Even though the course is self-contained, statistics and econometrics at an advanced undergraduate level are recommended. Computer applications will be based on GAUSS or MATLAB but no prior knowledge of this specific software is required because it will be quickly taught at the beginning.

• El alumno debe ser capaz de leer razonadamente y evaluar de forma crítica artículos de investigación en Econometría, captando cuáles son las contribuciones esenciales y las debilidades de cada uno de ellos. Además, el alumno debe ser capaz de entender cómo se han resuelto en cada caso los problemas técnicos con los que se han tenido que enfrentar los autores de estos artículos.
• El alumno debe aprender a realizar pruebas de proposiciones y teoremas.
• El alumno también debe ser capaz de comprender y replicar los análisis empíricos y los experimentos de simulación en que se basan las conclusiones presentadas en los artículos de investigación de otros autores.

The course is intended to provide participants with the necessary tools to apply state of the art techniques for the forecasting of macroeconomic and financial variables and turning points, with special emphasis on recent developments in “nowcasting” The course covers the required econometric theory but with the intention of putting it into practice in specific forecasting situations. Students will receive computer codes that exactly match the techniques covered in class in order to guarantee their applicability to real data.

Each session starts with the presentation of a forecasting technique, followed by the review of the econometric theory required for its analysis, and the detailed explanation of computer programs that can be used to obtain the forecasts. The syllabus covers a wide range of forecasting problems and linear and non-linear econometric methods, but it is designed to be self-contained.

Session 1

Review of things that you are supposed to know, Autoregressive Models, VAR models and cointegration.

Introduction to MATLAB

Session 2

Model selection: Small scale models; dynamic factor models. Common factors, principal components and Kalman filter. Output gap and coincident indicators.

Session 3

Nowcasting and real time forecasting. Incorporating real time information in forecasting models. Real-time out-of-sample evaluation of the models. Data revisions and publication lags. Hard indicators vs indicators based on surveys. Non-seasonally adjusted series. Construction of daily business cycle indicators and forecasts with different models.

Session 4 and 5

Midas, Bridge Equations and Mix-Frequency VAR

Session 6 and 7

Forecasting turning points. Non-linear methods. Markov switching and threshold models, Univariate and multivariate analysis. Dynamic non-linear factor models. Non parametric models. Real-time assessment of recession probabilities with unbalanced information. Leading indicators of turning points. Non-linear real-time models for the US, the Euro-area and other economies. Forecasting stock returns. Forecasting second, third and fourth moments of stock returns. Credit and the business cycle. Forecasting amplitudes, durations, and shapes of recessions and expansions. The role of macroeconomic and financial variables in forecasting amplitudes, durations, and shapes of recessions and expansions. Recent pitfalls in the literature. Real-time tests of structural breaks.

Session 8                                 

Forecasting using large scale models. Advantages and disadvantages of large scale models. Principal components. Dynamic principal components.

 Bibliography

1.) James Hamilton. “Time Series Analysis” Princeton. (1994). Chapter 13.

2)  Chang-Jin Kim, Charles R. Nelson. “State-Space Models With Regime Switching: Classical and Gibbs-Sampling Approaches With Applications”. MIT Press (1999). Chapter 2.

3) Clark, Peter K. (1987) “The Cyclical Component of US Economic Activity” Quarterly Journal of Economics, 102, 797-814   

4). Clark, Peter K. (1989) “Trend Reversion in Real Output and Unemployment” Journal of Econometrics, 40, 15-32 5). Kuttner, Ken (1996) “Estimating Potential Output as a Latent Variable” Journal of Business and Economics Statistics, Vol 12, Num 3 361-381

6) Stock, James and Mark Watson (1991) “A probability model of the Coincident Economic Indicators” In Leading Economic Indicators: New Approaches and Forecasting Records, ed. K. Lahiri and G.H. Moore. Cambridge. Cambridge University Press, 63-89

7) Aruoba, B., F. Diebold and C. Scotti (2008). Real-time measurement of business conditions, PIER Working Paper No. 08-011, Department of Economics, University of Pennsylvania

8) Maximo Camacho and Perez Quiros, Gabriel: “Introducing the Euro-Sting: Euro Area Short Term Indicator of Growth” Journal of Applied Econometrics 2010

9) Maximo Camacho, Marcos dil Bianco and Perez Quiros, Gabriel:Short-Run Forecasting of the Euro-Dollar Exchange Rate with Economic Fundamentals  Journal of International Money and Finance Vol 31 Issue 2 March 2012 pp 377-396.

10) Maximo Camacho and Perez Quiros, Gabriel:Spain-STING: España Short Term Indicator of Growth  The Manchester School.  The Manchester School,  79: 594-616., 2011

11) Jörg Breitung Sandra Eickmeier “Dynamic factor models“ Discussion Paper Series 1: Economic Studies No 38/2005

12) James Stock. “Dynamic factor models“. (2010) Mimeo

13).Stock, J., Watson, M. (2002) Macroeconomic Forecasting Using Difusion Indexes. Journal of Business and Economic Statistics 20: 147-162.

14)  Camacho, M. and Sancho, I. “Spanish Diffusion Indexes” Span. Econ. Rev. 5, 173–203 (2003)

15) Giannone, D. Reichlin, L and Small, D. 2008. Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics 55: 665-676.

16).  Barhoumi, K, Benk, S., Cristadoro, R. Ard Den, Jakaitiene, A. Jelonek, P. Rua, A. Runstler, G. Ruth, K. Van Nieuwenhuyze, C. “Short Term Forecasting og GDP using large monthly datasets: A Pseudo Real Time Forecast Evaluation Exercise. ECB Occasional Paper Series, N 84, April 2008.

