State Space Models
• 10.14.3 Example: Time-Varying Beta Estimation
• 10.14.4 Further Reading on State Space Models
• 11 Panel Data
• 11.1 Introduction: What Are Panel Techniques and Why are They Used?
• 11.2 What Panel Techniques Are Available?
• 11.3 The Fixed Effects Model
• 11.4 Time-Fixed Effects Models
• 11.5 Investigating Banking Competition Using a Fixed Effects Model
• 11.6 The Random Effects Model
• 11.7 Panel Data Application to Credit Stability of Banks in Central and Eastern Europe
• 11.8 Panel Unit Root and Cointegration Tests
• 11.8.1 Background and Motivation
• 11.8.2 Tests with Common Alternative Hypotheses
• 11.8.3 Panel Unit Root Tests with Heterogeneous Processes
• 11.8.4 Panel Stationarity Tests
• 11.8.5 Allowing for Cross-Sectional Heterogeneity
• 11.8.6 Panel Cointegration
• 11.8.7 An Illustration of the Use of Panel unit Root and Cointegration Tests: The Link Between Finan
• 11.9 Further Feading
• 12 Limited Dependent Variable Models
• 12.1 Introduction and Motivation
• 12.2 The Linear Probability Model
• 12.3 The Logit Model
• 12.4 Using a Logit to Test the Pecking Order Hypothesis
• 12.5 The Probit Model
• 12.6 Choosing Between the Logit and Probit Models
• 12.7 Estimation of Limited Dependent Variable Models
• 12.8 Goodness of Fit Measures for Linear Dependent Variable Models
• 12.9 Multinomial Linear Dependent Variables
• 12.10 The Pecking Order Hypothesis Revisited: The Choice Between Financing Methods
• 12.11 Ordered Response Linear Dependent Variables Models
• 12.12 Are Unsolicited Credit Ratings Biased Downwards? An Ordered Probit Analysis
• 12.13 Censored and Truncated Dependent Variables
• 12.13.1 Censored Dependent Variable Models
• 12.13.2 Truncated Dependent Variable Models
• Appendix 12.1 The Maximum Likelihood Estimator for Logit and Probit Models
• 13 Simulation Methods
• 13.1 Motivations
• 13.2 Monte Carlo Simulations
• 13.3 Variance Reduction Techniques
• 13.3.1 Antithetic Variates
• 13.3.2 Control Variates
• 13.3.3 Random Number Re-Usage Across Experiments
• 13.4 Bootstrapping
• 13.4.1 An Example of Bootstrapping in a Regression Context
• 13.4.2 Situations where the Bootstrap will be Ineffective
• 13.5 Random Number Generation
• 13.6 Disadvantages of the Simulation Approach to Econometric or Financial Problem Solving
• 13.7 An example of Monte Carlo Simulation in Econometrics: Deriving a Set of Critical Values for a D
• 13.8 An Example of how to Simulate the Price of a Financial Option
• 13.8.1 Simulating the Price of a Financial Option Using a Fat-Tailed Underlying Process
• 13.8.2 Simulating the Price of an Asian Option
• 13.9 An Example of Bootstrapping to Calculate Capital Risk Requirements
• 13.9.1 Financial Motivation
• 14 Additional Econometric Techniques for Financial Research
• 14.1 Event Studies
• 14.1.1 Some Notation and a Description of the Basic Approach
• 14.1.2 Cross-Sectional Regressions
• 14.1.3 Complications When Conducting Event Studies and Their Resolution
• 14.1.4 Conducting an Event Study Using Excel
• 14.2 Tests of the CAPM and the Fama–French Methodology
• 14.2.1 Testing the CAPM
• 14.2.2 Asset Pricing Tests: the Fama–French Approach
• 14.3 Extreme Value Theory
• 14.3.1 Extreme Value Theory: An Introduction
• 14.3.2 The Block Maximum Approach
• 14.3.3 The Peaks Over Threshold Approach
• 14.3.4 Parameter Estimation for Extreme Value Distributions
• 14.3.5 Introduction to Value at Risk
• 14.3.6 Some Final Further Issues in Implementing Extreme Value Theory
• 14.3.7 An Application of Extreme Value Theory to VaR Estimation
• 14.3.8 Additional Further Reading on Extreme Value Theory
• 14.4 The Generalised Method of Moments
• 14.4.1 Introduction to the Method of Moments
• 14.4.2 The Generalised Method of Moments
• 14.4.3 GMM in the Asset Pricing Context
• 14.4.4 A GMM Application to the Link Between Financial Markets and Economic Growth
• 14.4.5 Additional Further Reading
• 15 Conducting Empirical Research or Doing a Project or Dissertation in Finance
• 15.1 What is an Empirical Research Project and What is it For?
• 15.2 Selecting the Topic
• 15.3 Sponsored or Independent Research?
• 15.4 The Research Proposal
• 15.5 Working Papers and Literature on the Internet
• 15.6 Getting the Data
• 15.7 Choice of Computer Software
• 15.8 Methodology
• 15.9 How Might the Finished Project Look?
• 15.10 Presentational Issues
• Appendix 1 Sources of Data Used in This Book and the Accompanying Software Manuals
• Appendix 2 Tables of Statistical Distributions
• Glossary
• References
• Index