Books on the topic 'Identification of an autoregressive model'

Choi, ByoungSeon. ARMA model identification. New York: Springer-Verlag, 1992.

Brüggemann, Ralf. Model Reduction Methods for Vector Autoregressive Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17029-4.

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. Cambridge, MA: National Bureau of Economic Research, 1994.

Hellendoorn, Hans, and Dimiter Driankov, eds. Fuzzy Model Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60767-7.

Choi, ByoungSeon. ARMA Model Identification. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8.

Goodwin, Graham, ed. Model Identification and Adaptive Control. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0711-8.

Abonyi, János. Fuzzy Model Identification for Control. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0027-7.

Mocan, H. Naci. Business cycles and fertility dynamics in the U.S.: A vector-autoregressive model. Cambridge, MA (1050 Massachusetts Avenue, Cambridge, MA 02138): National Bureau of Economic Research, 1989.

Billings, S. A. Model identification and assessment based on model predicted output. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

Johansen, Søren. The asymptotic variance of the estimated roots in a cointegrated vector autoregressive model. Florence: European University Institute, Department of Economics, 2001.

Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London: London School of Economics, 1986.

Hasan, Mohammad S. Monetary policy, fiscal policy, and aggregate economic activity in a vector autoregressive model. Newcastle upon Tyne: University of Northumbria at Newcastle, 1995.

Rodda, Gail. Model T Ford parts identification guide. [S.l]: G. Rodda, 1994.

Johansen, Søren. Controlling inflation in a cointegrated vector autoregressive model with an application to US data. San Domenico: European University Institute, Department of Economics, 2001.

Polizzi, Rick. Classic plastic model kits: Identification & value guide. Paducah, Ky: Collector Books, 1996.

Jia, Shiyi. Model identification of a reconfigurable robot manipulator. Ottawa: National Library of Canada, 1993.

Brenner, Marty. Aeroservoelastic uncertainity model identification from flight data. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 2001.

Billings, S. A. Rational model data smoothers and identification algorithms. Sheffield: Universityof Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. San Domenico (FI) Italy: European University Institute, 2000.

Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. Badia Fiesolana, San Domenico: European University Institute, 2000.

Wensink, Hans Einar. Autoregressive model inference in finite samples =: Autoregressief modelleren op basis van een eindig aantal waarnemingen. [Delft, Netherlands]: Delft University Press, 1996.

Dass, Rajanish. Feasibility and sustainability model for identity management. Ahmedabad: Indian Institute of Management, 2009.

Warne, Anders. A common trends model: Identification, estimation and inference. Stockholm: Stockholm University, Institute for International Economic Studies, 1993.

Syddall, Mark Timothy. Improving the identification of a penicillin fermentation model. Birmingham: University of Birmingham, 1998.

Zapranis, Achilleas, and Apostolos-Paul N. Refenes. Principles of Neural Model Identification, Selection and Adequacy. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0559-6.

Quach, Tai. A study of techniques for rotorcraft model identification. [Downsview, Ont.]: Aerospace Science and Engineering, 1989.

Recursive identification based on the nonlinear Wiener model. Uppsala: Academia Upsaliensis, 1990.

Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. London: International Centre for Economics and Related Disciplines, 1988.

Raol, J. R. Comparison of recent model selection criteria for system identification. Bangalore, Inida: National Aeronautical Laboratory, 1988.

Billings, S. A. Rational model identification using an extended least squares algorithm. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

Goodwin, Graham. Model Identification and Adaptive Control: From Windsurfing to Telecommunications. London: Springer London, 2001.

Haynes, B. R. Global analysis and model validation in nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

Gassiat, Élisabeth. Universal Coding and Order Identification by Model Selection Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96262-7.

Bayer, Patrick J. Nonparametric identification and estimation in a generalized Roy model. Cambridge, MA: National Bureau of Economic Research, 2008.

Mao, K. Z. Multi-directional model validity tests for nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

Billings, S. A. Extended model set, global data and threshold model identification of severely non-linear systems. Sheffield: University of Sheffield, Dept. of Control Engineering, 1989.

Jones, G. N. Identification of system dynamics of a high incidence research model. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

Mao, K. Z. A regularized least squares algorithm for nonlinear rational model identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

Chen, S. Recursive maximum likelihood identification of a nonlinear output-affine model. Sheffield: University of Sheffield, Dept. of Control Engineering, 1987.

Simani, Silvio, Cesare Fantuzzi, and Ronald Jon Patton. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3829-7.

Ferguson, Warren Scott. Optimal closed-loop experiments for accurate step response model identification. Ottawa: National Library of Canada, 2001.

1946-, Zwick Paul Dean, ed. Smart land-use analysis: The LUCIS model land use identification strategy. Redlands, Calif: ESRI Press, 2007.

Liu, G. P. Multiobjective criteria for nonlinear model selection and identification with neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

Shi, Feng. Learn About the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in R With Data From the DJIA 30 Stock Time Series (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526487650.

Fryer, Roland G. Categorical cognition: A psychological model of categories and identification in decision making. Chicago, Ill: American Bar Foundation, 2002.

Ikhouane, Faycal. Systems with hysteresis: Analysis, identification and control using the Bouc-Wen model. Chichester, England: John Wiley, 2007.

Michaud, Pierre-Carl. A collective retirement model: Identification and estimation in the presence of externalities. Bonn, Germany: IZA, 2004.

Fryer, Roland G. Categorical cognition: A psychological model of categories and identification in decision making. Cambridge, Mass: National Bureau of Economic Research, 2003.

Carr, Margaret H. Smart land-use analysis: The LUCIS model, land-use conflict identification strategy. Redlands, CA: ESRI Press, 2007.