ECO 108 Economic Forecasting Koreguota

7/27/2019 ECO 108 Economic Forecasting Koreguota

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ECONOMIC FORECASTING

Course code ECO108

Course title Economic Forecasting

Type of course Main

Study level 1st

Department Bachelor studies

Year of study 4th

Semester Autumn

ECTS credits 6: 32 hours of lectures, 16 hours of practice, 112 hours of independent study, 2 hours of consultation

Coordinating lecturer Dr. Renatas Kizys

Study form Full-time

Course prerequisites Econometrics

Language of instruction English

Course description

Building upon time series econometrics, this course provides introduction to economic forecasting. The course placesemphasis on the importance and practical uses of economic forecasting. It sheds light on evaluation and measurementof forecasting accuracy. Forecasting models are supported by data collection and analysis tools. The course providesgrasp of time series models and forecast methods, including white noise (WN), moving average (MA), autoregressive(AR), autoregressive and moving average (ARMA) models, as well as models with integrated time series, ARIMA. Timeseries models are identified using the Box-Jenkins methodology that requires information on autocorrelation and partialcorrelation functions. The course also revisits the main estimation methods, including ordinary least squares (OLS) andmaximum likelihood (ML) estimation. The impulse response functions are another important concept in time series

econometrics that helps to bridge the gap between estimation and forecasting. Multi-equation time series models – vector autoregressive (VAR) models – are also studied in the course. The course also addresses the issue of forecastingin the long term. Judgmental forecasting and behaviour of professional forecasters finalise the course.

Aims of the course

1. To provide students with the theoretical knowledge and practical skills necessary for the analysis of economic data bymeans of single-equation and multi-equation models of time series econometrics.

2. Based on time series econometrics, to introduce students to economic forecasting.

Subject learning outcomes (SLO) Study methods Assessment methods

SLO1. Identify, estimate, evaluate andunderstand single-equation and multi-equation time series models.

Theoretical sessions, practicalsessions, independent study,individual projects

Midterm examination, individualproject, final examination

SLO2. Apply various quantitative methods for economic forecasting



SLO3. Based on economic theory, constructeconomic forecasting models and describetheir structure



SLO4. Evaluate the accuracy of forecastingmethods



SLO5. Distinguish between short-term andlong-term forecasting



SLO6. Apply judgmental forecastingmethods, combine them with quantitativemethods



SLO7. Use an appropriate computationalsoftware package to estimate and forecasteconomic variables.

Practical sessions, independentstudy, individual projects

Individual project



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Teaching and learning methods

The course is entirely taught in English and is designed to achieve its aims through a combination of theoretical andpractical sessions, as well as individual projects and consultations. At the instructor’s discretion, in-class hours will beallocated to theoretical and practical sessions. Formal material is presented in theoretical sessions with practicaldemonstrations on computer during a practical session.

The teaching method consists of taking a simple and specific time series model and then gradually building towardsmore complex and general models. Students are encouraged to actively participate in lectures and in practical sessions,co-operate with other students, develop and implement new ideas through interactive communication and criticalthinking.

Students are expected to install computational software on their personal computers (laptops) before the coursecommences or during the opening day of the course. The computational software package GRETL (both for Windowsand for Mac OS X) is publicly available at http://gretl.sourceforge.net/. Practical sessions will be devoted to application of theory to real world data.

Quality assurance issues

The lecturer will strive to ensure a variety of teaching and learning methods, interim knowledge assessment and

discussions of individual and group work in class throughout the course. The feedback from students will always behighly valued and appreciated.

Cheating prevention

The teaching and testing methods are chosen taking into account the purpose of the minimization of cheatingopportunities. The course is based and promotes the value of integrity. Lack of academic integrity – such as plagiarism,copying another person’s work, the use of unauthorized aids on examinations, cheating, facilitating acts of academicdishonesty by others – will not be tolerated. For instance, there should be no suggestion that a student ’s individualproject is similar to that of other student. Consequences for violations range from zero mark given for the individualproject over failure of the course up to disciplinary measures for severe cases.

Weekly Course Content

Date TOPICSIN-CLASS SESSIONS READINGS

Theory Practice

Sep 31. Introduction to economic forecasting. Basicdefinitions. Forecasting problems.

