EC820. Time Series Econometrics 2013/14

Transcription

1 EC820 Time Series Econometrics 2013/14

2 SCHOOL OF ECONOMICS EC820 Time Series Econometrics Staff Module convenor Office Keynes Bg.3 Professor Hans-Martin Krolzig Teaching information Teaching period Teaching pattern Hours of study Spring Term One two hour lecture/seminar/workshop per week Contact hours 24 Private study hours 126 Total study hours 150 Assessment Task Weighting Test/exam date Coursework submission date Computer Based Empirical Project 30% N/A Class Test 10% N/A Exam 60% May/June 2014 N/A Coursework submission policy All coursework must be submitted by the deadline stipulated by the module convenor, as listed above, to the School of Economics General Office, Mg.14 Keynes College. All coursework should be accompanied by a completed cover sheet. No extensions to submission deadlines are granted. If you miss the deadline and submit the coursework late, you must also submit a concessions form for late submission, available from the Social Sciences Faculty Office,

3 UNIVERSITY OF KENT AT CANTERBURY SCHOOL OF ECONOMICS SPRING TERM 2014 EC820 Empirical Macroeconomics Module Convenor: Professor Hans-Martin Krolzig PRELIMINARY MODULE OUTLINE 1 Introduction Since the probability theory revolution in econometrics, it has been standard to view economic time series, i.e., chronological sequences of observations, as realizations of stochastic processes. This approach allows the model builder to use statistical inference when estimating relationships between economic variables and testing hypotheses from economic theory. Analyzing the nature of time series and their description in some parametric statistical way are essential for empirical macroeconomic and financial modelling. Forecasting the macroeconomy and financial markets demands some knowledge of its structure. Designing and evaluating economic models often involves comparisons of their statistical implications against the true nature of time series such as inflation and growth. These issues will be considered from both a univariate and a multivariate perspective, but often the multivariate statistical models turn out to be straightforward extensions of the univariate ones. Most macroeconomic and financial time series follow a stochastic trend, so that temporary shocks have permanent effects. These time series are called nonstationary; they differ from stationary series which do not grow over time, but fluctuate around a given value. While statistical methods used for stationary time series can yield misleading results when applied to the analysis of nonstationary data, specific combinations of nonstationary time series may exhibit stationarity, thereby allowing for correct statistical inference. This phenomenon is called cointegration and is of special interest to this module. This module offers a research-oriented introduction to the econometric analysis of economic and financial time series. Students are introduced to the methods and models used in central banks, research institutions for the analysis of macroeconomic data for policy purposes and forecasting as well as in financial institutions for the analysis of financial data as the foundation to investment decision. This module aims to present a systematic and operational approach to econometric modelling, which combines the understanding of theories and techniques with their practical implementation for empirical research using econometric software. Students will also gain insight into contemporary empirical macro- and financial economics by linking the econometric theory to empirical studies of the macroeconomy and financial markets. The introduction to financial econometrics focussed on the statistical properties of low-frequency financial market data and econometric methods that can be used for their analysis. The focus is on the modelling and forecasting of the time-varying volatility of asset returns, covering the tools of financial econometrics a with a moderate degree of sophistication. Special emphasis is also here on applications by the student. The module can be taken as part of the following programmes of study: MSc in Economics - optional; MSc in Economics and Econometrics - optional; MSc in Economics and Finance - optional; MSc in Finance and Econometrics - optional; MSc in International Finance and Development - optional. The teaching on this module is based upon students having successfully completed the courses EC805 Advanced Macroeconomics and EC821 Econometric Methods. Some prior knowledge of time series analysis would prove useful but is not required (see below). 1

