UCLM Univesidad de Castilla-La Mancha


Diego J. Pedregal

Full Professor at the Industrial Engineering Politecnic of Ciudad Real (Spain)
Department of Business Administration at the Castilla-La Mancha University

Languaje: Es En        
Foto Diego Pedregal

First degree in Economics from the Universidad Autónoma de Madrid (UAM, June 1991).

IV M.A. in Public Finance and Economic Programming from the Institute of Fiscal Studies (June 1992).

PhD in Economics in April 1995 from the UAM, from the Economic Analysis department.

Associate Research position at Lancaster University (UK) during a post doc from September 1994 to October 1997.

Ex-member of the Systems and Control Group of the Centre of Research on Environment and Statistics (CRES) at Lancaster University.

Assitant professor at the Universidad Autónoma de Madrid and Banco del Comercio staff (BBVA holding) from October 1997 until October 1999.

Professor in the Industrial Engineering Politecnic at the Castilla-La Mancha University since 15/12/2000, assistant professor since 14/04/2002, full professor since 13/05/2009.

TOPICS OF INTEREST: time series analysis, econometrics, state space modelling, dynamic system identification, forecasting.





  • García, F.P. and Pedregal, D.J., (2011), "Digital filters for Maintenance Management", in: F.P. García (ed.), Digital Filters, InTech, Fijeka, Croatia.
  • Pedregal, D.J., Contreras, J., and Sánchez, A. (2010), "ECOTOOL: A general MATLAB Forecasting Toolbox with applications to Electricity Markets", in: P.M. Pardalos, M.V.F. Pereira, N.A. Iliadis, S. Rebennack and A. Sorokin(eds), Handbook of Networks in Power Systems, Springer Verlag.
  • Young, P.C., Pedregal, D.J. (2004), 'Environmental forecasting'. Encyclopedia of Environmetrics, editado por A.H. El-Shaarawi and W.W. Piegorsch (J. Wiley). http://www.wiley.com/legacy/wileychi/eoenv/pdf/Vaf007-.pdf
  • Pedregal, D.J., Young, P.C(2002), 'Statistical Approaches to Modelling and ForecastingTime Series'. En Clements, M. and Hendry, D. (eds.), Companion to Economic Forecasting (Blackwell Publishers), 69-104.
  • Young, P.C., Pedregal, D.J. (1997), 'Data-Based Mechanistic Modelling'. C. Heij et al. (eds.), System Dynamics in Economic and Financial models (J. Wiley, Chichester), 169-213.
  • Young, P.C., Pedregal, D.J. (1997), 'Comments on "Multivariate Structural Time Series Models" ' by A. Harvey and S. J. Koopman. C. Heij et al.(eds.), System Dynamics in Economic and Financial models(J. Wiley, Chichester), 291-293.


