1. Lecture 1 Introduction to econometrics

2. Language?
Eng-eng
Eng-rus
Rus-eng

3. Objectives To provide you with information about
the subject of econometrics and the topics that we shall cover in the module
the learning teaching and assessment arrangements for the module
To introduce the simple bivariate linear
regression model

4. Learning and Teaching Lectures and accompanying slides
Computer lab sessions
Tutorial problems
Office hours
Text books
Web pages and links
Telegram

5. Recommended texts
Basic Econometrics, fifth edition (2009), by Gujarati, D and Porter, D
Introductory Econometrics: A modern approach, fourth edition (2008). by J. Wooldridge
Econometrics by Example (2011), by Gujarati D.
Borodich “Ekonometrika” (library)

6. Computer software for the lab classes
Stata 12
Excel

7. What is econometrics? The measurement of economic relationships
“the application of mathematical statistics to economic data to lend empirical support to models constructed by mathematical economics and to obtain numerical estimates” (Samuelson et al., Econometrica, 1954)

8. Venn diagram

9. aims of econometric modelling
explanation
policy evaluation
forecasting

10. Why study Econometrics?
Rare in economics (and many other areas without labs!) to have experimental data
Need to use nonexperimental, or observational, data to make inferences
Important to be able to apply economic theory to real world data
Useful for oil and gas industry
An empirical analysis uses data to test a theory or to estimate a relationship
A formal economic model can be tested
Theory may be ambiguous as to the effect of some policy change – can use econometrics to evaluate the program

11. traditional econometric methodology
1.Statement of theory or hypothesis. 2.Specification of the mathematical model of the theory 3.Specification of the statistical, or econometric, model 4.Obtaining the data 5.Estimation of the parameters of the econometric model 6.Hypothesis testing 7.Forecasting or prediction 8.Using the model for control or policy purposes.

12. Types of Data – Cross Sectional
Cross-sectional data is a random sample
Each observation is a new individual, firm, etc. with information at a point in time
If the data is not a random sample, we have a sample-selection problem
E.g. firms’ turnover, operating margin, market shares, individual data at a point of time.

13. Types of Data – Time Series
Time series data has a separate observation for each time period – e.g. stock prices
Since not a random sample, different problems to consider
Trends and seasonality will be important
Typical examples are daily share prices, interest rates, GDP values over time.

14. Types of Data – Panel
Can pool random cross sections and treat similar to a normal cross section. Will just need to account for time differences.
Can follow the same random individual observations over time – known as panel data or longitudinal data
An example is a data set where a number of firms are randomly selected, say in 1990, and traced from that time to 2000.

15. Causality Can be difficult to establish causality
Simply establishing a relationship between variables is rarely sufficient
Want to the extent to be considered causal
If we’ve truly controlled for enough other variables, then the estimated ceteris paribus effect can often be considered to be causal

16. Example: Rule of law
A model of economic growth implies higher Rule of law should lead to higher economic development
In the simplest case, this implies an equation like

17. Example: (continued)
The estimate of b1, is the return to ROL, but can it be considered causal?
While the error term, u, includes other factors affecting earnings, want to control for as much as possible
Some things are still unobserved, which can be problematic
Use nlsy.dta to estimate a simple earnings function

18. growth
ROL

19. GRowth colonists ROL

20. general notation for the simple bivariate linear model
for i = 1,2,…….n
With time series data we tend use t rather than i as the subscript
and T as the sample size

21. model specification the equation(s) – variables and functional form
a priori restrictions on parameters
stochastic assumptions (assumptions about the disturbance term)

22. assumptions about u mean zero constant variance
independent between observations
independent of the X variable

23. The role of the disturbance term
Reasons for the disturbance
random nature of human behaviour (random walk)
omitted variables that influence Y
errors of measurement
non-linearity
others

24. Econometric “problems”
autocorrelation and unit root
multicollinearity
heteroskedasticity
bias (omitted variable, errors-in-variables,

25. Oil export and production modeling filetype:pdf
BARRO
Yi=a1+a2*Xi1+a3*xi2+e, t=const. cross-section
Yt=a1+a2*Xt1+a3*xt2+e, i=const, time-series
Yit=a1+a2*Xit1+a3*xit2+e panel data

26. Types of econometric models
simple bivariate linear
non-linear bivariate
multiple regression
dynamic
simultaneous equation
other (e.g. logit and probit)
panel

27. Thank you!