Module Title: Introductory
Econometrics
Module Code: MQM 302
Programme: BA
Economics +
Credit Value: 12
Module Tutor: Ngawang Dendup and
Kailash Pati
General Objectives:
This module aims to provide
learners with the knowledge and introductory tools to conduct their own
empirical research in economics, to evaluate government and business policies,
to perform forecasting, and to understand the basic statistical analyses of researchers.
Learning Outcomes
At the end of this module, the
students are expected to be able to:
·
Identify
different types of economic data.
·
Explain
how the concept of ceteris paribus of economics is applied in economic
regression models.
·
Identify
the problem of heteroskedasticity, autocorrelation, multicollinearity and
identification in the model.
·
Perform
correction to the problem endogeneity in the model.
·
Test
the existing economic theories and assumptions.
·
Apply
econometric tools to explain different economic theories.
Learning and teaching approach:
Lectures (30 hours)
Lab & Tutorials (30 hours)
Assessment:
Semester end examination (40%)
Lab Exam (45%)
Assignments (15%)
Pre-requisite: Statistics 1
Subject Matter
1. Nature of Econometrics
and Economic Data
Definition of
econometrics, steps in empirical economic analysis, structure of economic data
(cross sectional data, time series data, pooled cross sections,
panel/longitudinal data), causality and notion of ceteris paribus in
econometric analysis. (5
hours)
2. Simple & Multiple
Regression Analysis Estimations
Definition of simple regression
model, deriving the ordinary OLS estimates, mechanics of OLS, algebraic
properties of OLS statistics, goodness of fit, units of measurement and
functional forms, expected values and variances of OLS estimators and
estimating the error variance, meaning of “holding other factors fixed”,
changing more than one independent variable simultaneously, fitted values and
residuals, including irrelevant variables regression model, omitted variable
bias (simple and general case), Properties of least square estimators
-Gauss-Markov Theorem. Coefficient of determination r2 - a measure
of goodness of fit, derivation of least square estimates, linearity and
unbaisedness properties of least square estimators, variances and standard
errors of least-square estimators, covariance between 
and 
, least square
estimator of 
, minimum variance
property of least square estimators and consistency of least square estimators.
Introduction of dummy variables (case of both dependent and explanatory
variables). (30 hours)
3. Interval Estimation and
Hypothesis Testing: statistical prerequisites, interval estimates, confidence intervals
for regression coefficients, confidence interval for variance, hypothesis
testing (one tail and two test), regression analysis and analysis of variance
(12
hours)
4. Remedies to variation
in classical assumption: multicollinerity, Heteroscedascity and auto
correlation. Test for endogeneity and its correction (instrumental variable)
(13 hours)
Suggested Reading
Essential Readings:
1.
GujaratimDamodar N. (2006). Basic Econometrics, 4thedition
(Special Edition), McGraw-Hill, Inc. (ISBN:0072478527).
2.
Wooldridge, Jeffrey M., Introductory Econometrics: A Modern
Approach, 3rd edition, Thomson South-Western, 2006 (ISBN: 0324289782)
Suggested reading:
1.
Gujarati, D.N & Porter, D.C (2010), Essentials of
Econometrics, 4th edition, McGraw-Hill, Inc.
2.
Greene, William H. (2007), Econometric Analysis, 6th
Edition, Prentice Hall (ISBN: 0135132452)
3.
Brooks, Chris (2008), Introductory Econometrics for Finance,
2nd edition, Cambridge Press, (ISBN: 0521873061)
4.
http://epub.lib.aalto.fi/pdf/wp/w458.pdf
5.
http://www.jstor.org/stable/2352349
(Updated
June, 2013)