  • Castillo, J.I., López, L. and Pedregal, D.J. (in press), 'How can the effects of the introduction of a new airline on a national airline network be measured? A time series approach for the Ryanair case in Spain', Journal of Transport Economics and Policy.
  • Castillo, J.I., Castro, M. and Pedregal, D.J. (in press), 'Can fear of going to jail reduce the number of road fatalities? The Spanish experience', Journal of Safety Research.
  • García, F.P., Pedregal, D.J., Roberts, C. (in press), 'New methods for the condition monitoring of level crossings', International Journal of System Science.
  • Leal, T., Pedregal, D.J. and Pérez, J.J. (2011), 'Short term monitoring of the Spanish government balance', Journal of the Spanish Economic Association (SERIEs), 2, 97-119.
  • Carnero, C., Pedregal, D.J. (2010), 'Modelling and forecasting occupational accidents of different severity levels in Spain', Reliability Engineering and System Safety, 95, 1134-1141.
  • Castillo, JI, Castro, M, Pedregal, DJ,  (2010), "An econometric analysis of the effects of the penalty points system driver's license in Spain", Accident Analysis & Prevention 42 (4): 1310-1319.
  • García, F.P., Pedregal, D.J., Roberts, C. (2010), 'Time Series methods applied to failure prediction and detection', aceptado en Reliability Engineering & System Safety’, 95, 698-703.
  • Pedregal, D.J., Trapero, J.R. (2010), 'Mid-term hourly electricity forecasting based on a multi-rate approach', Energy Conversion and Management, 51, 105-111.
  • Pedregal, D.J., Pérez, J.J. (2010), 'Should quarterly government finance statistics be used for fiscal surveillance in Europe?, International Journal of Forecasting, 26, 794-807.
  • Leal, T., Pedregal, D.J., Pérez, J.J. (2010), 'Short-term monitoring of the Spanish government balance', SERIEs, DOI 10.1007/s13209-010-0018-3.
  • Onorante, L., Pedregal, D.J., Pérez, J.J., Signorini, S. (2010), 'The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area', Journal of Policy Modeling, 32, 98-119.
  • Trapero, J.R., Pedregal, D.J. (2009), 'Frequency domain methods applied to forecasting electricity markets', Energy Economics, 31, 727-735.
  • Pedregal, D.J., Dejuán, O., Gómez, N. y Tobarra, M.A. (2009), 'Modelling demand for crude oil products in Spain', Energy Policy, 37, 4417-4427.
  • Pedregal, D.J., Rivas, R., Feliu, V., Sánchez, L., Linares, A. (2009), 'A non-linear forecasting system for the Ebro River at Zaragoza, Spain', EnvironmentalModelling and Software, 24, 502-509.
  • Pedregal, D.J., Carnero, C. (2009), 'Vibration analysis diagnostics by continuous-time models: A case study', Reliability Engineering and System Safety, 94, 244-253.
  • Pedregal, D.J., García, F.P., Roberts, C. (2009), 'An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions', Annals of Operations Research, 166, 109-124.
  • Rivas, R., Feliu, V., Sánchez, L., Pedregal, D.J., Linares, A., Aguilar, J.V., Langarita, P. (2008), 'Identificación del primer tramo del canal principal de riego "Imperial de Aragón"', Ingeniería hidráulica en México, 23, 71-87.
  • Pedregal, D.J., Young, P.C. (2008), Development of improved adaptive approaches to electricity demand forecasting, Journal of the Operational Research Society, 59, 1066-1076.
  • García, F.P. and Pedregal, D. J.(2007). Applied RCM2 Algorithms Based on Statistical Methods, International Journal of Automation and Computing, 4, 109-116.
  • Pedregal, D.J., Trapero, J.R. (2007), 'Electricity Prices Forecasting by Automatic Dynamic Harmonic Regression Models', Energy Conversion and Management,48, 1710-1719.
  • Taylor, C.J., Pedregal, D.J., Young, P.C., Tych, W., (2007) 'Time series analysis and forecasting with the Captain toolbox', Environmental Modelling and Software, 22, 797-814.
  • García, F.P., Pedregal, D.J., Schmid, F., (2007), 'Unobserved Components models applied to theassessment of wear in railway points: a case study', European Journal of Operational Research, 176, 1703-1712.
  • García, F.P., Pedregal, D.J., (2007), 'Failure Analysis and Diagnostics for Railway Trackside Equipment', Engineering Failure Analysis, 14, 1411-1426.
