Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Set of Syllabi of Courses in Econometrics
Andrei Stoianov
Econometrics beginner course syllabus
This course is designed for the second year of undergraduate studies in Novosibirsk
State University. Students are supposed to know the basics of statistics and
probability theory, macro and microeconomics. The purpose of the course is to give
basic knowledge of elementary econometric tools with use of examples and empirical
studies. It does not include complicated matrix algebra, mathematical proofs and deep
calculus.
The course consists of 32 lectures and 16 seminars to be hold in computer labs for
empirical practice and preparation of the homework.
The final grade is determined by two homeworks (10%), midterm test (20%), term
assignment (20%) and final exam (50%).
The main textbooks:
Gujarati, Basic Econometrics, McGraw-Hill, 1988
G. Maddala, Introduction to Econometrics, New York:Macmillian, 2nd ed., 1992.
Course outline:
TOPIC 1. INTRODUCTION TO ECONOMETRICS. What is Econometrics?
Definition, aims and methodology of econometrics. Econometric models. Data
sources.
TOPIC 2. CLASSICAL LINEAR REGRESSION MODEL. Specification of
relationships and basic assumptions of the simple linear regression model. The
interpretation of the assumptions. Estimation method of ordinary least squares.
Multiple linear regression. Statistical inference in the simple and multiple regression
models. Prediction with simple and multiple regression models. Interpretation of
results.
Homework 1
TOPIC 3. STATISTICAL INFERENCE WITH REGRESSION MODELS.
Hypothesis testing. Testing significance of regression and correlation coefficients.
Goodness of fit of the model. Testing a set of linear restrictions. Testing of structural
change in data.
TOPIC 4. FUNCTIONAL FORM. Alternative functional forms: estimation and
interpretation of the results. Choice of correct functional form. Consequences of
incorrect functional forms.
TOPIC 5. NORMALITY. The role and significance of the normality assumption:
when it is needed and when it is not. Testing for normality. Linearization of a model
and the normality assumption.
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
TOPIC 6. MODEL SPECIFICATION: SELECTION OF INDEPENDENT
VARIABLES. Omission of relevant variables. Inclusion of irrelevant variables.
Search of specification. Consequences of incorrect specification. The dangers of pretesting in variable selection.
Midterm test
TOPIC 7. DATA PROBLEMS. MULTICOLLINEARITY. Definition of
multicollinearity and its consequences. Possible reasons. Detection of multicolinearity
and remedies for it. Principal component regression, dropping variables. Other data
problems: missing observation. Measurement error and proxy variables.
TOPIC 8. NON SPHERICAL DISTURBANCES: HETEROSCEDASTICITY.
Definition, causes and consequences. Detection and remedies to the heteroscedasticity
problem (White's method, White's, Goldfeld-Quandt, Reusch-Pagan tests).
TOPIC 9. NON SPHERICAL DISTURBANCES: AUTOCORRELATION.
Definition and possible reasons. Consequences of autocorrelation. Detection of
autocorrelation problem (Durbin-Watson statistics, autocorrelation function, LM test).
Remedies for correction: GLS, differentiation.
Homework 2.
TOPIC 10. DUMMY VARIABLES IN ECONOMETRICS. Dummy variables for
changes in the intercept term, for changes in the slope coefficients, for cross-equation
constraints, for testing stability of regression coefficients. Seasonality. Outlayers
correction.
TOPIC 11. INTRODUCTION TO THE REGRESSION ANALYSIS OF TIME
SERIES DATA. Least square estimation. Problems: autocorrelation; detection and
consequences. Correction for autocorrelation. Misspecified dynamics. Model(s) with
lagged dependent variable(s).
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Larisa Melnikova
Novosibirsk State University
Financial Econometrics: problems and application of methods and models
Naim Ibragimov
Valery Karpenko
Larissa Melnikova
This one-semester course is about choosing adequate methods of forecasting time
series of financial data and about applying the techniques chosen on the example of
the FOREX market. The volume of the course is 36 hours in classroom. In the future
it is proposed to expand it up to 2-semester course.
The feature of the course – to set students into imitating environment (the work with a
training account on the site http://www.akmos.ru/main/study/), which will allow to
check practically the methods and the theories studied earlier.
Background of students of the third year in the Novosibirsk State University:
“Macroeconomics” –2-semester course, “Introduction to the social-economic
statistics” - 2-semester course, “Time series analysis” – 1-semester course;
“Regression analysis”– 1-semester course; “Econometrics-2” – 1-semester course,
“Theory of finances” – 1-semester course.
