Title: Econ 399 Introductory Econometrics
1Econ 399Introductory Econometrics
- Multivariable Regressions
- Multivariable Inference
- Multivariable Statistical Adjustments
Lorne Priemaza, M.A. Lorne.priemaza_at_ualberta.ca
21. Nature of Econometrics
- 1.1 What is Econometrics?
- 1.2 Steps in Empirical Economic Analysis
- 1.3 The Structure of Economic Data
- 1.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis - Note All uncredited quotes are from
Wooldridges Introductory Econometrics (2006)
31.1 What is Econometrics?
- Definition
- Econometrics is based upon the development of
statistical methods for estimating economic
relationships, testing economic theories, and
evaluating and implementingpolicy
41.1 What is Econometrics?
- Uses
- -What impact does the price of writable DVDs
have on the price of movie popcorn? (estimating
relationship) - -Success of a marriage is inversely related to
time spent dating. (testing theory) - -Implementing a health care fee acts to eliminate
waste. (evaluating policy)
5Econometrics vs. Math. Statistics (generally)
- Mathematical Statistics
- Deals with controlled Experimental Data
- Experimental Data Data collected in a controlled
environment - Researcher is an active collector in a
controlled, artificial environment
- Econometrics
- Deals with problematic nonexperimental data
- Nonexperimental Data Observational Data,
observations of agents in the real world - Researcher is a passive collector of data from
the real world
61.1 What is Econometrics?
- Econometrics
- -Using a hidden camera in a supermarket, 27 of
shoppers bought Captain Chocolates Chocolate
Heart Attack in a Box (CCCHAB) with extra
Chocolate marshmallows - Mathematical Statistics
- -In a focus group of 57 people, 63 chose CCCHAB
over the top 3 chocolate brands
71.1 What is Econometrics?
- Note
- -Econometrics can use controlled experiments and
statistics originally devised ways to deal with
observable data - -Due to monetary, scope and morality constraints,
econometricians wrestle with nonexperimental data
more often - -ie a lab study on the mortality rate of middle
class citizens using cell phones is monetarily,
morally, and administratively unfeasible
81.2 Steps in Empirical Economic Analysis
- -Empirical analysis generally arises from two
areas - Estimating a Relationship
- Ie What factors determine a hockey players
salary? - 2) Testing a Theory
- Ie Studying after 11pm is less effective than
studying before 11pm.
91.2 Steps in Empirical Economic Analysis
- An Empirical Analysis uses data to test a theory
or estimate a relationship.
How?
101.2 Steps in Empirical Economic Analysis
- 1) Formulate a question/hypothesis
- -Does income influence driving habits?
- 2) Construct an economic model
- Economic Models consist of mathematical
equations that describe various relationships. - -Drivingf(age, income, training, family,
vehicle, location)
111.2 Steps in Empirical Economic Analysis
- Economic Models Can Come From Formal Derivations
- Formal Derivations Arise From Economic
Assumptions and Models - -Economic agents are acting to maximize utility
- -Resources are scarce
- -Information is imperfect
- -An increase in price causes a decrease in
quantity demanded - -Nash Equilibrium
121.2 Steps in Empirical Economic Analysis
- VERY SIMPLE Formal Derivations
- -Brushing ones teeth is a function of
inputssimple production theory - brushingf(time, toothpaste)
- -The amount of toothpaste purchased is a function
of price, availability, income and price of
substitutes (ie whitening strips)simple demand
theory - toothpastef(Ptp, avail, I, Py)
131.2 Steps in Empirical Economic Analysis
- -Time is a function of income, work, sleep,
family status, motivation (laziness) - timef(I, work, sleep, family, motivation)
- -Therefore, brushing ones teeth is a function of
the determinants of the inputs - brushingf(Ptp, Availtp, I, Py, work, sleep,
- family, motivation)
141.2 Steps in Empirical Economic Analysis
- Economic Models Can Also Arise From Intuition or
Observation (ie statistics) - -Tall people dont like Wii video games
- -Small businesses are less likely to change
prices - -Marks are higher in morning classes than
afternoon classes - -Impaired Driving Charges Jump 25 (Keith Gerein
and Elise Stolete, Impaired Driving Charges Jump
25, Edmonton Journal (4 January 2008), A1) - -Couples living together have an 80 greater
chance of divorce than those who dont (Barbara
