Book contents
- Frontmatter
- Contents
- Preface
- User Guide
- 1 Introduction
- PART 1 DESCRIPTION
- PART 2 INFERENCE
- 9 Monte Carlo Simulation
- 10 Review of Statistical Inference
- 11 The Measurement Box Model
- 12 Comparing Two Populations
- 13 The Classical Econometric Model
- 14 The Gauss–Markov Theorem
- 15 Understanding the Standard Error
- 16 Confidence Intervals and Hypothesis Testing
- 17 Joint Hypothesis Testing
- 18 Omitted Variable Bias
- 19 Heteroskedasticity
- 20 Autocorrelation
- 21 Topics in Time Series
- 22 Dummy Dependent Variable Models
- 23 Bootstrap
- 24 Simultaneous Equations
- Glossary
- Index
13 - The Classical Econometric Model
from PART 2 - INFERENCE
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- User Guide
- 1 Introduction
- PART 1 DESCRIPTION
- PART 2 INFERENCE
- 9 Monte Carlo Simulation
- 10 Review of Statistical Inference
- 11 The Measurement Box Model
- 12 Comparing Two Populations
- 13 The Classical Econometric Model
- 14 The Gauss–Markov Theorem
- 15 Understanding the Standard Error
- 16 Confidence Intervals and Hypothesis Testing
- 17 Joint Hypothesis Testing
- 18 Omitted Variable Bias
- 19 Heteroskedasticity
- 20 Autocorrelation
- 21 Topics in Time Series
- 22 Dummy Dependent Variable Models
- 23 Bootstrap
- 24 Simultaneous Equations
- Glossary
- Index
Summary
… the class of populations we are dealing with does not consist of an infinity of different individuals, it consists of an infinity of possible decisions which might be taken with respect to the value of y.
Trygve HaavelmoIntroduction
This chapter will introduce and discuss the classical econometric box model. We will use CEM as our acronym for this fundamental model. In other books and articles, you might see this model referred to as the classical linear model or the classical regression model. The name is not as important as the content.
The CEM has been by far the most commonly used description of the data generation process in econometrics. Understanding the requirements, functioning, and characteristics of the CEM is extremely important because modeling the data generation process is a crucial step in econometric analysis. Without a model of how the data were generated, inference is impossible. Subsequent chapters present more complicated box models designed to handle some of the situations in which this basic model deals inadequately with the data generation process.
Sections 13.2 and 13.3 present a hypothetical example designed to provide an intuitive understanding of the CEM, and Sections 13.4 and 13.5 describe the CEM in a more formal way.
Introducing the CEM via a Skiing Example
Workbook: Skiing.xls
The heart of this chapter, and a crucial idea in econometrics, is the data generation process (DGP) specified by the CEM.
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- Information
- Introductory EconometricsUsing Monte Carlo Simulation with Microsoft Excel, pp. 316 - 334Publisher: Cambridge University PressPrint publication year: 2005