Checking date: 29/04/2019


Course: 2019/2020

Quantitative Macroeconomics
(13665)
Study: Bachelor in Economics (202)


Coordinating teacher: ESCRIBANO SAEZ, ALVARO

Department assigned to the subject: Department of Economics

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Basic courses of Economics (Microeconomics and Macroeconomics) and Econometrics
Competences and skills that will be acquired and learning results. Further information on this link
This is an empirical macroeconomic course. The student will become familiar with univariate macroeconomic modeling, analyzing macroeconomic relationships using time series data. The material taught in this course will lead the student to acquire the ability to use basic econometric programs (EVIEWS, GRETEL) for univariate time series data (ARIMA), for single equation models (ARDL) and multiple equations (VAR models) stationary and nonstationary (Cointegration). These abilities will give the student the capacity to construct empirical economic models and to test macroeconomic hypotheses based on econometric models. These models are commonly used in macroeconomics and finances, in particular, those related to business cycles (booms and recessions), nonlinear models and determinants of economic growth. Other skills that the student will acquire include familiarity with methods of analysis of the current state of the economy that is useful to interpret the movements of macroeconomic aggregates and of sectors in market economies.
Description of contents: programme
Part I: Univariate analysis of macroeconomic time series I.1 Univariate Models I.1a Evolution & decomposition of univariant time series - Stationary and non-stationary variables. Integrated processes, random walks, martingales and unit root testing (Dickey-Fuller) - Transformations of variables (logarithms & differencing) - Trend and cyclical properties of macroeconomic variables - Trend-Cycle decompositions: Beveridge-Nelson (BN) and the Hodrick- Prescott (HP) filter - ARIMA Models: Impulse Response Functions and Forecasting Empirical Applications: - International evolution of income per capita and it's components - Evolution of macroeconomic aggregates - Purchasing power parity (PPP) - Descriptive and graphical analysis of the current state of the economy - The efficiency of financial markets, etc. I.1b Non-linearity and Stationarity - Seasonal filters, seasonally adjusted variables - Non-linearity in parameters vs. Non-linearity in regressors, - Structural change in the parameters and threshold variables - Smooth Transition Autoregressive Models (STAR) - Autoregressive Models with Conditional Heteroskedasticity (ARCH, GARCH) - Non-linearity in the mean versus non-lineality in the variance Empirical Applications: - Modeling of energy prices in centralized markets (asymmetries and volatility) - Asymmetries in the increases and decreases in oil prices etc., Rockets and Feathers hypothesis, etc. - Modeling inflation and its volatility - Modeling of financial assets and their volatility I.2 Single Equation Models I.2a Specification and comparisons of single equation models - Estimation & inference in static and dynamic regression models - Specification of models from general to particular - Specification testing: Consistency and nested models - Exogeneity & Causality: Concepts & tests - Error Correction Models (EC or EqCM) & Co-integration - Spurious regression & cointegration Empirical Applications: - Micro-fundamentals of single-equation specification - Production functions and growth accounting - Determinants of growth - Demand for Money in the UK (1878-1970) - Hypothesis testing of finance models (CAPM), etc. I.2b Non-linear single-equation Models - Estimation & inference in dynamic non-linear regression models - Non-linear error correction models (NEC) - Smooth transition regression models (STR) & structural change Empirical Applications: - Money Demand in the UK (1878-1970) - Inflation & Unemployment: The Phillips Curve Part II: Analysis of Mulitple Equation Models II.1 The Vector Autoregression (VAR) model a) Stationary Case: - Structural form (SVAR) vs. Reduced form (VAR): Identification - VMA Representation (Wold) & Impulse Response Functions - Variance Decomposition and Forecasting - Formulation, estimation, diagnostics, selection of lag length. - The SVAR model, weak and strong exogeneity, Granger causality, the Lucas¿s critic, super-exogeneity b) Non-stationary case without cointegration: - Multivariate trend-cycle decomposition of Beveridge-Nelson (BN) - Structural Vector Autoregression (SVAR) with I(1) & I(0) variables: Identification by use of long-run restrictions Empirical applications: -Analysis of the New York Fish Markets (Fulton): System of equations of supply and demand. -Testing long-run neutrality -Blanchard and Quah model with long-run restrictions: GDP and Unemployment c) Non-stationary case with cointegration: - Multivariate trend-cycle decomposition of Beveridge-Nelson (BN) & the representation of common trends - Error Correction Mechanism & analysis of co-integration: Granger´s Representation Theorem - Multivariate time series models/Vector Error Correction Models (VEqCM) - The Maximum-Likelihood approach of Johansen for the estimation of the rank of cointegrated systems Empirical application -Money Demand Part III: Student´s Empirical Project