Vobejda, Number of Couples Cohabitating Soring
as Mores Relax, Houston Chronicle (5 December
1996), 13A)
151.2 Steps in Empirical Economic Analysis
- Economic Models Can Also Arise From A Mixture of
Formal Derivations, Intuition or Observation (ie
statistics) - -Tall people dont like Wii video games
- And
- -Quantity demanded is a function of price
- Therefore
- Wii game demandf(height, price)
161.2 Steps in Empirical Economic Analysis
- 3) Specify an Econometric Model
- -Econometric Models have specific functional
forms and OBSERVABLE parameters - Ie brushingf(Ptp, Availtp, I, Py, work, sleep,
- family, motivation)
- Becomes
Where famSize estimates family status and u
takes into account unobservable factors
17Econ 299 Review
If we are interested in the impact of sleep on
teeth brushing, we are interested in the B5
parameter. Notice also that dBi/ dSleepi B5
181.2 Steps in Empirical Economic Analysis
- Note
- For the most part, econometric analysis begins
by specifying an econometric model, without
consideration of the details of the models
creation. - -Loosely guided by economic theory and intuition,
chose a functional form and include variables for
the initial model - -functional forms can be modified and variables
added or deleted as statistical tests are done
191.2 Steps in Empirical Economic Analysis
- 4) Formulate Hypothesis on the various parameters
- -Ask the questions or challenge the issues from
part 1 - Ie if you believe that sleep has no impact on
teeth brushing - Ho B50
- HaB5?0
201.3 The Structure of Economic Data
- Before a hypothesis can be tested and any
conclusion made, data must be gathered. - There exist a variety of types of economic data
- Cross-Sectional Data
- Time Series Data
- Pooled Cross Section Data
- Panel (Pooled) Data
- -Each data type has advantages and disadvantages.
211.3 The Structure of Economic Data
- 1) Cross-Sectional Data
- -A sample of economic agents (households, firms,
governments, groups, etc) at one point in time. - Examples
- -Household spending this Christmas
- -current Wii prices across the city
- -class height
- -National Unemployment
221.3 The Structure of Economic Data
- Generally the entire population cannot be polled,
so a Cross-Sectional data set is assumed to be a
RANDOM SAMPLE However, a sample of the
population is not random if - Bias occurs
- A sample selection problem occurs (some
categories of respondents are more likely to
respond than others) - Sample size is too small
- Sample size is too large
231.3 The Structure of Economic Data
- Bias Example
- -Interview university students to find out common
society attitudes towards sex - -Doing a landline phone survey to determine long
distance plans - Sample Selection Example
- -Rich households are less likely to report their
incomes - -Men are more likely to overestimate the number
of their relationships
241.3 The Structure of Economic Data
- Small Sample Size Example
- -Using this class as representative of the
university population - -Any study with less than 30-40 observations
- Large Sample Size Example
- -Asking 80 of this class their opinions on the
text and expected grade - -One students answer is affected by anothers
251.3 The Structure of Economic Data
- 1) Cross-Sectional Data
- -Cross-sectional data is often used in
microeconomics - -labour economics
- -public finance
- -industrial organization (IO)
- -urban economics
- -health economics
26Cross-Sectional Wii Data
Obs. Person Hours Wii Played Hours Studied Utility Male
1 Alberta 5 8 24 0
2 Jayne 12 1 35 1
3 Dominique 3 12 22 0
4 Craig 4 4 23 1
5 Kristy 6 2 28 0
6 Josh 3 1 27 1
7 David 1 15 21 1
8 Francis 1 18 20 0
271.3 The Structure of Economic Data
- 1) Cross-Sectional Data
- -Generally cross-sectional data will include an
observation number - -the order of these observations doesnt matter
- -Data may also include a DUMMY VARIABLE to
indicate if a given observation has a given trait
(male, educated, employed, etc.) - -Dummy variables will be covered in chapter 7
281.3 The Structure of Economic Data
- 2) Time Series Data
- -Time series tracks the movement of (one
agent/groups) variables over time - Examples
- -Stock, Wii or Xbox 360 prices
- -GDP
- -Player Stats
- -Edmontons vacancy rate
291.3 The Structure of Economic Data
- 2) Time Series Data
- -Time series data also often uses a chronological
observation variable - -in this case, ORDER IS IMPORTANT!