17) . Angelini, E., Camba-Mendez, G., Giannone, D., Reichlin, L., and Runstler, G. 2008. Short-term forecasts of Euro area GDP growth. CEPR discussion paper No. 6746.

18). Boivin, J., and Ng, S. 2006. Are more data always better for factor analysis? Journal of Econometrics 132: 169-194.

19). Bai, J., and Ng, S. 2008. Forecasting economic time series using targeted predictors. Journal of Econometrics 148: 304-317.

20) Alvarez. Camacho y Perez Quiros (2011) Finite sample performance of small versus large scale dynamic factor models

21) Banbura, Marta and Michelle Modugno (2010)  Maximum Likelihood estimation of factor models on data sets with arbitrary pattern of missing data. ECB Working paper series

22) James H. Stock & Mark W. Watson, 2010. "Estimating Turning Points Using Large Data Sets," NBER Working Papers 16532, National Bureau of Economic Research

23) Hamilton, James (1989) “ A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle” Econometrica, 57: 357-384

24). Perez Quiros, Gabriel and Allan Timmermann (2000) “Firm Size and Cyclical Variations in Stock Returns” The Journal of Finance, Vol 55, Number 3. June 2000.

25) Perez Quiros, Gabriel and Allan Timmermann “Business Cycle Asymmetries in Stock Returns: Evidence from Higher Order Moments and Conditional Densities” Journal of Econometrics, Vol. 103 1-2. July 2001

26) McConnell Margaret, and Perez Quiros, Gabriel. (2000) “Output Volatility in the US: What has Changed Since the Early 80s?.American Economic Review Vol 90, Num 5 December 2000.

27) Bengoechea, P. ,Camacho M. y Pérez Quirós, G. “A useful tool to forecast the Euro-area Business Cycle Phases” International Journal of Forecasting, 22, 2006, pp. 735-749

28) Kim, Chang-Jin and Charles Nelson. (1999). “Friedman´s Plucking Models of Business Fluctuations: Tests and Estimates of Permanent and Transitory Components. Journal of Money Credit and Banking, Vol 31, No 3 pp 317-334

29) Maximo Camacho, Gabriel Perez Quiros and Pilar Poncela Green Shoots in the Euro area. A real time approach. Forthcoming International Journal of Forecasting

30). Chauvet, M., and Piger, J. 2008. A comparison of the real-time performance of business cycle dating methods. Journal of Business and Economic Statistics 26: 42-49.

31) Maximo Camacho, Gabriel Perez Quiros and Pilar Poncela “Real time Common Factor Markov Switching Models” Mimeo

32) Stock and Watson. Indicators for dating business cycles: Cross-History Selection and Comparison. American Economic Review May 2010

33) Hamilton, James  “Calling Recessions in Real Time”. International Journal of Forecasting

34) Hamilton, James  What’s Real About the Business Cycle. Federal Reserve Bank of St. Louis Review, July/August 2005, 87(4), pp. 435-52.

35) Gali Jordi. Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?. American Economic Review. March 1999

36) Maximo Camacho and Perez Quiros, Gabriel (2002) “This is What the Leading Indicators Lead” Journal of Applied Econometrics, 17, 61-80.

37). Harding, D. and A. Pagan (2002a). Dissecting the cycle: a methodological investigation. Journal of Monetary Economics 49: 365-381.

38) Potter, Simon  (1995) “A Nonlinear Approach to US GNP” Journal of Applied Econometrics, Vol 10.(7)

39). Teräsvirta, T. (1994). “Specification, estimation and evaluation of smooth transition autoregressive models”. Journal of the American Statistical Association 89: 208-18.

40) Drehmann Mathias and Michael Juselius, 2012. "Do debt service costs affect macroeconomic and financial stability?" BIS Quarterly Review, September.

41) Gourinchas, Pierre-Olivier. and Maurice Obstfeld. 2011. "Stories of te Twentiech Century for the Twenty-First." American Economic Journal: Macroeconomics (forthcoming).

42) International Monetary Fund. 2009. "From recession to recovery: how soon and how strong?" In World Economic Outlook.

43) International Monetary Fund. 2010. "The IMF-FSB Early Warning Exercise. Design and Methodological Toolkit" September 2010.

44) Jorda, Oscar, Moritz Schularick and Alan M. Taylor. 2011a. "When credit bites bak: leverage, business cycles and crises." NBER Working Paper Series 17621.

45) Jorda, Oscar, Moritz Schularick and Alan M. Taylor. 2011b. "Financial crises, Credit Booms, and External Imbalances." IMF Economic Review (forthcoming).

46) Gadea, Lola and Perez Quiros, Gabriel The failure to predict the Great Recession. A view through the role of credit. 2014

47) Forecast Evaluation and Combination. Frank Diebold and Jose Lopez. 1995. Federal Reserve Bank of New York WP 9525

48) Comparing Predictive Accuracy. Frank Diebold and Roberto Mariano Journal of Business & Economic Statistics, Vol. 13, No. 3, (Jul., 1995), pp. 253-263 (8)

49) "Nonlinear Time Series Models in Empirical Finance" Phillip Franses and Dick van Dick, June 2000  Cambridge University Press.

Students will receive 4 homeworks and they have to hand them in due time. The homeworks are graded, and the students will have to discuss their solutions in class. These homeworks will count for 50% of the grade. The other 50% will be a final exam. The final exam can be retaken in July.