Master's degree in Statistics and Operations Research

Time Series for Macroeconomics and Finance


poster_summer school_harvey -a3.jpgTítol del curs: Time Series for Macroeconomics and Finance
Impartit per: Andrew Harvey

Llengua del curs: Anglès

Dates i horaris del curs: 18-22 de juliol de 2011, de 10 a 12.30 h (el dia 18, de 10 a 13 h)

Lloc: 19 i 21 de juliol: aula PC3 // 18 i 22: aula 100 FME

Tipus d'activitat i càrrega lectiva: Curs de 10 hores

Reconeixement acadèmic: 1,5 crèdits

Data de matrícula: del 20 de juny al 10 de juliol de 2011



Course Objective:
The course will show how economic and financial time series can be modelled and analysed. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details. Statistical modelling will be demonstrated using the STAMP 8 package (http://www.stamp-software.com/) and participants will be given the opportunity to use the package in class.
Participants are expected to have taken an introductory course in econometrics or time series analysis. The references to Time Series Models (TSM) in the course outline give an indication of the material covered. The notes are shown as [TS*] and [FTS*]. Although these notes correspond to the slides, not all the material will be covered in detail during the lectures. Students should be familiar with most of the TS material. The FTS notes is more advanced (F stands for “Further”). Assessment will be made by exercises which will be done as homework.

Course Content:
  • Introduction. [TS1] Stationary time series. ARMA models. Prediction. Unobserved components and signal extraction. [TS2] TSM, chs 1,2,3. ARIMA models. Structural (unobserved components) time series models. Testing for nonstationarity. TSM, ch 5. [TS3] Explanatory variables and intervention analysis [TS4].
  • State space models and the Kalman filter. Signal extraction. Missing observations and other data irregularities. TSM, ch 4. [FTS1].
  • Spectral analysis. Spectra of ARMA processes; stochastic cycles; linear filters; estimation of spectrum. TSM, ch6, sections 1 to 7. [FTS2].
  • Trends and cycles. Analysis of the effects of moving average and differencing operations. Band-pass and Hodrick-Prescott filters. Seasonality. TSM, ch6, sections 5 and 6. [FTS3].
  • Multivariate time series models. Dynamic econometric models; common trends and co-integration; control groups. TSM, ch 7. [FTS5] Financial econometrics. Nonlinear models; distributions of returns, stochastic volatility and GARCH; nonlinear state space models. TSM, ch8,Taylor, chs 8-11, 15. [FTS4].
     
Main Texts 


Harvey, A. C., Time Series Models (TSM), 2nd Edition, HarvesterWheat-sheaf, 1993.


Selected bibliography:

Canova, F., Methods for Applied Macroeconomic Research. Princeton University Press, 2007.
 
Durbin, J. and S.J. Koopman, Time Series Analysis by State Space Methods. Oxford University Press, Oxford, 2001.
 
Harvey, A. C., Forecasting, Structural Time Series Models and the Kalman Filter (FSK), Cambridge University Press, 1989.
 
Hamilton, J. D., Time Series Analysis, Princeton University Press, 1994.
 
Koopman, S. J. et al., STAMP 8. Timberlake Consultants, 2007.
 
Maddala, G.S. and I-M. Kim, Unit roots, Co-integration, and Structural change. Cambridge: Cambridge University Press, 1998.
 
Mills, T. and R.N. Markellos, The Econometric Modelling of Financial Time Series, 3rd ed. Cambridge University Press, 2008.
 
Mills, T, Modelling Trends and Cycles in Economic Time Series. 2003. Palgrave.
 
Shephard, N. Stochastic Volatility. OUP, 2005.
 
Taylor, S. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2005.
 
Articles include:

Andersen, T.G., Bollerslev, T., Christo¤ersen, P.F. and F.X. Diebold. (2006). Volatility and correlation forecasting. Handbook of Economic Forecasting, edited by G Elliot, C Granger and A Timmermann, 777-878. North Holland. (5)
 
Baxter, M. and R. G. King (1999), Measuring business cycles: approximate band-pass Filters for economic time series, Review of Economics and Statistics, 81, 575-93. (4)
 
Harvey, A.C., (2006). Forecasting with Unobserved Components Time Series Models, Handbook of Economic Forecasting, edited by G Elliot, C. Granger and A. Timmermann, 327-412. North Holland. (1)
 
Harvey, A.C. (2011). Modelling the Phillips curve with Unobserved Components. Applied Financial Economics, special issue in honour of Clive Granger, 21, 7-17. (1,5)
 
Harvey, A.C. and G de Rossi. (2006). Signal Extraction. Palgrave Hand- book of Econometrics, vol 1, edited by K Patterson and T C Mills, 970-1000. Palgrave MacMillan. (2)
 
Harvey, A.C. and A. Jaeger (1993), Detrending, Stylized Facts and the Business Cycle, Journal of Applied Econometrics, 8, 231-47. (4)
 
Harvey, A.C. and Trimbur, T., (2003). General model-based …Filters for extracting cycles and trends in economic time series. Review of Economics and Statistics 85, 244-255. (4)
 
Hodrick, R. J. and E. C. Prescott (1997) Postwar US business cycles: an empirical investigation,” Journal of Money, Credit and Banking 24, 1-16. (4)
 
Lee, J. and C.R. Nelson (2007). Expectation Horizon and the Phillips 3 Curve: The Solution to an Empirical Puzzle, Journal of Applied Econometrics, 22, 161-178. (1,5)