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1

Dag, Tjøstheim, and Granger, C. W. J. (Clive William John), 1934-2009, eds. Modelling nonlinear economic time series. Oxford: Oxford University Press, 2010.

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2

Costas, Milas, and Rothman Philip, eds. Nonlinear time series analysis of business cycles. Boston: Elsevier, 2006.

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3

Christian, Dunis, and Zhou Bin 1956-, eds. Nonlinear modelling of high frequency financial time series. Chichester [England]: Wiley, 1998.

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4

Philip, Rothman, ed. Nonlinear time series analysis of economic and financial data. Boston: Kluwer Academic Publishers, 1999.

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5

1949-, Creedy John, and Martin Vance 1955-, eds. Nonlinear economic models: Cross-sectional, time series and neural network applications. Cheltenham, U.K: E. Elgar, 1997.

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6

Terdik, György. Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1552-3.

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7

Nonlinear time series analysis with applications to foreign exchange rate volatility. Heidelberg: Physica-Verlag, 1997.

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8

Douc, Randal. Nonlinear times series: Theory, methods and applications with R examples. Boca Raton: CRC Press, 2014.

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9

Lewis, Peter A. W. Nonlinear modeling of time series using Multivariate Adaptive Regression Splines (MARS). Monterey, Calif: Naval Postgraduate School, 1990.

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10

Kariya, Takeaki. Hi senkei keizai jikeiretsu bunsekihō to sono ōyō: Gausu-sei kentei to hi senkei moderu. Tōkyō: Iwanami Shoten, 1997.

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11

Stock, James H. A comparison of linear and nonlinear univariate models for for[e]casting macroeconomic time series. Cambridge, MA: National Bureau of Economic Research, 1998.

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12

A, Barnett William, and International Symposium in Economic Theory and Econometrics (11th : 1995 : University of Aarhus), eds. Nonlinear econometric modeling in time series: Proceedings of the Eleventh International Symposium in Economic Theory. Cambridge, UK: Cambridge University Press, 2000.

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13

1950-, Ashley Richard A., ed. A nonlinear time series workshop: A toolkit for detecting and identifying nonlinear serial dependence. Boston, Mass: Kluwer Academic, 2000.

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14

Robinson, Peter M. Nonlinear time series with long memory: A model for stochastic volatility. London: London School of Economics, Financial Markets Group, 1996.

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15

Robinson, P. M. Nonlinear time series with long memory: A model for stochastic volatility. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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16

Workshop on Chaos in Brain? (1999 Bonn, Germany). Workshop on Chaos in Brain?: NIC, Forschungszentrum Jülich and Dept. of Epileptology, University of Bonn, Germany, March 10-12, 1999. Singapore: World Scientific, 2000.

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17

1963-, Schreiber Thomas, ed. Nonlinear time series analysis. 2nd ed. Cambridge, UK: Cambridge University Press, 2004.

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18

1963-, Schreiber Thomas, ed. Nonlinear time series analysis. Cambridge: Cambridge University Press, 1997.

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19

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1993.

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20

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1992.

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21

Time series models. 2nd ed. Cambridge, Mass: MIT Press, 1993.

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22

Deistler, Manfred, and Wolfgang Scherrer. Time Series Models. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1.

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23

Cox, D. R., D. V. Hinkley, and O. E. Barndorff-Nielsen, eds. Time Series Models. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2879-5.

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24

Patterson, Douglas M., and Richard A. Ashley. A Nonlinear Time Series Workshop. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8688-7.

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25

Brandt, Patrick, and John Williams. Multiple Time Series Models. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2007. http://dx.doi.org/10.4135/9781412985215.

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26

Franses, Philip Hans. Periodic time series models. Oxford: Oxford University Press, 2004.

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27

Barber, David, A. Taylan Cemgil, and Silvia Chiappa, eds. Bayesian Time Series Models. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

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28

1958-, Williams John T., ed. Multiple time series models. Thousand Oaks, Calif: Sage Publications, 2007.

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29

Barber, David. Bayesian time series models. Cambridge: Cambridge University Press, 2011.

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30

Potter, Simon M. Nonlinear time series modelling: An introduction. [New York, N.Y.]: Federal Reserve Bank of New York, 1999.

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31

R, Mohler Ronald, ed. Nonlinear time series and signal processing. Berlin: Springer-Verlag, 1988.

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32

Mohler, R. R., ed. Nonlinear Time Series and Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0044270.

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33

Potter, Simon M. Nonlinear impulse response functions. [New York, N.Y.]: Federal Reserve Bank of New York, 1999.

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34

Les, Oxley, and Potter Simon M, eds. Surveys in economic dynamics. Oxford: Blackwell, 2000.

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35

Akhmet, Marat. Nonlinear Hybrid Continuous/Discrete-Time Models. Paris: Atlantis Press, 2011. http://dx.doi.org/10.2991/978-94-91216-03-9.

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36

service), SpringerLink (Online, ed. Nonlinear Hybrid Continuous/Discrete-Time Models. Paris: Atlantis Press, 2011.

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37

Gourieroux, Christian. Time series and dynamic models. Cambridge: Cambridge University Press, 1997.

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38

Reisen, V. A. Long memory time series models. Manchester: UMIST, 1993.

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39

Doukhan, Paul. Stochastic Models for Time Series. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7.

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40

Gourieroux, Christian. Time series and dynamic models. New York: Cambridge University Press, 1997.

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41

Nonlinear time series analysis: Methods and applications. Singapore: World Scientific, 1999.

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42

Qiwei, Yao, ed. Nonlinear time series: Nonparametric and parametric methods. New York: Springer, 2003.

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43

Donner, Reik V., and Susana M. Barbosa, eds. Nonlinear Time Series Analysis in the Geosciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78938-3.

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44

Kock, Anders Bredahl, and Timo Teräsvirta. Forecasting With Nonlinear Time Series Models. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780195398649.013.0004.

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45

Howell, Tong, ed. Dimension estimation and models. Singapore: World Scientific, 1993.

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46

Rothman, Philip. Nonlinear Time Series Analysis of Economic and Financial Data. Springer London, Limited, 2012.

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47

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Nonlinear Time Series Analysis with R. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.001.0001.

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Abstract:
In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.
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48

Rothman, Philip. Nonlinear Time Series Analysis of Economic and Financial Data. Springer, 2012.

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49

Hafner, Christian. Nonlinear Time Series Analysis with Applications to Foreign Exchange Rate Volatility. Physica-Verlag, 2013.

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50

Terdik, György. Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis: A Frequency Domain Approach. Springer, 1999.

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