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```									      Econ 399
Introductory Econometrics

•Multivariable Regressions
•Multivariable Inference
•Multivariable Statistical
Adjustments
Lorne Priemaza, M.A.

Lorne.priemaza@ualberta.ca
1. 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 Wooldridge’s
Introductory Econometrics (2006)
1.1 What is Econometrics?

• Definition
“Econometrics is based upon the
development of statistical methods for
estimating economic relationships,
testing economic theories, and
evaluating and implementing…policy”
1.1 What is Econometrics?

•Uses
-What impact does the price of writable
DVD‟s 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)
Econometrics vs. Math. Statistics
(generally)
• Econometrics                 • Mathematical Statistics
– Deals with problematic         – Deals with
nonexperimental                  controlled
data                             Experimental Data
– Nonexperimental                – Experimental Data:
Data: Observational              Data collected in a
Data, observations of            controlled
agents in the real               environment
world                          – Researcher is an
– Researcher is a                  active collector in a
passive collector of             controlled, artificial
data from the real               environment
world
1.1 What is Econometrics?

• Econometrics
-Using a hidden camera in a supermarket,
27% of shoppers bought Captain Chocolate‟s
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
1.1 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
1.2 Steps in Empirical Economic Analysis
-Empirical analysis generally arises from two
areas:

1) Estimating a Relationship
Ie: What factors determine a hockey
player‟s salary?

2) Testing a Theory
Ie: Studying after 11pm is less effective
than studying before 11pm.
1.2 Steps in Empirical Economic Analysis

“An Empirical Analysis uses
data to test a theory or
estimate a relationship.”

How?
1.2 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.”
-Driving=f(age, income, training, family,
vehicle, location)
1.2 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
1.2 Steps in Empirical Economic Analysis
VERY SIMPLE Formal Derivations…
-Brushing one‟s teeth is a function of
inputs…simple production theory:

brushing=f(time, toothpaste)
-The amount of toothpaste purchased is a
function of price, availability, income and price of
substitutes (ie: whitening strips)…simple demand
theory

toothpaste=f(Ptp, avail, I, Py)
1.2 Steps in Empirical Economic Analysis
-Time is a function of income, work, sleep, family
status, motivation (laziness)

time=f(I, work, sleep, family, motivation)

-Therefore, brushing one‟s teeth is a function of
the determinants of the inputs:

brushing=f(Ptp, Availtp, I, Py, work, sleep,
family, motivation)
1.2 Steps in Empirical Economic Analysis
Economic Models Can Also Arise From Intuition or
Observation (ie: statistics)
-Tall people don‟t 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 don‟t (Barbara Vobejda,
“Number of Couples „Cohabitating Soring as Mores Relax,” Houston Chronicle (5 December 1996), 13A)
1.2 Steps in Empirical Economic Analysis
Economic Models Can Also Arise From A Mixture
of Formal Derivations, Intuition or Observation
(ie: statistics)
-Tall people don‟t like Wii video games
And
-Quantity demanded is a function of price
Therefore
Wii game demand=f(height, price)
1.2 Steps in Empirical Economic Analysis
3) Specify an Econometric Model
-Econometric Models have specific functional forms
and OBSERVABLE parameters

Ie: brushing=f(Ptp, Availtp, I, Py, work, sleep,
family, motivation)
Becomes

Where famSize estimates family status and u takes into
account unobservable factors
Econ 299 Review

If we are interested in the impact of sleep
on teeth brushing, we are interested in the
B5 parameter.

Notice also that

δBi/ δSleepi = B5
1.2 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 model‟s 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
1.2 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: B5=0
Ha:B5≠0
1.3 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.
1.3 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
1.3 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:
1) Bias occurs
2) A sample selection problem occurs (some
categories of respondents are more likely to
respond than others)
3) Sample size is too small
4) Sample size is too large
1.3 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
1.3 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 student‟s answer is affected by another‟s
1.3 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
Cross-Sectional Wii Data
Hours Wii       Hours
Obs.   Person        Played          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
1.3 The Structure of Economic Data
1) Cross-Sectional Data

-Generally cross-sectional data will include an
observation number
-the order of these observations doesn‟t 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
1.3 The Structure of Economic Data
2) Time Series Data

-Time series tracks the movement of (one
agent/group‟s) variables over time
Examples

-Stock, Wii or Xbox 360 prices
-GDP
-Player Stats
-Edmonton‟s vacancy rate
1.3 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 term‟s observation depends
somewhat on last term‟s observation
-ie: Income, weight, spending, happiness
1.3 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
Time Series Wii Data For Jayne
Hours Wii
Week        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
1.3 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
Pooled Cross-Sectional Nintendo Data
Hours
Obs                   System Hours
. Year               Played  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
1.3 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
1.3 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
Pooled 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
1.3 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
1.4 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
-can‟t show causation
-although many people use simple movement
statistics to conclude about causation
1.4 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 variable‟s impact on another variable can
only be isolated if all other variables remain
constant
1.4 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
a) Take two identical agents and change one of
their variables (X) and observe the change in
Z (cross sectional study)
b) Take an agent and exogenously change one
variable (X) and observe the change in Z
(time series study)
-less accurate due to trending
1.4 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?
Z=f(A)? Z=f(B)? Z=f(X)? Z=f(Y)?
Or         Z=f(A, B, X, Y)?
1.4 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
1.4 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?
1.4 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) can‟t be fixed
-ie: Aptitude

-the question is: are enough variables fixed that
a good case can be made for causality?
1.4 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: Oiler‟s standings and the amount of rainfall in
New York

-Any econometric model must have behind it
some THEORY of causation

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