- -few economic observations are independent across
time - -trending this terms observation depends
somewhat on last terms observation - -ie Income, weight, spending, happiness
301.3 The Structure of Economic Data
- 2) Time Series Data
- -Time series data can vary in data frequency
(daily, weekly, quarterly, etc.) - -frequent time series data can exhibit seasonal
patterns (ie ice cream sales fall in winter) - -frequent time series data can be aggregated to
evaluate all data on the same frequency
31Time Series Wii Data For Jayne
Week Hours Wii Played Hours Studied Utility
1 4 2 31
2 12 1 35
3 8 1 27
4 10 2 23
5 5 4 21
6 9 1 29
7 11 3 36
8 14 4 39
321.3 The Structure of Economic Data
- 3) Pooled Cross Sections
- -Pooled Cross sections are a combination of
RANDOM samples from different years - -the same observation should not be followed over
different years - -Analysis is similar to cross sectional data,
with the additional consideration of structural
changes due to time - -relatively new concept useful for analyzing
policy effects
33Pooled Cross-Sectional Nintendo Data
Obs. Year Hours System Played Hours Studied Utility Male
1 1995 (pre Wii) 6 9 27 1
2 1995 9 5 35 1
3 1995 4 7 12 0
4 1995 7 2 25 0
5 2007 (post Wii) 6 5 17 0
6 2007 3 7 22 1
7 2007 1 11 25 0
8 2007 6 4 22 1
341.3 The Structure of Economic Data
- 4) Panel (Pooled) Data
- -time series data for EACH cross-sectional agent
in set - -also called longitudinal data
- -preferred ordering is by grouping agents
- -ie first agent over time followed by second
agent over time
351.3 The Structure of Economic Data
- Panel (Pooled) Data Advantages
- -able to control for unobserved characteristics
- -able to study the effect of lags
- -able to work with a larger data set
- Panel (Pooled) Data Disadvantages
- -statistical problems of cross-sectional data
- -statistical problems of time series data
- -more difficult to work with
36Pooled Tuition
University Tuit 99/00 Tuit 00/01 Tuit 01/02 Tuit02/03
Alberta 3551.00 3770.00 3890.00 4032.00
British Columbia 2295.00 2295.00 2181.00 2661.00
Calgary 3650.00 3834.00 3975.00 4120.00
Concordia 1668.00 1668.00 1668.00 1668.00
Lethbridge 3360.00 3470.00 3470.00 3470.00
Manitoba 3005.00 2796.00 2807.00 2818.00
McGill 1668.00 1668.00 1668.00 1668.00
Ottawa 3760.00 3892.00 4009.00 4085.00
371.3 The Structure of Economic Data
- Notes
- -panel data and pooled cross sectional data is
not covered in this course, but can be used in
the project report if extra research is done - -as time series data is difficult to analyze due
to trending, methods on dealing with time series
data become obsolete and disproved over time
381.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- One goal of econometric analysis is to examine
the causality of two variables - -a simple plotting of two variables or
calculation of correlation will only see if the
two variables move together - -cant show causation
- -although many people use simple movement
statistics to conclude about causation
391.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Ceteris paribus
- -causality can only be correctly examined Ceteris
Paribus with all else held equal - -one variables impact on another variable can
only be isolated if all other variables remain
constant
401.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Causation in a perfect, experimental world
- -causation is easier to isolate in an
experimental world - Take two identical agents and change one of their
variables (X) and observe the change in Z (cross
sectional study) - Take an agent and exogenously change one variable
(X) and observe the change in Z (time series
study) - -less accurate due to trending
411.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Causation in the real world
- -in the real world, variables change for a reason
- Ie the change in X is caused by a change in A
and B, which itself causes a change in Y - Is the change in Z due to the change in A, B, X,
Y or Z? - Zf(A)? Zf(B)? Zf(X)? Zf(Y)?
- Or Zf(A, B, X, Y)?
421.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Causation example
- Take the statistic Living together before
marriage increases the chance of divorce
Living Together
Higher Divorce Chance
431.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Causation example
- BUT why do two people decide to live together?
Uncertainty about partner
?
?
Living Together
Higher Divorce Chance
?
Fear of Commitment
What actually affects divorce rates?
441.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Causation in the real world
- -in the real world, rarely can ALL variables be
fixed - -for example, some immeasurable factors (part
of the error term) cant be fixed - -ie Aptitude
- -the question is are enough variables fixed that
a good case can be made for causality?
451.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis
- Final Note
- -Even a perfectly controlled model can
economically show causation between unrelated
variables - Ie Oilers standings and the amount of rainfall
in New York - -Any econometric model must have behind it some
THEORY of causation