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Journal articles on the topic 'Nonlinear time series models'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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1

Andel, Jiri. "On nonlinear models for time series." Statistics 20, no. 4 (January 1989): 615–32. http://dx.doi.org/10.1080/02331888908802217.

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2

Mills, Terence C. "NONLINEAR TIME SERIES MODELS IN ECONOMICS." Journal of Economic Surveys 5, no. 3 (September 1991): 215–42. http://dx.doi.org/10.1111/j.1467-6419.1991.tb00133.x.

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3

Hollkamp, Joseph J., and Stephen M. Batill. "Time‐Series Models for Nonlinear Systems." Journal of Aerospace Engineering 3, no. 4 (October 1990): 271–84. http://dx.doi.org/10.1061/(asce)0893-1321(1990)3:4(271).

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4

Tjøstheim, Dag. "Estimation in nonlinear time series models." Stochastic Processes and their Applications 21, no. 2 (February 1986): 251–73. http://dx.doi.org/10.1016/0304-4149(86)90099-2.

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5

Ngatchou-Wandji, Joseph. "Checking nonlinear heteroscedastic time series models." Journal of Statistical Planning and Inference 133, no. 1 (July 2005): 33–68. http://dx.doi.org/10.1016/j.jspi.2004.03.013.

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6

Harvey, Andrew C. "Score-Driven Time Series Models." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 321–42. http://dx.doi.org/10.1146/annurev-statistics-040120-021023.

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The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes.
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7

Hagemann, Andreas. "Stochastic equicontinuity in nonlinear time series models." Econometrics Journal 17, no. 1 (January 21, 2014): 188–96. http://dx.doi.org/10.1111/ectj.12013.

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8

Öcal, Nadir. "Nonlinear Models for U.K. Macroeconomic Time Series." Studies in Nonlinear Dynamics and Econometrics 4, no. 3 (September 1, 2000): 123–35. http://dx.doi.org/10.1162/108118200750387982.

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9

Robinzonov, Nikolay, Gerhard Tutz, and Torsten Hothorn. "Boosting techniques for nonlinear time series models." AStA Advances in Statistical Analysis 96, no. 1 (June 30, 2011): 99–122. http://dx.doi.org/10.1007/s10182-011-0163-4.

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10

Judd, Kevin, and Alistair Mees. "On selecting models for nonlinear time series." Physica D: Nonlinear Phenomena 82, no. 4 (May 1995): 426–44. http://dx.doi.org/10.1016/0167-2789(95)00050-e.

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11

Ma, Ni, and Gang Wei. "Research on nonlinear models of time series." Journal of Electronics (China) 16, no. 3 (July 1999): 200–207. http://dx.doi.org/10.1007/s11767-999-0016-4.

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12

Koul, Hira L., and Anton Schick. "Efficient Estimation in Nonlinear Autoregressive Time-Series Models." Bernoulli 3, no. 3 (September 1997): 247. http://dx.doi.org/10.2307/3318592.

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13

Geweke, John, and Nobuhiko Terui. "BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES." Journal of Time Series Analysis 14, no. 5 (September 1993): 441–54. http://dx.doi.org/10.1111/j.1467-9892.1993.tb00156.x.

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14

Psaradakis, Zacharias, Martin Sola, Fabio Spagnolo, and Nicola Spagnolo. "Selecting nonlinear time series models using information criteria." Journal of Time Series Analysis 30, no. 4 (July 2009): 369–94. http://dx.doi.org/10.1111/j.1467-9892.2009.00614.x.

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15

Majda, Andrew J., and John Harlim. "Physics constrained nonlinear regression models for time series." Nonlinearity 26, no. 1 (November 20, 2012): 201–17. http://dx.doi.org/10.1088/0951-7715/26/1/201.

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16

Wu, Tracy Z., Haiqun Lin, and Yan Yu. "Single-index coefficient models for nonlinear time series." Journal of Nonparametric Statistics 23, no. 1 (March 2011): 37–58. http://dx.doi.org/10.1080/10485252.2010.497554.

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17

Ouagnina, Hili. "On the estimation of nonlinear time series models." Stochastics and Stochastic Reports 52, no. 3-4 (February 1995): 207–26. http://dx.doi.org/10.1080/17442509508833972.

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18

Mittnik, Stefan, and Denghua Zhong. "Dynamic properties in nonlinear multivariate time series models." IFAC Proceedings Volumes 32, no. 2 (July 1999): 6121–26. http://dx.doi.org/10.1016/s1474-6670(17)57044-5.

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19

Cheng, Fuxia. "Variance estimation in nonlinear autoregressive time series models." Journal of Statistical Planning and Inference 141, no. 4 (April 2011): 1588–92. http://dx.doi.org/10.1016/j.jspi.2010.11.010.

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20

Cai, Zongwu, Jianqing Fan, and Qiwei Yao. "Functional-Coefficient Regression Models for Nonlinear Time Series." Journal of the American Statistical Association 95, no. 451 (September 2000): 941–56. http://dx.doi.org/10.1080/01621459.2000.10474284.

