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Applying mathematical and statistical practices to economics, econometrics enables economists to test theoretical hypotheses with real world data.
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Applying mathematical and statistical practices to economics, econometrics enables economists to test theoretical hypotheses with real world data. This Econometrics e-book is available as a free download. It provides simple explanations of key concepts in the field, with numerous examples and clear statistical tables for reference.

The text covers twelve important topics in econometrics, including basic probability and statistics, probability distributions, simple and multiple regression models, statistical inference, linear specification, dummy variables, heteroskedasticity, autocorrelation, multicollinearity, and simultaneous equation models.

Econometrics provides a clear and in-depth introduction to these concepts, and is clearly organized for reference use. Important vocabulary and terminology is highlighted in bold. Each chapter begins with a clear introduction and useful equations are broken down with detailed explanations.

  1. Basics of probability and statistics
    1. Random variables and probability distributions
    2. The multivariate probability distribution function
    3. Characteristics of probability distributions
  2. Basic probability distributions in econometrics
    1. The normal distribution
    2. The t-distribution
    3. The Chi-square distribution
    4. The F-distribution
  3. The simple regression model
    1. The population regression model
    2. Estimation of population parameters
  4. Statistical inference
    1. Hypothesis testing
    2. Confidence interval
    3. Type I and type II errors
    4. The best linear predictor
  5. Model measures
    1. The coefficient of determination (R2)
    2. The adjusted coefficient of determination (Adjusted R2)
    3. The analysis of variance table (ANOVA)
  6. The multiple regression model
    1. Partial marginal effects
    2. Estimation of partial regression coefficients
    3. The joint hypothesis test
  7. Specification
    1. Choosing the functional form
    2. Omission of a relevant variable
    3. Inclusion of an irrelevant variable
    4. Measurement errors
  8. Dummy variables
    1. Intercept dummy variables
    2. Slope dummy variables
    3. Qualitative variables with several categories
    4. Piecewise linear regression
    5. Test for structural differences
  9. Heteroskedasticity and diagnostics
    1. Consequences of using OLS
    2. Detecting heteroskedasticity
    3. Remedial measures
  10. Autocorrelation and diagnostics
    1. Definition and the nature of autocorrelation
    2. Consequences
    3. Detection of autocorrelation
    4. Remedial measures
  11. Multicollinearity and diagnostics
    1. Consequences
    2. Measuring the degree of multicollinearity
    3. Remedial measures
  12. Simultaneous equation models
    1. Introduction
    2. The structural and reduced form equation
    3. Identification
    4. Estimation methods
  13. Statistical tables
    1. Area below the standard normal distribution
    2. Right tail critical values for the t-distribution
    3. Right tail critical value of the Chi-Square distribution
    4. Right tail critical for the F-distribution: 5 percent level
Great book, very interesting and a great tool to bridge the gap between A-level Maths statistics modules (such as chi-squared being in s2 and 3) to university level.
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Thomas Andren