Lecture 1 Introduction to econometrics

Language? Eng-eng Eng-rus Rus-eng

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

Learning and Teaching Lectures and accompanying slides Computer lab sessions Tutorial problems Office hours Text books Web pages and links E-mail Telegram

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)

Computer software for the lab classes Stata 12 Excel

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)

Venn diagram

aims of econometric modelling explanation policy evaluation forecasting

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

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.

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.

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.

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.

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

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

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

growth ROL

GRowth colonists ROL

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

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

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

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

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

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

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

Thank you!