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21

Çinar, Ali. "Nonlinear time series models for multivariable dynamic processes." Chemometrics and Intelligent Laboratory Systems 30, no. 1 (November 1995): 147–58. http://dx.doi.org/10.1016/0169-7439(95)00060-7.

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22

Battaglia, Francesco, and Mattheos K. Protopapas. "Multi–regime models for nonlinear nonstationary time series." Computational Statistics 27, no. 2 (May 21, 2011): 319–41. http://dx.doi.org/10.1007/s00180-011-0259-z.

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23

Hall, Jamie, Michael K. Pitt, and Robert Kohn. "Bayesian inference for nonlinear structural time series models." Journal of Econometrics 179, no. 2 (April 2014): 99–111. http://dx.doi.org/10.1016/j.jeconom.2013.10.016.

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24

Siu, Tak Kuen, and Hailiang Yang. "On pricing derivatives under nonlinear time series models." PAMM 7, no. 1 (December 2007): 1050501–2. http://dx.doi.org/10.1002/pamm.200700110.

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25

Önskog, Thomas, Christian L. E. Franzke, and Abdel Hannachi. "Nonlinear time series models for the North Atlantic Oscillation." Advances in Statistical Climatology, Meteorology and Oceanography 6, no. 2 (October 7, 2020): 141–57. http://dx.doi.org/10.5194/ascmo-6-141-2020.

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Abstract. The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models, including both short and long lags, perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution, and the different timescales of the two phases. As a spin-off of the modelling procedure, we can deduce that the interannual dependence of the NAO mostly affects the positive phase, and that timescales of 1 to 3 weeks are more dominant for the negative phase. Furthermore, the statistical properties of the model make it useful for the generation of realistic climate noise.
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26

Shephard, N. "Fitting nonlinear time-series models with applications to stochastic variance models." Journal of Applied Econometrics 8, S1 (December 1993): S135—S152. http://dx.doi.org/10.1002/jae.3950080509.

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27

Anosov, O. L., O. Ya Butkovskii, and Yu A. Kravtsov. "Nonlinear Chaotic Systems Identification from Observed Time Series." Mathematical Models and Methods in Applied Sciences 07, no. 01 (February 1997): 49–59. http://dx.doi.org/10.1142/s0218202597000049.

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Minimax procedure for nonlinear dynamic inverse problems solution is described using two adjacent moving time windows. The identification problem is solved for the discrete dynamic models (maps) and for continuous models (differential equations) (Kalmann–Bucy integrated models). We illustrate the efficiency of the identification procedure on the logistic map and Roessler system in chaotic regime, in the presence of moderate additive noise.
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28

HAN, NGAI SZE, and SHIQING LING. "GOODNESS-OF-FIT TEST FOR NONLINEAR TIME SERIES MODELS." Annals of Financial Economics 12, no. 02 (June 2017): 1750006. http://dx.doi.org/10.1142/s2010495217500063.

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Many time series models have been used extensively in modeling economic and financial data. However, it is difficult to determine the functional forms of the conditional mean and conditional variance in these models. In this paper, a test statistic based on the squared conditional residuals is proposed for testing these functional forms, and the asymptotic distribution of the test statistic is obtained. The test statistic is applicable not only to the family of GARCH models but also to other nonlinear time series models. Simulation results show that the proposed tests are powerful and have reasonable sizes. Two real examples are also given to illustrate our theory.
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29

Terui, Nobuhiko, and Herman K. van Dijk. "Combined forecasts from linear and nonlinear time series models." International Journal of Forecasting 18, no. 3 (July 2002): 421–38. http://dx.doi.org/10.1016/s0169-2070(01)00120-0.

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30

Banicescu, Ioana, Ricolindo L. Cariño, Jane L. Harvill, and John Patrick Lestrade. "Investigating asymptotic properties of vector nonlinear time series models." Journal of Computational and Applied Mathematics 236, no. 3 (September 2011): 411–21. http://dx.doi.org/10.1016/j.cam.2011.07.018.

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31

Stollenwerk, Nico, Friedhelm R. Drepper, and Helge Siegel. "Testing nonlinear stochastic models on phytoplankton biomass time series." Ecological Modelling 144, no. 2-3 (October 2001): 261–77. http://dx.doi.org/10.1016/s0304-3800(01)00377-5.

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32

Amiri, Esmail. "Forecasting daily river flows using nonlinear time series models." Journal of Hydrology 527 (August 2015): 1054–72. http://dx.doi.org/10.1016/j.jhydrol.2015.05.048.

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33

Cai, Yuzhi. "A forecasting procedure for nonlinear autoregressive time series models." Journal of Forecasting 24, no. 5 (2005): 335–51. http://dx.doi.org/10.1002/for.959.

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34

Smith, A. A. "Estimating nonlinear time-series models using simulated vector autoregressions." Journal of Applied Econometrics 8, S1 (December 1993): S63—S84. http://dx.doi.org/10.1002/jae.3950080506.

