An introduction to applied econometrics : a time series approach /
"An Introduction to Applied Econometrics is an entry level book that delivers real understanding of what is done in practice by applying the philosophy that an understanding of econometrics comes from seeing it in use. It bridges the gap between techniques and applications, enabling the reader...
| Основен автор: | Patterson, K. D. |
|---|---|
| Формат: | Книга |
| Език: | English |
| Публикувано: |
New York :
St. Martin's Press,
2000.
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| Предмети: | |
| Онлайн достъп: |
Table of contents Publisher description |
Съдържание:
- Economics and quantitative economics
- Description, construction and models in economics
- The scope of model building in quantitative economics
- A historical debate
- Present-day concerns
- Stylisations of methodology
- The structure and aims of this book
- General aims
- Parts and chapters
- General comments about the structure of this book
- Distinguishing characteristics of the data
- Series and cross-section data
- Time series graphs
- Frequency
- Dimension of a variable
- Some examples of time series data
- Nonexperimental data
- Experimental data
- Lagging and leading time series data
- Lagging time series data
- Leading time series data
- The lag operator
- Definition of the lag operator
- The lag polynomial
- Obtaining the sum of the lag coefficients
- A univariate dynamic model
- Bivariate relationships
- A deterministic bivariate model
- A stochastic bivariate model
- Visual representation of two variables
- Dynamic bivariate models
- Autoregressive distributed lag (ADL) models
- The distributed lag function
- More than one conditioning variable
- Notation in more complex models
- Several equations together
- An introduction to stationary and nonstationary random variables
- Time series with a varying mean
- Random variables
- The expected value of a random variable
- The variance of a random variable
- Continuous random variables
- Joint events, covariance, autocovariance and autocorrelation
- Joint events
- Covariance and autocovariance
- Conditional expectation.