COURSE INFORMATION Course Title: TIME SERIES IN ECONOMETRICS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
ECO 402	B	2	3	0	0	3	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Fatbardha Morina fmorina@epoka.edu.al , By appointment
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Master of Science in Banking and Finance
Classroom and Meeting Time:	E212 18:00 - 20:45
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	75 %
Course Description:	The course consists of econometric models which are employed in time series analyses. It covers the topics such as time series regression, exploratory data analysis, ARMA/ARIMA models, model identification/estimation/linear operators, GARCH family models, Co-integration, VAR analysis, Causality, Panel Data analyses. The Analyses are performed using E-views software program.
Course Objectives:	- to teach applied Time Series methodologies with an emphasis on model building and accurate prediction in order to apply to real life situations. - to provide the ability to bring together and flexibly apply knowledge to characterize, analyses and solve a wide range of social, economic and scientific problems. - to provide students skills required to research in economics and finance with exposure to more advanced econometric practices and models.

BASIC CONCEPTS OF THE COURSE

1	Autoregressive Process of Order One [AR(1)]: A time series model whose current value depends linearly on its most recent value plus an unpredictable disturbance.
2	Durbin-Watson (DW) Statistic: A statistic used to test for first order serial correlation in the errors of a time series regression model under the classical linear model assumptions.
3	Seasonality: A feature of monthly or quarterly time series where the average value differs systematically by season of the year
4	Unit Root Process: A highly persistent time series process where the current value equals last period’s value, plus a weakly dependent disturbance.
5	Serial Correlation-Robust Standard Error: A standard error for an estimator that is (asymptotically) valid whether or not the errors in the model are serially correlated.
6	Breusch-Godfrey Test: An asymptotically justified test for AR(p) serial correlation, with AR(1) being the most popular; the test allows for lagged dependent variables as well as other regressors that are not strictly exogenous
7	Exogenous Explanatory Variable: An explanatory variable that is uncorrelated with the error term.
8	Endogenous Variables: In simultaneous equations models, variables that are determined by the equations in the system.
9	Feasible GLS (FGLS) Estimator: A GLS procedure where variance or correlation parameters are unknown and therefore must first be estimated.
10	Error Correction Model: A time series model in first differences that also contains an error correction term, which works to bring two I(1) series back into long-run equilibrium

COURSE OUTLINE

Week	Topics
1	Introduction
2	Chapter 10: 10.1 The Nature of Time Series Data pg. 344. 10.2 Examples of Time Series Regression Models pg. 345 Static Models pg.346 Finite Distributed Lag Models pg. 346 A Convention about the Time Index pg.349
3	Chapter 10: 10.3 Finite Sample Properties of OLS under Classical Assumptions pg.349 Unbiasedness of OLS pg.349 The Variances of the OLS Estimators and the Gauss-Markov Theorem pg.352 Inference under the Classical Linear Model Assumptions pg.355 10.4 Functional Form, Dummy Variables, and Index Numbers pg. 356
4	Chapter 10: 10.5 Trends and Seasonality pg.363 Characterizing Trending Time Series pg.363 Using Trending Variables in Regression Analysis pg.366 A Detrending Interpretation of Regressions with a Time Trend pg.368 Computing R-Squared when the Dependent Variable Is Trending pg.370 Seasonality pg.371
5	Chapter 11: 11.1 Stationary and Weakly Dependent Time Series pg.381 Stationary and Nonstationary Time Series pg.381 Weakly Dependent Time Series pg.382 11.2 Asymptotic Properties of OLS pg.384 11.3 Using Highly Persistent Time Series in Regression Analysis pg.391 Highly Persistent Time Series pg.391 Transformations on Highly Persistent Time Series pg.395 Deciding Whether a Time Series Is I(1) pg.396
6	Chapter 11: 11.4 Dynamically Complete Models and the Absence of Serial Correlation pg.399
7	Chapter 11: 11.5 The Homoskedasticity Assumption for Time Series Models pg.402
8	Midterm Exam
9	Chapter 12: 12.1 Properties of OLS with Serially Correlated Errors pg.412 Unbiasedness and Consistency pg.412 Efficiency and Inference pg.413 Goodness-of-Fit pg.414 Serial Correlation in the Presence of Lagged Dependent Variables pg.415 12.2 Testing for Serial Correlation pg.416 A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors pg.416 The Durbin-Watson Test under Classical Assumptions pg.418 Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors pg.420 Testing for Higher Order Serial Correlation pg.421 12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors pg.423 Obtaining the Best Linear Unbiased Estimator in the AR(1) Model pg.423 12.4 Differencing and Serial Correlation pg.429 12.5 Serial Correlation-Robust Inference after OLS pg.431 12.6 Heteroskedasticity in Time Series Regressions pg.434 Heteroskedasticity-Robust Statistics pg.435
10	Chapter 18: 18.1 Infinite Distributed Lag Models pg.633 The Geometric (or Koyck) Distributed Lag pg.635 Rational Distributed Lag Models 637 18.2 Testing for Unit Roots pg.639
11	Chapter18: 18.3 Spurious Regression pg.644
12	Chapter 18: 18.4 Cointegration and Error Correction Models pg.646 Cointegration pg. 646 Error Correction Models pg. 651
13	Chapter 18: 18.5 Forecasting pg. 652 Types of Regression Models Used for Forecasting pg. 654 One-Step-Ahead Forecasting pg. 655 Comparing One-Step-Ahead Forecasts pg. 658 Multiple-Step-Ahead Forecasts pg. 660 Forecasting Trending, Seasonal, and Integrated Processes pg. 662
14	Review

Prerequisite(s):	Introductory course in statistics and econometrics.
Textbook(s):	Wooldridge J.M.(2015). Introduction to Econometrics. Cengage Learning
Additional Literature:	Vogelvang B. (2015). Econometrics Theroy and Applications with E-views. Printice Hall.
Laboratory Work:	Yes
Computer Usage:	E-views software program
Others:	No

COURSE LEARNING OUTCOMES

1	Provide skill to apply economic models in econometrics
2	Provide skill to set up econometric models
3	Provide ability for testing specification of the model
4	Provide skill to apply time series econometric methods in academic research

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Master of Science in Banking and Finance Program
1	The students are gained the ability to look at the problems of daily life from a broader perspective. They gain the needed skills not only to understand economic problems in banking and finance but also to construct a model and defend in meaningful way.	4
2	They have knowledge about the finance and banking.	4
3	They have knowledge about the money and banking.	4
4	They have knowledge about the international finance and banking.	4
5	They have ability to use mathematical and statistical methods in banking and finance.	5
6	They know how to use computer programs in both daily office usage and statistical data evaluations in banking and finance department.	5
7	They have necessary banking and finance skills that needed in private and public sector.	2
8	They are intended to be specialist in one of departmental fields that they choose from the list of general economics, finance economics, public finance, corporate finance, finance management, international finance markets and institutions, banking and central banking, international finance and banking, money and banking, international trade and banking.	2
9	They have ability to utilize fundamental economic theories and tools to solve economic problems in banking and finance.	5
10	They are aware of the fact that banking and finance is a social science and they respect the social perspectives and social values of the society’s ethics.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Project	1	20
Final Exam	1	40
Other	1	10
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	5	80
Mid-terms			0
Assignments	1	29.5	29.5
Final examination			0
Other	1	30	30
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5

CONCLUDING REMARKS BY THE COURSE LECTURER

N/A