The goal of students is to “gain” a maximum sum of money on training account using
the methods proposed.
Preliminary content
1) Introduction and overview: (1 hour)
2) Common knowledge about the trading on Forex (2 hours)
a) Forex as a part of the global financial market, risks, Forex sectors (spot
market, forward market, futures market, currency options)
b) Trade systems on Forex (trading with brokers, direct dealing), tools of access
to the market (real trading and imitation);
c) Available software
3) The methods of technical analysis
(8 hours)
a) Graphical methods
i) Charts for the technical analysis
ii) Trends, support and resistance lines
iii) Trend reversal and continuation patterns; gaps
b) Analytical methods
i) moving averages; oscillators; other functions and indices (envelops;
Ballinger bands; average true range; median price, commodity channel
index; directional movement index; moving average convergencedivergence (MACD); momentum; the relative strength index (RSI); rate of
change (ROC); indicators combination)
c) Trading on a virtual account with the use of tools of technical analysis
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
4) Basic methods of fundamental analysis (12 hours)
a) Testing the simple models of exchange rate determination
i) Covered and uncovered interest rate parity
ii) Real interest rate parity
iii) Purchasing power parity
b) Indicators for the fundamental analysis
i) Basic macroeconomic indices (purchasing power index, business activity
indices, indices of monetary system, consumer price index)
ii) Analysis of the balanceof payments and of the state budget
iii) Testing of forecasting power of macroeconomic indices
c) Trading on a virtual account with the use of tools of fundamental analysis
d) Comparison of the results obtained with the use of technical and fundamental
analysis
5) Application of econometric tools – 8 hours
a) Autoregressive models, Box-Jenkins model, ARCH models, GARCH models
(construction of one of AR-models)
b) Spectral analysis methods (trend-cyclical models)
c) Cointegration models
d) Trading on a virtual account with the use of econometric tools
e) Comparison of the results obtained with the use of different econometric tools
6) Acquaintance with practical trading on the Siberian Interbank Stock
Exchange and in some banks – 3 hours
7) Conclusion – 2 hours
a) Generalisation of the work in the group
b) Links with the theories studied
c) History of developing the methods presented
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Svetlana Golovina
Econometrics (introductory course)
The purpose of an introductory course in Econometrics is to introduce students to the
theory and application of econometric methods. It covers basic tools of estimation and
inference in the context of the single-equation linear regression model, and deals
primarily with least squares methods of estimation. The course is supposed to
emphasise the intuitive understanding and practical application of these basic tools of
regression analysis to the specific situations in such areas, for example, as agriculture.
This course covers the statistical tools needed to understand empirical economic
research and to plan and execute independent research projects. Topics include
statistical inference, regression, generalised least squares, instrumental variables,
simultaneous equations models, and the evaluation of government policies and
programs. This course is designed to give students a solid background in
econometrics. The course will be conducted as a combination of theory and practice.
This learning-by-doing approach will entail theoretical lectures followed by computer
modeling exercises using special software programs, such as STATISTICA and
Eviews.
Intended learning outcomes:
At the end of the course students should be able to:
1. Critically analyse empirical studies which use econometric techniques.
2. Carry out an applied econometric analysis using ‘real’ data.
3. Assess the integration of, and co-integration between economic time series.
4. Describe the main kinds of distributed lags which are used in empirical work.
5. Assess the stability of estimated equations.
6. Have an understanding of some of the problems that occur in estimating and testing
the theoretical models which arise in microeconomics and macroeconomics.
Prerequisites:
This course is designed to be a first course in econometric theory. It will be carry out
for third year students. The students are assumed to have sufficient background in
macroeconomics, mathematical statistics, probability theory, and statistics. However,
a selective review of basic concepts in statistics is often advisable.
Teaching and learning methods:
Lectures
Classroom work
PC classes: the statistical analysis (computer-exercise) is to be done using
STATISTICA and EVIEWS.
Weekly consultations (tutorials): several graded problem sets (which have both
theoretical and analytical components) will be review.
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
The empirical project consists of an attempt to apply empirical studies to the analysis
of particular macroeconomic issues. Students can work on projects in teams (no more
than four students in a team).
Assessment. The final course grade will be computed using the following weights:
classroom work - 25%, empirical project - 25%, exam paper - 50%.
