Table of Contents
TABLE OF CONTENTS:
Preface ix
Data and software xi
Chapter 1: The Bernoulli model
1.1 Sample and population distributions
1.2 Distribution functions and densities
1.3 The Bernoulli model
1.4 Summary and exercises
Chapter 2: Inference in the Bernoulli model
2.1 Expectation and variance
2.2 Asymptotic theory
2.3 Inference
2.4 Summary and exercises
Chapter 3: A first regression model
3.1 The US census data
3.2 Continuous distributions
3.3 Regression model with an intercept
3.4 Inference
3.5 Summary and exercises
Chapter 4: The logit model
4.1 Conditional distributions
4.2 The logit model
4.3 Inference
4.4 Mis-specification analysis
4.5 Summary and exercises
Chapter 5: The two-variable regression model
5.1 Econometric model
5.2 Estimation
5.3 Structural interpretation
5.4 Correlations
5.5 Inference
5.6 Summary and exercises
Chapter 6: The matrix algebra of two-variable regression
6.1 Introductory example
6.2 Matrix algebra
6.3 Matrix algebra in regression analysis
6.4 Summary and exercises
Chapter 7: The multiple regression model
7.1 The three-variable regression model
7.2 Estimation
7.3 Partial correlations
7.4 Multiple correlations
7.5 Properties of estimators
7.6 Inference
7.7 Summary and exercises
Chapter 8: The matrix algebra of multiple regression
8.1 More on inversion of matrices
8.2 Matrix algebra of multiple regression analysis
8.3 Numerical computation of regression estimators
8.4 Summary and exercises
Chapter 9: Mis-specification analysis in cross sections
9.1 The cross-sectional regression model
9.2 Test for normality
9.3 Test for identical distribution
9.4 Test for functional form
9.5 Simultaneous application of mis-specification tests
9.6 Techniques for improving regression models
9.7 Summary and exercises
Chapter 10: Strong exogeneity
10.1 Strong exogeneity
10.2 The bivariate normal distribution
10.3 The bivariate normal model
10.4 Inference with exogenous variables
10.5 Summary and exercises
Chapter 11: Empirical models and modeling
11.1 Aspects of econometric modeling
11.2 Empirical model
11.3 Interpreting regression models
11.4 Congruence
11.5 Encompassing
11.6 Summary and exercises
Chapter 12: Autoregressions and stationarity
12.1 Time-series data
12.2 Describing temporal dependence
12.3 The first-order autoregressive model
12.4 The autoregressive likelihood
12.5 Estimation
12.6 Interpretation of stationary autoregressions
12.7 Inference for stationary autoregressions
12.8 Summary and exercises
Chapter 13: Mis-specification analysis in time series
13.1 The first-order autoregressive model
13.2 Tests for both cross sections and time series
13.3 Test for independence
13.4 Recursive graphics
13.5 Example: finding a model for quantities of fish
13.6 Mis-specification encompassing
13.7 Summary and exercises
Chapter 14: The vector autoregressive model
14.1 The vector autoregressive model
14.2 A vector autoregressive model for the fish market
14.3 Autoregressive distributed-lag models
14.4 Static solutions and equilibrium-correction forms
14.5 Summary and exercises
Chapter 15: Identification of structural models
15.1 Under-identified structural equations
15.2 Exactly-identified structural equations
15.3 Over-identified structural equations
15.4 Identification from a conditional model
15.5 Instrumental variables estimation
15.6 Summary and exercises
Chapter 16: Non-stationary time series
16.1 Macroeconomic time-series data
16.2 First-order autoregressive model and its analysis
16.3 Empirical modeling of UK expenditure
16.4 Properties of unit-root processes
16.5 Inference about unit roots
16.6 Summary and exercises
Chapter 17: Cointegration
17.1 Stylized example of cointegration
17.2 Cointegration analysis of vector autoregressions
17.3 A bivariate model for money demand
17.4 Single-equation analysis of cointegration
17.5 Summary and exercises
Chapter 18: Monte Carlo simulation experiments
18.1 Monte Carlo simulation
18.2 Testing in cross-sectional regressions
18.3 Autoregressions
18.4 Testing for cointegration
18.5 Summary and exercises
Chapter 19: Automatic model selection
19.1 The model
19.2 Model formulation and mis-specification testing
19.3 Removing irrelevant variables
19.4 Keeping variables that matter
19.5 A general-to-specific algorithm
19.6 Selection bias
19.7 Illustration using UK money data
19.8 Summary and exercises
Chapter 20: Structural breaks
20.1 Congruence in time series
20.2 Structural breaks and co-breaking
20.3 Location shifts revisited
20.4 Rational expectations and the Lucas critique
20.5 Empirical tests of the Lucas critique
20.6 Rational expectations and Euler equations
20.7 Summary and exercises
Chapter 21: Forecasting
21.1 Background
21.2 Forecasting in changing environments
21.3 Forecasting from an autoregression
21.4 A forecast-error taxonomy
21.5 Illustration using UK money data
21.6 Summary and exercises
Chapter 22: The way ahead
References
Author index
Subject index
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