4 2 2 Aims Our aims in this module are: to explain and illustrate some past and more recent developments in the empirical macroeconomics; to build upon and extend the material introduced in EC805 Advanced Macroeconomics and EC821 Econometric Methods, such that students who intend to pursue doctoral studies in economics, or to work as professional economists after graduation, will have the necessary skills to analyse macroeconomic and financial data and to undertake econometric modelling; to develop students understanding and ability to apply econometric techniques for the modelling of macroeconomic and financial phenomena; to use an intuitive approach by use of practical examples and class based workshops using econometric packages such as PcGive and EViews; to give participants the ability to critically evaluate empirical literature and carry out empirical research. to enable students to understand, develop and apply a range of advanced macroeconometric techniques which they may wish to use in their dissertations; to produce graduate students trained in the theory and practice of modern time series econometrics. 3 Objectives and Learning Outcomes On completion of the module, students will: have a better understanding of econometric techniques, particularly those used in with time series data; be more critical in their reading and interpretation of their own empirical research; have the ability to undertake unsupervised practical work using (for example) PcGive and EViews; be practiced in analysing macroeconomic and financial time series using appropriate econometric techniques; be able to make oral presentations of empirical macroeconomic and financial economic work; have developed the ability to report applied econometric work in written form. The quantitative analysis of economic data and empirical evaluation of economic models are crucial to the study and application of modern economics. The ability to apply econometric methods also constitutes an essential part of modern postgraduate degree training in economics. 4 Skills The skills involved in the study at Masters level include literacy, numeracy, communication, presentation, logical argument, critical analysis of arguments, abstraction, decision-making and organizing your own work and learning. This module will emphasize to develop your quantitative and empirical skills around the weekly lectures, seminars, computer classes and assignments. In seminars there will be scope for working in groups, and improving your communication and presentation skills. If you need help in study skills you may ask for advice from the Module Convenor or get assistance from the Student Learning Advisory Service. The Economics Graduate Handbook gives information on support available through the Student Learning Advisory Service (SLAS), which is part of the Unit for the Enhancement of Learning and Teaching, and through the Centre for English and World Languages (CEWL). You should read this handbook carefully and make full use of these services. All students should visit the Student Advisory Service to see what it offers in terms of advice on essay writing, examination preparation, time management etc. You also need to think about how you can improve your own learning and outcomes, as at MSc level the emphasis is put on independent learning and time management. Personal Development Planning is now a part of the education system, which encourages record keeping on your academic and skill development in order to improve your employment prospects. The university has introduced a new facility called My Folio which has a more modern social networking interface to help you record all your activities and plan for the future, in particular career objectives. The link is

5 3 5 Module Arrangements The course will run in Spring Term for 12 weeks with the last week being reserved for a computer-based project Monday 12:00 14:00 tbc r Tuesday 16:15 18:00 tbc r The Tuesday sessions are seminars and computing workshops. The time will be flexibly divided into lectures, computing workshops, and seminars. The lectures introduce the module material and provide an overview of the principles of time series econometrics (22 hours). Applications of these techniques are conducted in computing workshops using real world data (5 sessions). The material taught in the lecture will be reviewed in seminars by tackling theoretical exercises and facilitating discussion (3 sessions). Students are expected to read the relevant literature and solve the relevant problems before coming to the seminar or lecture so they can actively participate in the discussion, which is an essential ingredient of this module. Students will be expected to undertake approximately 10 hours of private study per week. The total anticipated work load is 150 hours. Contact details for the module convenor, Hans-Martin Krolzig, are as follows: room: Keynes Bg3, phone: (82)7426, hm.krolzig@kent.ac.uk; office hours: Wednesday, 14:00 16:00. 6 Assessment and Examination The final mark for the module is comprised of 40% in-term coursework plus 60% end-of-year examination. The coursework is in two parts; a small computer-based empirical project (30% of the final module mark) and a class test (10% of the final module mark). The coursework assesses all the non-oral specific and generic learning outcomes. Each of the two assessments will test and develop most of the specific and general skills and learning outcomes. The econometric theory material taught in the lectures is reviewed by an unseen test in week 19. The computer-based exercise will involve computing using the econometric packages like Eviews or PcGive to undertake econometric modelling of a relevant applied macroeconomic issue, and will test students analytical skills, their ability to formulate methods of analysis to investigate macro level data, and their ability to deal with practical problems and demonstrate an understanding of relevant issues that arise in applied financial work. Each student will face a unique data set. You should submit the empirical project to the Economic General Office, Keynes Mg14, together with the appropriate essay cover sheet at the end of the first week of Spring break. An end-of-year two-hour examination will consist of theoretical and applied exercises. The theoretical part reconsiders the econometric theory material taught in the lectures and assessed in the class test. The applied part requires students to discuss empirical estimation and test results produced by the computational software used throughout the module within the conceptual context of the module. The examination is designed to test and develop the non-computing skills and learning outcomes.