  • Pedregal, D.J., Young, P.C. (2006), 'Modulated cycles, an Approach to Modelling Periodic Components from Rapidly Sampled Data', International Journal of Forecasting, 22, 189-194.
  • Pedregal, D.J., Carnero, C. (2006), 'State Space Models for Condition Monitoring. A case study', Reliability Engineering and System Safety, 91, 171-180.
  • Pedregal, D.J., García, F.P., Schmid, F. (2004), 'RCM2 Predictive Maintenance of Railway Systems Based on Unobserved Components Models', Reliability Engineering and System Safety, 83, 103-110.
  • Tych, W., Pedregal, D.J., Young, P.C., Davies, J. (2002), 'An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system', International Journal of Forecasting, 18, 673-695.
  • Pedregal, D.J., (2001), 'Book review: Tourism demand modelling and forecasting. Modern Econometric approaches, Haiyan Song and Stephen F. Witt (1999) (Elsevier Science Ltd.)', International Journal of Forecasting, 17, 297-299.
  • Pedregal, D.J., Young, P.C. (2001), 'Some comments on the use and abuse of the Hodrick-Prescott Filter', Review on Economic Cycles, II. http://www.uned.es/imaec2000/revista2/hp_final.pdf
  • Pedregal, D.J., (2001), 'Analysis of Economic Cycles Using Unobserved Components Models', Review on Economic Cycles, II. http://www.uned.es/imaec2000/articulos revista/revista2/pedregal.pdf
  • Pedregal, D.J., (2001), 'Trend Models for the Prediction of Economic Cycles', Review on Economic Cycles, III. http://www.uned.es/imaec2000/articulos revista/revista3/pedregal.pdf
  • Young, P.C., Pedregal, D.J., Tych, W. (1999), 'Dynamic Harmonic Regression', Journal of Forecasting, 18, 369-394.
  • Young, P.C., Pedregal, D.J. (1999), 'Recursive and En-Block Approaches to Signal Extraction', Journal of Applied Statistics, 26, 103-128.
  • Young, P.C., Pedregal, D.J. (1999), 'Macro-Economic Relativity: Government Spending, Private Investment and Unemployment in the USA', Structural Change and Economic Dynamics, 10, 359-380.
  • Young, P.C., Tych, W., Pedregal, D.J. (1998), 'Stochastic unobserved Component models for adaptive signal extraction and forecasting', Proceedings of Neural Networks for Signal Processing, VIII, 234-243.
  • Young, P.C., Pedregal, D.J. (1997), 'COMMENTS ON THE PAPER "An analysis of the international tourism demand in Spain", International Journal of Forecasting, 13, 551-556.
  • Young, P.C., Pedregal, D.J. (1996), 'Recursive Fixed Interval Smoothing and the Evaluation of LIDAR Measurements: A Comment on the Paper by Holst, Hössjer, Björklund, Ragnarsson and Edner', Environmetrics, 7, 417-427.
  • Pedregal, D.J., (1996),'Forecasting with STAMP', O.R. Insight, 9, No. 3, 29-32.
  • Young, P.C., Pedregal, D.J. (1996), 'SOFTWARE review: Bayesian analysis of time series (BATS)', International Journal of Forecasting, 12, 429-432.






  • CAPTAIN (with P.C. Young, W. Tych, C.J. Taylor and Paul Mckenna), a MATLAB toolbox for the prediction and analysis of time series in a very general and flexible way. It includes functions for the decomposition of time series into unobserved components, seasonal adjustment, State Dependent Analysis, discrete and continuous time functions, etc.
  • ECOTOOL (ECONometrics TOOLbox), another MATLAB toolbox for the analysis of classical times series with novel properties: Exponential Smoothing, ARIMA and Unobserved Components Models, all of them with Transfer Function Terms added and with automatic detection of outliers. VARX models also included and powerfull Graphical User Interfaces very usefull for identification, diagnosis and prediction analysis of all the available models. Check ECOTOOLdemos(1) in the MATLAB command window for an extensive tutorial.
  • SSpace (State Space), yet another MATLAB toolbox for general and flexible State Space modelling. It includes some templates for the most used types of models. Check SSpacedemos(1) in the MATLAB command window to see and extensive tutorial.


ETSI Industriales, Edificio Politécnico, 13071 Ciudad Real Teléfono: +34 (9) 26 295430 FAX: +34 (9) 26 295361 - Ext.: 3810 Diego.Pedregal@uclm.es