In total the course includes: 30 hours of lectures, 40 hours of classes (second
semester)
Texts and readings:
The main textbooks for this course is:
1. Katyshev, P., Magnus, J., and Peresetsky, A., Econometrics. Introductory Course,
5th edition, Delo, Moscow, 2001
2. Econometrics, editor Eliseeva, I.
3. J.M. Wooldridge, Introductory Econometrics, South-Western (2000).
4. J. Stewart, l.Gill. Econometrics, Prentice Hall Europe (1998)
These books provide an excellent treatment of most topics covered in the course.
Supplementary reading:
1. R.S. Pindyck & D.L. Rubinfeld, Econometric Models and Economic Forecasts,
3rd edition, McGraw Hill, 1991.
2. W.H.Greene, Econometric Analysis, 3rd edition, Prentice Hall, 1997.
3. J.Johnston, J.DiNardo, Econometrics Methods, 4th edition, McGraw-Hill, 1997.
4. Katyshev, P. and Peresetsky, A., Exersises for introductory course in
econometrics, Delo, Moscow, 1999 Дело, Москва, 1999
Each of these books provides accessible introductory treatment of most of the topics
covered in the course. Where appropriate, references to relevant parts of these
textbooks are given in the course outline. Students are urged to consult these
alternative readings, particularly when they encounter difficulties with a given topic.
COURSE OUTLINE
Lecture 1. Econometrics: Introduction
1.1 What is econometrics? Why is it useful?
1.2 Types of models: cross sections and time series.
1.3 Estimation: method of ordinary least squares (OLS).
Lecture 2. Review of statistical inference (point and interval estimation;
hypothesis testing)
1.1 Sampling Distributions and Inference. Probability and Distribution. Expectation
and Moments
1.2 Approximate [Asymptotic] Distribution of the Sample Mean
1.3 Confidence Intervals
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Lecture 3. Bivariate Regression
1.1 Classical Linear Regression Model.
1.2 Basic assumptions, homoscedasticity, heteroscedasticity, serial correlation.
Normally distributed errors.
1.3 Statistical properties of OLS estimations. Gauss-Markov Theorem, discussion of
basic assumptions, proof.
1.4 Nonlinear Models
Lecture 4. Estimation and testing in dynamic models. Theory and Methods for
Dependent Processes
1.1 OLS in linear dynamic models
1.2 Testing for serial correlation
1.3 Estimation in nonstationary models
1.4 Unit roots and cointegration. Stationarity. Random walk. AR(p) process. Unit
roots. Dickey-Fuller statistic. Augmented Dickey-Fuller test. Spurious regression.
Cointegration. Engel and Granger approach. MakKinnon statistic. Cointegration
vector. Long-run dynamic equilibrium.
Lecture 5. Introduction to Multivariate Regression
1.1 The multiple regression model, matrix algebra. Residuals and their properties.
1.2 R2 and adjusted R2. Testing hypothesis , critical values, significance level, Pvalue, confidence intervals. Testing regression equation, discussion on R 2 and t-,
F-statistics.
1.3 Maximum likelihood estimators.
Lecture 6. Multivariate Regression (continued)
1.1 Multicollinearity (geometrical interpretation, examples),
estimates, interpretation of regression coefficients.
1.2 Generalised least squares.
1.3 Heteroscedasticity. Nonnormality and testing for normality.
non-stability
of
Lecture 7. Using multiple regression
1.1 Regression analysis of macroeconomic issues.
1.2 Regression analysis of agrarian issues and food industry problems.
1.3 Regression diagnostics
Lecture 8. Time Series Analysis
1.1 Stationary time series models, ARIMA models
1.2 Time Series and Spectral Methods in Econometrics
1.3 Estimation of time series models
1.4 Forecasting with an ARIMA models. Seasonal ARIMA models.
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Lecture 9. Simultaneous-equation models. Panel Data
1.1 Simultaneous Equations Models -- Motivation and Identification
1.2 Simultaneous Equations Models -- Estimation
1.3 Panel Data – Motivation, Uses and Estimation
Lecture 10. Selected Applications and Uses of Econometrics
1.1 Demand Analysis
1.2 Econometric Methods for Modeling Producer Behaviour
1.3 Labor Econometrics
1.4 New Econometric Approaches to Stabilization Policy in Stochastic Models of
Macroeconomic Fluctuations
1.5 Economic Policy Formation: Theory and Implementation (Applied Econometrics
in the Agrarian Policy)
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Oksana Sidorova
Time series econometrics
Year: 4th
Lectures: 18
Semester: 1st or 2d
Classes: 9
Main aims:
1. To teach students to estimate and test dynamic econometric models using
modern econometric techniques.
2. To develop abilities of critical assessment and analysis of results of
econometric estimation and testing.