6 4 7 Reading list Time series econometrics is a rapidly expanding area of econometric theory and application. While there will be no single text which covers the whole module, you will find the following econometric textbooks useful: Enders, W. (2009) Applied Economics Time Series. New York: Wiley. 3rd edition. Chapters 1 2,4 6. Useful guide to empirical modelling. Hamilton, J.D. (1994) Time Series Analysis. Princeton: Princeton University Press. Chapters 1 5, 7 8, 10 11, 15. Handbook-style reference to time series models and econometric methods. Hendry, D.F. (1995) Dynamic Econometrics. Oxford: Oxford University Press. Chapter 8,9. Johansen, S. (1995) Likelihood-based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press. Lütkepohl, H. (2006) New Introduction to Multiple Time Series Analysis. New York: Springer. Chapters 2, 3. The graduate textbooks of Hamilton and Enders will be of greatest use for this course, but given time constraints only a subset of the material presented in these book can be discussed. Good textbooks on advanced macroeconometrics include: Bårdsen, G., Ø. Eitrheim, E. S. Jansen, and R. Nymoen (2004), The Econometrics of Macroeconomic Modelling, Oxford: Oxford University Press. Favero, C.A. (2001) Applied Macroeconometrics. Oxford: Oxford University Press. Chapters 2 6. Good but not perfect introduction to empirical macroeconomics. Juselius, K. (2006) The Cointegrated VAR Model: Econometric Methodology and Macroeconomic Applications, Oxford: Oxford University Press. A good start is to read the seminal paper by Chris Sims: Sims, C.A. (1980)Macroeconomics and Reality, Econometrica, 48, There is a great number of text books on financial econometrics. Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997), The Econometrics of Financial Markets, Princeton: Princeton University Press. Cochrane, J. (2001), Asset Pricing, Princeton: Princeton University Press. Franses, P.H., and D. van Dijk (2000), Non-linear time series models in empirical finance, Cambridge: Cambridge University Press. Gourieroux, C., and J. Jasiak (2001), Financial Econometrics: Problems, Models and Methods, Princeton: Princeton University Press. Mills, T.C. (1999), The Econometric Modelling of Financial Time Series, Cambridge: Cambridge University Press. Taylor S.J. (2005), Asset Price Dynamics, Volatility, and Prediction, Princeton: Princeton University Press. Tsay, R.S. (2005), Analysis of Financial Time Series, New Jersey: John Wiley & Sons, 2nd edition. Students are recommended to start with two excellent surveys on financial econometrics by and Rob Engle, the founder of financial econometrics, and Adrian Pagan (more technical): Engle, R. (2001), GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics, Journal of Economic Perspectives, 15, Pagan, A. (1996), The econometrics of financial markets, Journal of Empirical Finance, 3, Lecture notes will be supplied for each topic. These will summarize the discussion in the lectures rather than offering complete accounts. The notes and details of the specific reading for each separate element of the module will be handed out during the course of the lecture. As this is a research oriented course, supplementary reading to the lecture notes will not only be drawn from the text books above, but also from research papers. Copies of the lecture notes, assignments, research papers and data will be made available before each lecture on Moodle.

7 5 8 Module structure 1. Introduction to univariate time series analysis Financial & macroeconomic time series and their characteristics; Stochastic processes; Linear time series models: Autoregression and Moving Average; Nonstationarity processes; Predicting stochastic processes; Estimating and testing time series models. 2. Introduction to financial econometrics Time-varying Volatility Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Value-at-Risk. 3. Introduction to macroeconometrics Simultaneous equation models and their critique; Vector autoregressions and their structural analysis; Cointegration and vector equilibrium correction. HMK/