Undergraduate Econometrics Hill Griffiths Judge Pdf 40

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Undergraduate Econometrics by Hill, Griffiths and Judge: A Review

Undergraduate Econometrics is a textbook written by R. Carter Hill, William E. Griffiths and George G. Judge, first published in 1997 and now in its second edition (2001). The book aims to introduce econometrics to undergraduate students using an intuitive approach that begins with an economic model. It emphasizes motivation, understanding and implementation and shows readers how economic data are used with economic and statistical models as a basis for estimating key economic parameters, testing economic hypotheses and predicting economic outcomes.

The book covers topics such as the simple and multiple linear regression models, hypothesis testing, heteroskedasticity, autocorrelation, pooling time-series and cross-sectional data, simultaneous equations models, nonlinear least squares, distributed lag models and time series models. The book also includes examples, exercises, data sets and computer programs to help students apply the concepts and methods learned. The book is suitable for students who have some background in calculus, matrix algebra and statistics.

Undergraduate Econometrics has been praised for its clear and concise exposition, its use of real-world data and applications, its balance between theory and practice, and its pedagogical features such as summaries, key terms, review questions and problems. The book has also been criticized for some errors and typos, its lack of coverage of some advanced topics such as panel data, cointegration and error correction models, its reliance on outdated software such as SHAZAM and TSP, and its high price.

Undergraduate Econometrics is available in hardcover and paperback editions from Wiley. A PDF version of the book can be downloaded for free from the Internet Archive[^1^]. The book has 366 pages and costs $40.

In this section, we will provide a brief overview of the main topics and concepts covered in Undergraduate Econometrics. The book is divided into 16 chapters, each with its own objectives, outline, introduction, main text, summary, key terms, review questions and problems. The book also has appendices on mathematical and statistical tools, data sources and computer programs.

Chapter 1: The Role of Econometrics in Economic Analysis

This chapter introduces the concept and scope of econometrics, which is the application of statistical methods to economic data. The chapter explains the difference between positive and normative economics, the role of economic models and theories, the types and sources of economic data, the steps involved in econometric analysis and the criteria for evaluating econometric models. The chapter also discusses some examples of econometric applications such as estimating the demand for gasoline, testing the efficient market hypothesis and forecasting inflation.

Chapter 2: Some Basic Probability Concepts

This chapter reviews some basic probability concepts that are essential for understanding econometrics. The chapter covers topics such as random variables, probability distributions, expected values, variances, covariances, correlation coefficients, conditional probabilities, Bayes' theorem and the law of large numbers. The chapter also introduces some common probability distributions such as the binomial, normal and chi-square distributions.

Chapter 3: The Simple Linear Regression Model: Specification and Estimation

This chapter introduces the simple linear regression model, which is the simplest and most widely used econometric model. The chapter explains how to specify and estimate the model using the method of ordinary least squares (OLS). The chapter also shows how to interpret the estimated coefficients and measure their precision using standard errors. The chapter illustrates the use of the simple linear regression model with an example of estimating the demand for cigarettes.

Chapter 4: Properties of the Least Squares Estimators

This chapter examines the properties of the OLS estimators under different assumptions about the error term. The chapter defines and derives the properties of unbiasedness, efficiency, consistency and asymptotic normality. The chapter also discusses some issues related to measurement errors, functional form misspecification and omitted variables bias.

Chapter 5: Inference in the Simple Regression Model: Interval Estimation, Hypothesis Testing and Prediction

This chapter explains how to conduct inference in the simple regression model using confidence intervals, hypothesis tests and prediction intervals. The chapter covers topics such as sampling distributions, t-tests, F-tests, p-values and critical values. The chapter also shows how to test hypotheses about individual or joint coefficients, linear restrictions or nonlinear functions. The chapter illustrates the use of inference in the simple regression model with an example of testing the permanent income hypothesis. aa16f39245