Learning activities and methodology
The teaching method will be the following: (1) Magistral classes, where the concepts will be developed in detail and the properties of macroeconomics models of time series will be covered. To facilitate understanding and learning of this material by the student, the students will have access to the class material (slides etc.) via the internet. They will also receive an ample list of complementary materials that will permit them to understand and go deeper into issues covered in class, and into some related issues of interest that may not have been covered in class. (2) Discussion of the exercises done by the student, covering the estimation and specification of classic models in the literature, previously covered in class, such as the various exercises of estimation and forecasting with time series in various economies and different time periods. (3) Comments on current economic issues to which the student can use the knowledge acquired in the course to deepen their understanding. (4) Practical classes in reduced groups where the students will learn to make arguments and reason in public, to use the necessary econometric programs (above all E-Views) to do estimation and testing of macroeconomic models of time-series. This will be done by the use of both algebraic and empirical exercises in class, with an emphasis on the applied nature of this course. (5) Complete an empirical project by the end of the course that demonstrates that the student understands how to apply with rigor and economic reasoning the econometric techniques studied. The project should be well written and have the basic structure of a short scientific article: Introduction, literature review, model and estimation, description of the data used and their quality, empirical results, evaluation of the model and hypothesis tests, conclusions & future extensions. Every student should give a formal oral presentation (in Power Point) of their empirical project in front of all students of the class and the professor.
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • ENDERS W. (2015).. APPLIED ECONOMETRIC TIME SERIES (4 ED.). JOHN WILEY. 2015
  • HENDRY D.F. AND NIELSEN B. . ECONOMETRIC MODELLING: A LIKELIHOOD APPROCH.. PRINCETON UNIVERSITY PRESS.. 2007
  • Hendry D.F (2015). . Introductory Macro-econometrics: A New Approach. . Timberlake Consultants Ltd. London SE26 5BN, UK. http://www.timberlake.co.uk/intromacroeconometrics. 2015
  • MILLS T.C. AND MARKELLOS R.N.. THE ECONOMETRIC MODELLING FOR FINANCIAL TIME SERIES (3RD ED.). CAMBRIDGE UNIVERSITY PRESS. . 2010
  • PATTERSON K. . AN INTRODUCTION TO APPLIED ECONOMETRICS: A TIME SERIES APPROACH, . PALGRAVE.. 2000
Additional Bibliography
  • BARRO R.J. (2001).. DETERMINANTS OF ECONOMIC GROWTH: A CROSS-COUNTRY EMPIRICAL STUDY.. THE MIT PRESS, CAMVBRIDGE, MASSACHUSETTS..
  • ESCRIBANO A. (2004).. NONLINEAR ERROR CORRECTION: THE CASE OF MONEY DEMAND IN THE UK (1878-2000).. MACROECONOMIC DYNAMICS. 2004, 8, 76-116..
  • ESCRIBANO A. J.I. PEÑA AND VILLAPLANA P. (2011).. MODELING ELECTRICY PRICES: INTERNATIONAL EVIDENCE, . OXFORD BULLETIN OF ECONOMICS AND STATISTICS.
  • FRANSES PH. H. AND VAN DIJK D. (2000).. NON-LINEAR TIME SERIES MODELS IN EMPIRICAL FINANCE,. CAMBRIDGE UNIVERSITY PRESS..
  • HENDRY D. F. (2008).. EQUILIBRIUM-CORRECTION MODELS. THE NEW PALGRAVE DICTIONARY OF ECONOMICS ONLINE (2ND ED.). EIDTED BY S. DURLAUF AND L.E. BLUME..
  • JONES CH. I. (2002).. INTRODUCTION TO ECONOMIC GROWTH (2ND ED.),. W.W.NORTON & COMPANY, NEW YORK, LONDON.
  • JUSELIUS K. (2006).. THE COINTEGRATED VAR MODEL: METHODOLOGY AND APPLICATIONS,. OXFORD UNIVERSITY PRESS..
  • PEÑA D. (2005).. ANÁLISIS DE SERIES TEMPORALES,. ALIANZA EDITORIAL..
  • WOOLDRIDGE J. (2006),. INTRODUCTORY ECONOMETRICS: A MORDER APPROACH (3RD ED.),. NEW YORK: SOUTH-WESTER COLLEGE PUBLISHING..
  • ZIVOT E. (2000).. NOTES ON STRUCTURAL VAR MODELING.. JUNE.

The course syllabus and the academic weekly planning may change due academic events or other reasons.