Introductory Econometrics

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A brief overview of the classical linear regression model
What is a regression model?
Regression versus correlation
Simple regression
Some further terminology
Simple linear regression in EViews -- estimation of an optimal hedge ratio
The assumptions underlying the classical linear regression model Properties of the OLS estimator
Precision and standard errors
An introduction to statistical inference

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2.6
2.7
2.8
2.9

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Contents

2.10 A special type of hypothesis test: the t-ratio
2.11 An example of the use of a simple t-test to test a theory in finance: can US mutual funds beat the market?
2.12 Can UK unit trust managers beat the market?
2.13 The overreaction hypothesis and the UK stock market
2.14 The exact significance level
2.15 Hypothesis testing in EViews -- example 1: hedging revisited 2.16 Estimation and hypothesis testing in EViews -- example 2: the CAPM
Appendix: Mathematical derivations of CLRM results

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3 Further development and analysis of the classical linear
regression model
3.1 Generalising the simple model to multiple linear regression 3.2 The constant term
3.3 How are the parameters (the elements of the β vector) calculated in the generalised case?
3.4 Testing multiple hypotheses: the F -test
3.5 Sample EViews output for multiple hypothesis tests
3.6 Multiple regression in EViews using an APT-style model
3.7 Data mining and the true size of the test
3.8 Goodness of fit statistics
3.9 Hedonic pricing models
3.10 Tests of non-nested hypotheses
Appendix 3.1: Mathematical derivations of CLRM results
Appendix 3.2: A brief introduction to factor models and principal components analysis

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4 Classical linear regression model assumptions and
diagnostic tests
4.1 Introduction
4.2 Statistical distributions for diagnostic tests
4.3 Assumption 1: E (u t ) = 0
4.4 Assumption 2: var(u t ) = σ 2 < ∞
4.5 Assumption 3: cov(u i , u j ) = 0 for i = j
4.6 Assumption 4: the xt are non-stochastic
4.7 Assumption 5: the disturbances are normally distributed
4.8 Multicollinearity
4.9 Adopting the wrong functional form
4.10 Omission of an important variable
4.11 Inclusion of an irrelevant variable

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4.12 Parameter stability tests
4.13 A strategy for constructing econometric models and a discussion of model-building philosophies
4.14 Determinants of sovereign credit ratings

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Univariate time series modelling and forecasting
Introduction
Some notation and concepts
Moving average processes
Autoregressive processes
The partial autocorrelation function
ARMA processes
Building ARMA models: the Box--Jenkins approach
Constructing ARMA models in EViews
Examples of time series modelling in finance
Exponential smoothing
Forecasting in econometrics
Forecasting using ARMA models in EViews
Estimating exponential smoothing models using EViews

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Multivariate models
Motivations
Simultaneous equations bias
So how can simultaneous equations models be validly estimated? Can the original coefficients be retrieved from the π s ?
Simultaneous equations in finance
A definition of exogeneity
Triangular systems
Estimation procedures for simultaneous equations systems
An application of a simultaneous equations approach to
modelling bid--ask spreads and trading activity
Simultaneous equations modelling using EViews
Vector autoregressive models
Does the VAR include contemporaneous terms?
Block significance and causality tests
VARs with exogenous variables
Impulse responses and variance decompositions
VAR model example: the interaction between property returns and the macroeconomy
VAR estimation in EViews...
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