1 Carnot, Ch. 1;Makridakis, Ch. 1.

Sep 32. Forecasting institutions. Data collection andanalysis. Short-term forecasting.

1 Carnot, Ch. 2-3.

Sep 3,4

3. Introduction to time series econometrics.Basic forecasting techniques. Forecasting withsimple and multiple regression. Evaluation andmeasurement of forecast accuracy.

4 Carnot, Ch. 4, 11;Enders, Ch. 2, pp. 81-97;Makridakis, Ch. 2-6.

Sep 3,4 Practice 1 2

Sep 5

4. White noise (WN) and moving average (MA)models. Identification, estimation anddiagnostic tests. Invertibility and stationarity.Impulse response functions. Forecasting withMA models. Economic applications of MAmodels.

2 Carnot, Ch. 4;Enders, Ch. 2;Makridakis, Ch. 7.

Sep 5 Practice 2 2

Sep 6,10

5. Autoregressive (AR) models. Identification,estimation and diagnostic tests. Invertibility andstationarity. Impulse response functions.Forecasting with AR models. AR models andmacroeconomics of business cycles.



http://gretl.sourceforge.net/






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Sep 11

6. Autoregressive and moving average (ARMA)models. Identification, estimation anddiagnostic tests. Impulse response functions.Forecasting with ARMA models. Economicapplications of ARMA models.


Sep 11 Practice 4 2

Sep 12,13

7. Time series models with trends.Deterministic and stochastic trends. TheRandom walk (RW) models. Unit root tests. Co-integration and spurious regression. ARIMAmodels. Forecasting with ARIMA models.Economic applications of ARIMA models.



Dec 17,18

8. Vector autoregressive (VAR) models.Identification, estimation and diagnostic tests.Stability. Impulse response functions. Granger causality. Forecasting with VAR models.Forecast error variance decomposition.

Economic applications of VAR models.

4 Carnot, Ch. 4;Enders, Ch. 5.

Dec 17,18 Practice 6 4

Dec 199. Macroeconomic forecasting. Buildingalternative scenarios. Medium- and long-termprojections. Financial and commodity markets.

3 Carnot, Ch. 6-8;Makridakis, Ch. 9.

Dec 19 Practice 7 1

Dec 20

10. Economic forecasts’ virtues and limitations.Judgmental forecasts. Behaviour of professional forecasters: do professionalforecasters herd?

3 Carnot, Ch. 12;Makridakis, Ch. 10-12;Pierdzioch.

Dec 20 Practice 8 1TOTAL 32 16

Course assignments and evaluation

TYPEDAYS TOTAL HOURS

EVALUATION, %

Midterm exam 1-8 32 30

Individual project 1-8 32 30

Final exam 1-12 48 40

Consultation 1-12 2

Total: 112 100

Assessments

1. Two-hour written final exam. The exam will consist of conceptual questions and problem solving. The exam counts40% towards the final mark. The remaining 60% of the final mark will be awarded based on the following:

2. Two-hour written midterm exam, which will cover approximately two thirds of the course. The exam will take place inthe middle of the course and will count 30% of the final mark. It will consist of conceptual questions and problem solving.

3. Individual project will count 30% of the final mark. There is no defence envisaged of individual projects. Theguidance notes and instructions will be released shortly before the course commences.

4. The instructor reserves the right to add up 5% to the final mark based on the contribution and professionalismexhibited by the student in class.

5. Negative marks of midterm examinations shall be dropped from students' records. Also midterm marks shall not berounded. In case of negative final mark, student is allowed to re-take exam. The re-take examination will consist of theory



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and problem-solving and will account for 70% of the final mark. Evaluation of the midterm and final exams are annulled.The retake exam will cover all topics presented in lectures, as well as, for all assigned chapters in the text.

Literature:

Obligatory readings:

1. Carnot, N., Koen, V., Tissot, B. (2005). Economic Forecasting. United Kingdom: Palgrave Macmillan.2. Enders, W. (2010). Applied Econometric Time Series. Hoboken, NJ: John Wiley and Sons, Inc.3. Makridakis, S., Wheelwright S.C., Hyndman, R.J. (1998) Forecasting Methods and Applications. New

York: John Wiley & Sons, Inc.Optional readings:

4. Box, G.E.P., Jenkins, G.M., Reinsel, G.C. (2008). Time Series Analysis: Forecasting and Control.Hoboken, NJ: John Wiley and Sons, Inc.

