1 / 33

Lecture Notes 1a

Hans Lf, Lecture 1. 2. Lectures. 1. Introduction to the Course and to Econometrics (1/9,HL)REGRESSION ANALYSIS WITH CROSS SECTIONAL DATA2. Statistics and Simple Regression Analysis (2/9, HL)3. MultipleRegression Analysis (6/9, HL)4. Multiple Regression Analysis (9/9, HL)5. Multiple Regression

etta
Télécharger la présentation

Lecture Notes 1a

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Hans Lf, Lecture 1 1 Lecture Notes 1(a) Welcome to the Course & A Guide to Econometrics

    2. Hans Lf, Lecture 1 2 Lectures 1. Introduction to the Course and to Econometrics (1/9,HL) REGRESSION ANALYSIS WITH CROSS SECTIONAL DATA 2. Statistics and Simple Regression Analysis (2/9, HL) 3. MultipleRegression Analysis (6/9, HL) 4. Multiple Regression Analysis (9/9, HL) 5. Multiple Regression analysis (13/9, HL&MW) 6. Workshop (20/9, HL) REGRESSION ANALYSIS WITH TIME SERIAL DATA 7. Time Serial Analysis (27/9, MW) 8. Time Serial Analysis (30/9, MW) 9. Instrumental Variables Estimation and 2SLS (5/10, HL) 10.Pooled Data Models, Panel Data Models. (11/10, HL) 11.Limited Dependent Variable Models and Sample Selection (12/10, HL) 12. Advanced Time Series Topics. (19/10, MW).

    3. Hans Lf, Lecture 1 3 Exercises REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA Problem Sets 1. Simple regression Model. Chapter 2. (6/9) 2. Multiple Regression Model. Chapters 3-5. (13/9) 3. Multiple Regression Model. Chapters 6-9.(20/9) REGRESSION ANALYSIS WITH TIME-SERIES DATA Problem Sets 4. Time Series Analysis. Chapters 10-12 and 18. (30/9) 5. Instrumental Variables and 2SLS. Chapters 15 and 16. (5/10) 6. Panel Data Methods. Chapters 13-14. (11/10) 7 Limited Dependent Variable Models, Sample Selection Mod. Ch 17. (12/10) 8. Advanced Time Series Topics. Chapter 18. (19/12)

    4. Hans Lf, Lecture 1 4 Data sets BWGHT, data on births to women in the US 401K, data to study the rerlationsship between participation in a pension plan and the generosity of the plan. CEOSAL2, data on chief executive officers for U.S corporations. WAGE2, data on monthly salary and an IQ score RDCEM, data on expenditures on research and developemnt, and sales HPRICE, data on house prices and various characteristics of the houses. VOTE1, data on election votes and campaign expensitures. LAWSCH85, data on rank of law schools and salaries ATTEND.

    5. Hans Lf, Lecture 1 5

    6. Hans Lf, Lecture 1 6

    7. Hans Lf, Lecture 1 7 Requirement 3 Problem Sets (Probelems and Computer Exercises) Cross-Sectional Data 5 Problem Sets (Problems and Computer Exercises) Time Series Data 1 Written Project Final Exam

    8. Hans Lf, Lecture 1 8 Why study Econometrics? Rare in economics (and many other areas without labs) to have experimental data. Need o use nonexperimental, or observational, data to make inference. Important to be able to apply economic theory to the real world data.

    9. Hans Lf, Lecture 1 9 How to Learn? Introductory Econometrics (Wooldrige) ( If necessesary, Appendix A-C) STATA 9 (Axcess through Mimers Bar) 18 Data sets (Axcess through Mimers Bar) Lectures notes Group Dynamic learning, 3-person groups

    10. Hans Lf, Lecture 1 10 Example: Returns to Education A model of human capital investment implies getting more education should lead to higher earnings In the simplest case, this implies an equation like

    11. Hans Lf, Lecture 1 11 Example: (continued) The parameters of the model, b0 and b1, are constants to be estimated. In this case, they are intercept and slope as a function relating education to earnings. The estimate of b1, is the return to education. The error term, u, includes other factors affecting earnings, including unobserved factors.

    12. Hans Lf, Lecture 1 12 Relationship?

    13. Hans Lf, Lecture 1 13 Causality or correlation? Having added more factors to out model, we hope that we now have an estimate of ceteris paribus effect (every thing else equal) If we truly controlled for everyting (sex, age, ability etc) the estimated ceterisbus can be considered to be causal effect. Somethimes even if there are unobserved factors, the effect can be considered causal).

