1 Econometrics 1 Lecture 1 Classical Linear Regression Analysis.

Slides:



Advertisements
Similar presentations
The Simple Linear Regression Model Specification and Estimation Hill et al Chs 3 and 4.
Advertisements

Ordinary Least-Squares
Classical Linear Regression Model
Applied Econometrics Second edition
Applied Econometrics Second edition
 Coefficient of Determination Section 4.3 Alan Craig
Lecture 9 Autocorrelation
Psychology 202b Advanced Psychological Statistics, II February 10, 2011.
The Simple Linear Regression Model: Specification and Estimation
1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.
1 Econometrics 1 Lecture 7 Multicollinearity. 2 What is multicollinearity.
Linear Regression with One Regression
Lesson #32 Simple Linear Regression. Regression is used to model and/or predict a variable; called the dependent variable, Y; based on one or more independent.
Statistics 350 Lecture 23. Today Today: Exam next day Good Chapter 7 questions: 7.1, 7.2, 7.3, 7.28, 7.29.
ESTIMATING THE REGRESSION COEFFICIENTS FOR SIMPLE LINEAR REGRESSION.
1 MF-852 Financial Econometrics Lecture 6 Linear Regression I Roy J. Epstein Fall 2003.
1 Econometrics 1 Lecture 6 Multiple Regression -tests.
Week Lecture 3Slide #1 Minimizing e 2 : Deriving OLS Estimators The problem Deriving b 0 Deriving b 1 Interpreting b 0 and b 1.
Chapter 2 – Simple Linear Regression - How. Here is a perfect scenario of what we want reality to look like for simple linear regression. Our two variables.
Simple Linear Regression NFL Point Spreads – 2007.
Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.
Measures of Regression and Prediction Intervals
Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
LECTURE 2. GENERALIZED LINEAR ECONOMETRIC MODEL AND METHODS OF ITS CONSTRUCTION.
1 Dr. Jerrell T. Stracener EMIS 7370 STAT 5340 Probability and Statistics for Scientists and Engineers Department of Engineering Management, Information.
MTH 161: Introduction To Statistics
Gu Yuxian Wang Weinan Beijing National Day School.
Statistical Methods Statistical Methods Descriptive Inferential
Thomas Knotts. Engineers often: Regress data  Analysis  Fit to theory  Data reduction Use the regression of others  Antoine Equation  DIPPR.
Regression Regression relationship = trend + scatter
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
The Simple Linear Regression Model: Specification and Estimation ECON 4550 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s.
Pg. 97 Homework Pg. 109#24 – 31 (all algebraic), 32 – 35, 54 – 59 #30#31x = -8, 2 #32#33 #34#35 #36#37x = ½, 1 #38#39x = -1, 4 #40#41 #42 x = -3, 8#43y.
1 Econometrics (NA1031) Lecture 4 Prediction, Goodness-of-fit, and Modeling Issues.
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
Math 4030 – 11b Method of Least Squares. Model: Dependent (response) Variable Independent (control) Variable Random Error Objectives: Find (estimated)
© 2001 Prentice-Hall, Inc.Chap 13-1 BA 201 Lecture 18 Introduction to Simple Linear Regression (Data)Data.
1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 
Section 9.3 Measures of Regression and Prediction Intervals.
Lecturer: Ing. Martina Hanová, PhD. Business Modeling.
Statistics 350 Lecture 13. Today Last Day: Some Chapter 4 and start Chapter 5 Today: Some matrix results Mid-Term Friday…..Sections ; ;
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Part II: Theory and Estimation of Regression Models Chapter 5: Simple Regression Theory.
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
8- Multiple Regression Analysis: The Problem of Inference The Normality Assumption Once Again Example 8.1: U.S. Personal Consumption and Personal Disposal.
Regression Overview. Definition The simple linear regression model is given by the linear equation where is the y-intercept for the population data, is.
Lecture 6 Feb. 2, 2015 ANNOUNCEMENT: Lab session will go from 4:20-5:20 based on the poll. (The majority indicated that it would not be a problem to chance,
Lecture #26 Thursday, November 17, 2016 Textbook: 14.1 and 14.3
Ch. 2: The Simple Regression Model
LEAST – SQUARES REGRESSION
Linear Regression Special Topics.
Objectives By the end of this lecture students will:
The Simple Linear Regression Model: Specification and Estimation
Econometrics Econometrics I Summer 2011/2012
Ch12.1 Simple Linear Regression
Regression.
Multiple Regression.
Introduction to Econometrics
Linear Regression.
The regression model in matrix form
The Regression Model Suppose we wish to estimate the parameters of the following relationship: A common method is to choose parameters to minimise the.
Linear regression Fitting a straight line to observations.
The Simple Linear Regression Model: Specification and Estimation
Section 2: Linear Regression.
Simple Linear Regression
6.1.1 Deriving OLS OLS is obtained by minimizing the sum of the square errors. This is done using the partial derivative 6.
Regression and Correlation of Data
Lecture 20 Two Stage Least Squares
Presentation transcript:

1 Econometrics 1 Lecture 1 Classical Linear Regression Analysis

2 What is Econometrics ?

3 Branches of Econometrics

4 Econometric Methodology

5

6 Graphical Illustration of a Simple Linear Regression Model

7 Minimisation of Error Sum Square

8 Derivation of Normal Equations

9 OLS Estimators

10 An Example of OLS Estimation

11 Estimates

12 Interpretation and Prediction

13 Prediction of Food Expenditure

14 Use of regression estimates to calculate the elasticities

15 Hints to get into the Shazam program in the Network

16 How to Read Data in Shazam?

17 Getting Around with Shazam

18 A Simple Example of Shazam

19 Simple Regression in matrix notation

20 The estimators in terms of matrix notation