5. Carnot, N., Koen, V., Tissot, B. (2011). Economic Forecasting and Policy, Palgrave Macmillan.6. Clements, M.P., Hendry, D.F. (1998). Forecasting Economic Time Series. Cambridge: Cambridge

University Press.7. Clements, M.P., Hendry, D.F. (2002). A Companion to Economic Forecasting. Blackwell Publishing,

Ltd.8. Hamilton, J.D. (1994). Time Series Analysis. Princeton, NJ: Princeton University Press.9. Pierdzioch, C., Rulke, J.-C. (2012). Forecasting Stock Prices: Do Professional Forecasters Herd?

Economics Letters 116, 326 – 329.

The role of the subject in achieving the goals of Economics study programme

Special skills

EKO. To describe the main economic theories and theoretical models, to adapt them to the theoreticaland practical issues (for example, demand and supply elasticity theory, economic cycles, classicaleconomic theory, Keynesianism, monetarism, rational expectations theory, cost-benefit analysis);

SLO3

EKO. To explain the link between economic theory and practice, to apply that knowledge in assessing

statistical data and economic information on both micro- and macroeconomic level;

SLO2, SLO3,

SLO5, SLO7

EKO. To explain and to analyze the role of government in financial politics, to model the impact of government’s fiscal and monetary policy decisions on country’s economy and social welfare;

SLO3, SLO5

EKO. To describe and to analyze economic, legal, political and social environment of companies andother organizations; to identify and to evaluate internal and external changes and to make necessarydecisions to keep the firm’s or organization’s economic and financial stability;

SLO3, SLO5

EKO. To forecast the influence of economic and financial decisions not only in a financial context, but alsoin the wider context of business or organization management, to estimate the effect of economic andfinancial decisions on the processes of a company or an organization, the motivation of company’shuman resources, product and service quality, customer satisfaction, brand strength, corporate socialresponsibility;

SLO2, SLO3,SLO4, SLO5,SLO6

EKO. To select mathematical, statistical, econometric and other appropriate research methods and to

conduct individually a simple economic analysis of a country, a sector or a company (to collect, toorganize and to interpret the data).

SLO1, SLO2,

SLO3, SLO4,SLO5, SLO7

MNG. To be able to analyze a company or an organization as an integral unit, which strives for certaingoals in a market or social environment by effectively distributing their finite resources among objects andbusiness activities and obtains synergies from coordinated function planning, organization andmanagement;

MNG. To describe and to analyze financial management and financial decision making processes incompanies and other organizations, to solve problems of different levels of complexity in financial andmanagement accounting ;

FIN. To describe the basic financial theories and theoretical models, to adapt them to the theoretical andpractical issues (for example, time value of money, evaluation of investment projects, risk-returnrelationship, investment portfolio theory, stock and other securities pricing models, capital costs, riskmanagement, exchange rates, financial intermediaries);

FIN. To explain the link between financial theories and practice, to apply that knowledge in assessing thefinancial information (such as efficient markets hypothesis, anomalies, capital structure);



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FIN. To describe the functions of financial markets and institutions, and to analyze their activities;

FIN. To describe the main financial products and services, to assess their strengths and weaknesses fromboth the customer’s and the supplier’s perspective;

General skills

B1. To apply modern information technologies in the data gathering, analysis and communication; SLO1, SLO2,SLO3, SLO4,SLO7

B2. To apply a systematic, critical and constructive thinking in problem identification and solving;SLO3, SLO4,SLO5

B3. To be able to communicate well and express thoughts in writing and orally, both in English and nativelanguage; to communicate with specialists and non-professional audiences;

SLO1 - SLO7

B4. To prepare research papers according to proper language, writing style and general bibliographiccitation requirements;

SLO3

B5. To develop independent learning skills necessary to continue studies on a higher level; SLO1 - SLO7

B6. To communicate and to work effectively in an intercultural and interdisciplinary group or team.

B7. To know and to apply in practice certain aspects of various social sciences (history, geography,sociology, logics, philosophy, arts, etc.), to supplement effectively the education o f business or economicsby general knowledge.

Documents

ECO 108 Economic Forecasting Koreguota