class outline l.
Skip this Video
Loading SlideShow in 5 Seconds..
Class Outline PowerPoint Presentation
Download Presentation
Class Outline

Loading in 2 Seconds...

play fullscreen
1 / 17

Class Outline - PowerPoint PPT Presentation

  • Uploaded on

Class Outline The Meaning of Regression Data The Population Regression Function (PRF) Stochastic Specification of the PRF The Sample Regression Function (SRF) The Nature of the Stochastic Error Term Reading: Chapter 1 and2 Textbook The Meaning of Regression

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Class Outline' - lotus

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
class outline
Class Outline
  • The Meaning of Regression
  • Data
  • The Population Regression Function (PRF)
  • Stochastic Specification of the PRF
  • The Sample Regression Function (SRF)
  • The Nature of the Stochastic Error Term

Reading: Chapter 1 and2 Textbook

the meaning of regression
The Meaning of Regression

Regression analysis is concerned with the study of the relationship between one variable called explained, or dependent, variable and one or more other variables called independent, or explanatory, variables.

  • Warning: Regression analysis does not imply causation. Causality between two or more variables should be determined on the basis of some theory.
the meaning of regression3
The Meaning of Regression

Statistical versus Deterministic Relationships

We are concerned with what is known as the statistical, not functional or deterministic, dependence among variables. We deal with random or stochastic variables

Regression versus Causation

Regression does not imply causation. We need a theory to explain causation.

Regression versus Correlation

Correlation measure the strength or degree of linear association between two variables

Regression estimates or predict the average value of one variable on the basis of the fixed values of other variables

The dependent variable is assumed to be statistical, random, or stochastic. The explanatory variables are assumed to have fixed values

  • Types of Data: Time Series, Cross Section, Pooled and Panel Data.
  • Sources of Data
  • Accuracy of Data
the population regression function prf
The Population Regression Function (PRF)

Example: assume that we want to estimate the average consumption of 60 families in a community

Population = 60 families

We want to analyze the relationship between the Consumption expenditure of each family (Y) depending on the level of income (X)

the population regression function prf8
The Population Regression Function (PRF)
  • Population Regression Line (PRL)

The PRL gives the average, or mean, value of the dependent variable corresponding to each value of the independent variable, in the population as a whole

Since the PRL is approximately linear we can express it mathematically

The PRL is a line that passes through the conditional means of Y. The mathematical equation is called Population Regression Function (PRF)

the population regression function prf9
The Population Regression Function (PRF)
  • As a first approximation or a working hypothesis, we may assume that the PRF is a linear function of X

Where 1 and 2 are the parameters of the model.

By linear, we mean linearity on the parameters.

stochastic specification of the prf
Stochastic Specification of the PRF

We can express the deviation of an specific Yi around its expected value as

Where the deviation ui is an unobservable random variable taking positive or negative values known as the stochastic disturbance (error term)

stochastic specification of the prf11
Stochastic Specification of the PRF

This specification has two main parts:

  • Systematic or deterministic component
  • Nonsystematic component

If we take the expected value of the PRF, we obtain the following

significance of the stochastic disturbance term
Significance of the Stochastic Disturbance Term

The error term contains all the factors explained by other variables. Why not to include other variables?

  • Vagueness of theory
  • Unavailability of data
  • Core variables versus peripheral variables
  • Intrinsic randomness in human vehavior
  • Poor proxy variables
  • Principle of parsimony
  • Wrong functional form
the sample regression function srf
The Sample Regression Function (SRF)
  • Most of the time we do not know the population
  • We only have a sample of this population
  • Different samples will provide different sets of information
the nature of the stochastic error term
The Nature of the Stochastic Error Term
  • Samples from our population
the nature of the stochastic error term15
The Nature of the Stochastic Error Term
  • Mathematically, we can express this estimation as:

where = estimator of E(Y/Xi) the estimator of the population conditional mean

=estimator of 1

=estimator of 2

the nature of the stochastic error term16
The Nature of the Stochastic Error Term

Not all the sample data lie exactly on the respective sample regression line. Then, we need to develop the stochastic model, which we write as

where = estimator of ui

the nature of the stochastic error term17
The Nature of the Stochastic Error Term

represents the difference between the actual Y values and their estimated values from the sample regression, that is

In solving this estimation problem we do not observe 1, 2 and u. What we observe are their proxies