Main outcomes:
After the completion of this course the students should be able:
1. To use modern econometric techniques and independently estimate and test
dynamic econometric models;
2. To understand main advantages and disadvantages of modern econometric
methodologies.
Background of students:
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Mathematical analysis (1st – 2d year)
Linear algebra and geometry (1st year)
Quantitive methods (1st – 2d year)
Probability theory (2d year)
Mathematical statistics (3d year)
Theory of stochastic processes (3d year)
Introduction to econometrics (3d year)
Introduction to macroeconomics (2d year)
Problems of teaching econometrics:
1. Students have good training in mathematics but insufficient background in
economics and there is no relationship between mathematic and economic
disciplines;
2. It is necessary to pay attention to teaching economics and applied
mathematical methods in economics.
On graduating from our university students may receive high education (5 years) in
order to be specialists or take undergraduate (bachelor, 4 years) and graduate (master,
6 years) programs. This course is planned to be the one – term discipline teaching for
the 4th year students (undergraduate and specialists) studying at the chair of
“Mathematical statistics and econometrics”.
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
Content:
1. Traditional methodology.
(1) Cowles commission approach: main assumptions.
(2) The failure of traditional methodology.
(3) “Data mining”. Lovell bias.
2. Introduction to dynamic modeling.
(1) The Lag operator.
(2) ARMA modellig. Properties of AR, AM and ARMA processes.
(3) Identification, estimation and testing ARMA models.
(4) Forecasting.
3. “General to specific” methodology. ADL models.
(1) Autoregressive Distributed Lag Modeling. ARMA. Testing restrictions.
(2) “General to specific” modeling and “data mining”.
(3) Specification and testing.
(4) Exogenity.
4. Nonstationarity and unit roots. Integration.
(1) Stationarity and nonstationarity. Spurios regression. Unit root.
Stochastic trend.
(2) Integrated processes.
(3) Testing for the order of integration:
i. Augmented Dickey – Fuller test.
ii. Distribution of DF – statistic.
iii. Determination of order of integration.
iv. Dickey – Pantula test.
v. Integration under structural break.
5. Cointegration and ECM.
(1) The concept of cointegration.
(2) Testing for cointegration in univariate case.
(3) Estimation of cointegrating vector. Properties of the estimatior.
(4) Error correction model. Granger representation theorem.
(5) Estimation of error correction model. Granger-Engle approach.
Properties of the estimator.
6. Vector autoregressive model.
(1) VAR analysis.
(2) Estimation of VAR’s.
(3) Impulse response analysis. Decomposition of variance.
(4) Structural VAR analysis.
(5) Granger causality inference.
(6) Stationarity condition for VAR.
7. Vector error correction model.
(1) VAR model for nonstationary variables.
(2) Estimation of VECM:
i. Estimation of cointegration vectors.
ii. Testing for cointegration rank.
iii. Estimation of VECM coeffitients.
8. Heteroscedastisity.
(1) Properties of OLS estimator. Correction of errors. White and Newey –
West standard errors.
(2) Test for heteroscedastisity:
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Workshop “Teaching Modern Theoretical and Applied Macroeconomics”
European University at St. Petersburg
i. The White test
ii. The Breush – Pagan/Godfrey test.
iii. The Goldfeld – Quandt test.
(3) ARCH and GARCH models. Testing for ARCH and GARCH.
This is the variant of the advanced econometric course called Econometrica 2
teaching in our university. Now this discipline consists of the theoretical part and
computer classes where students learn to estimate and test dynamic econometric
models and apply their theoretical knowledge to analyzing some data. Usually during
the computer classes we use several examples illustrating every subject of the course
each taken separately. Moreover time–series which is used in practice are taken not
from the “real” life but modeled by students themselves with the help of some
statistical packages for example Statgrafics or Eviews.
From the one hand using “self-modeled” data allow not only to develop students
abilities and skills of working with modern econometric software but also to
understand better features of the methodologies and techniques they were been
familiar with.
From the other hand we want to emphasize the empirical correspondence through
examples of an empirical issues of “real” life problems to the theoretical aspects of
the course.
That’s why we would like to make some changes in the structure of the existing
course. Our idea is to divide it on two parts: theoretical and applied. The structure of
the first part has already been described more or less detailed. Second part we are
planning to devote in some sense to “global” econometric investigation (moving from
the easiest empirical techniques to the most advanced) with the use of one basic
example or several closely related examples of “real” financial time–series.
The structure of the second part of the course, the tasks, and exercises for students are
not clearly developed yet. This is the subject which I would like to discuss during the
curriculum development session.
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