    14. Hans Lf, Lecture 1 14 Introduction 1. What is Econometrics 2. The Disturbance Term 3. Estimates and estimators

    15. Hans Lf, Lecture 1 15 Some Criteria for Estimators 1. Least Squares 2. Highest R2 3. Unbiasedness 4. Efficiency

    16. Hans Lf, Lecture 1 16 What is Econometrics? There does not exit a generally accepted answer to this question! Why? Econometricians wear many different hats!

    17. Hans Lf, Lecture 1 17 (1) Economists First, and foremost, they are economicts, capable of utilizing economic theory to improve their empirical analyses and the the problems they adresses.

    18. Hans Lf, Lecture 1 18 (2) Mathematicians At times they are mathematicians, formulating economic theory in ways that make it appropriate for statistical testing.

    19. Hans Lf, Lecture 1 19 (3) Accountants At times thay are accountants, concerned with the problem of finding and collecting economic data and relating theoretical economic variables to the observable ones

    20. Hans Lf, Lecture 1 20 (4) Applied Statisticians At time they are applied statisticians, spending hours with the computer trying to estimate economic relationsships or predict economic events

    21. Hans Lf, Lecture 1 21 (5) Theoretical Statisticians And at times they are theoretical statiticians, applying their skills to the development of statistical techniqies appropriate to the empirical problem characterizing the science of economics

    22. Hans Lf, Lecture 1 22 What distinguishes an econometrican from a statistician? The formers preoccupation with problems caused by violation of the statisticians standard assumtions due to - The nature of economic relationships - The lack of controlled experimentation

    23. Hans Lf, Lecture 1 23 What distinguish an econometrician from an economist? A major distinction between economists and econometricians is that the latters concern with the disturbance terms. An economist will specify that consumption is a function of income: C= f(Y). An econometrician will claim that this relationship must also include a disturbance (or error) term: C=f(Y)+e

    24. Hans Lf, Lecture 1 24 Deterministic or Stochastic (1) Without the disturbance term, the relationsship is said to be exact or deterministic; With the disturbance term it is said to be stochastic.

    25. Hans Lf, Lecture 1 25 Deterministic or Stochastic (2) A stochastic relationship is not always rigth on the target in the sense that it predistcs the presice value of the variable beeing explained just as a dart thrown at a target seldom hits the bulls eye. The disturbance term is used to capture explicitly the size of these misses or errors.

    26. Hans Lf, Lecture 1 26 Parameter, Estimate and Estimators Parameter: An unknown value that describes a population relationship. Estimate is a numerical value taken on by an estimator for a parameter in a particular sample data. The estimator is the formula or the recipe by which the data are tranformed into an actual estimate.

    27. Hans Lf, Lecture 1 27 Least Squares (1) Assume a relationship: Y=f(X)+e For any set of values of the parameters characterizing a relationship between the - * dependent variable (Y) Income (the veriable beeing explained) Can be calculated using the values of the independet variable (s) [(X) for example R&D] in the data set.

    28. Hans Lf, Lecture 1 28 Least Squares (2) The estimated values on Income (called ) of the dependent variable can be subtracted from the actual values of (y) of the dependent variable in the data set to produce what is calles residuals These residuals can be thougth of as estimates of the unknown disturbances inherent in the data set. In Econometrics, we are trying to minimize the sum of squared residuals. (Lecture 1, and 2!)

    29. Hans Lf, Lecture 1 29 Highest R2 A statistic that appears frequently in economics is the coefficient of determination, R2. It is supposed to represent the proportion of the variation in the dependent variable (Y) explained by variation in the independet variables (X1Xn)

    30. Hans Lf, Lecture 1 30 Unbiasedness Suppose we perform the conceptual experiment of what is called a repeated sample. This could be repeated, say 2000 times. For each of these repeated samples, we could use an estimator b* to calulate an estimate of b ( i.g the marginal impact on wage of education), The man ner in which these estimates are distributed is called the sampling distribution of b*

    31. Hans Lf, Lecture 1 31 Unbiasedness An estimator b* is said to be an unbiased estimator of b if the mean of its sampling distribution is equal to b if the mean of its sampling distribution is equal to b. The mean of the sampling distribution of b* is called the expected value of b* and is written E b*. The bias of b* is the difference between E b* and b. (And if we could undertake repeated samling in an infinite number of times, we would get the correct estimate on the average.

    32. Hans Lf, Lecture 1 32 Efficiency In some econometric problems it is impossible to find an unbiased estimator. But whenever one unbiased estimator can be found, it is usually the case that a large number of ofther unbaised estimator also can be found. In this circumstance the unbaised estimator whose sampling distribution has the smallest variance is called the best unbiased estimator, or the efficient estimator.

    33. Hans Lf, Lecture 1 33

More Related