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35

Huang, Ren, Feiyun Xu, and Ruwen Chen. "General expression for linear and nonlinear time series models." Frontiers of Mechanical Engineering in China 4, no. 1 (January 7, 2009): 15–24. http://dx.doi.org/10.1007/s11465-009-0015-z.

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36

Fathian, Farshad, Ahmad Fakheri Fard, Taha B. M. J. Ouarda, Yagob Dinpashoh, and S. S. Mousavi Nadoushani. "Modeling streamflow time series using nonlinear SETAR-GARCH models." Journal of Hydrology 573 (June 2019): 82–97. http://dx.doi.org/10.1016/j.jhydrol.2019.03.072.

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37

Ngatchou-Wandji, Joseph, Madan L. Puri, Michel Harel, and Echarif Elharfaoui. "Testing nonstationary and absolutely regular nonlinear time series models." Statistical Inference for Stochastic Processes 22, no. 3 (December 18, 2018): 557–93. http://dx.doi.org/10.1007/s11203-018-9194-8.

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38

Valpine, Perry de, and Ray Hilborn. "State-space likelihoods for nonlinear fisheries time-series." Canadian Journal of Fisheries and Aquatic Sciences 62, no. 9 (September 1, 2005): 1937–52. http://dx.doi.org/10.1139/f05-116.

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State-space models are commonly used to incorporate process and observation errors in analysis of fisheries time series. A gap in analysis methods has been the lack of classical likelihood methods for nonlinear state-space models. We evaluate a method that uses weighted kernel density estimates of Bayesian posterior samples to estimate likelihoods (Monte Carlo Kernel Likelihoods, MCKL). Classical likelihoods require integration over the state-space, and we compare MCKL to the widely used errors-in-variables (EV) method, which estimates states jointly with parameters by maximizing a nonintegrated likelihood. For a simulated, linear, autoregressive model and a Schaefer model fit to cape hake (Merluccius capensis × M. paradoxus) data, classical likelihoods outperform EV likelihoods, which give asymptotically biased parameter estimates and inaccurate confidence regions. Our results on the importance of integrated state-space likelihoods also support the value of Bayesian analysis with Monte Carlo posterior integration. Both approaches provide valuable insights and can be used complementarily. Previously, Bayesian analysis was the only option for incorporating process and observation errors with complex nonlinear models. The MCKL method provides a classical approach for such models, so that choice of analysis approach need not depend on model complexity.
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39

Vika, Blerina, and Ilir Vika. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models." Academic Journal of Interdisciplinary Studies 10, no. 5 (September 5, 2021): 293. http://dx.doi.org/10.36941/ajis-2021-0140.

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Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework. Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021
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40

Hinich, Melvin. "INTRODUCTION TO THE SPECIAL ISSUE ON NONLINEAR TIME SERIES." Macroeconomic Dynamics 14, S1 (March 12, 2010): 1–2. http://dx.doi.org/10.1017/s1365100509991131.

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A nation's economy is a complex nonlinear dynamical system with links to other national economies. Classical macroeconomic models typically incorporate linear approximations to the nonlinear world and add simple dynamics to capture adjustments over time. Most of these simplifications offer little insight into the nonlinear structure of economic relationships that exist. Nor do they provide useful predictions beyond the short-term predictions of the autoregressive linear models from which they are derived.
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41

Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (December 28, 2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.

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NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
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42

NAKAMURA, TOMOMICHI, KEVIN JUDD, and ALISTAIR MEES. "REFINEMENTS TO MODEL SELECTION FOR NONLINEAR TIME SERIES." International Journal of Bifurcation and Chaos 13, no. 05 (May 2003): 1263–74. http://dx.doi.org/10.1142/s0218127403007205.

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Many models of the dynamics of nonlinear time series have large numbers of parameters and tend to overfit. This paper discusses algorithms for selecting the best basis functions from a dictionary for a model of a time series. Selecting the optimal subset of basis functions is typically an NP-hard problem which usually has to be solved by heuristic methods. In this paper, we propose a new heuristic that is a refinement of a previous one. We demonstrate with applications to artificial and real data. The results indicate that the method proposed in this paper is able to obtain better models in most cases.
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43

Rossen, Anja. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations." Jahrbücher für Nationalökonomie und Statistik 236, no. 3 (May 1, 2016): 389–409. http://dx.doi.org/10.1515/jbnst-2015-1019.

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Abstract Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, nonlinear models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, sine, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that these models, including nonlinear transformation of lagged autoregression residuals, are able to provide better forecasting results than simple linear models. Thus, it may be possible to improve the forecasting accuracy of linear models by including nonlinear components. This is especially true for time series that are positively tested for nonlinear characteristics and longer forecast horizons.
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44

LU, ZUDI, and PING CHENG. "NONPARAMETRIC IDENTIFICATION FOR NONLINEAR AUTOREGRESSIVE TIME SERIES MODELS: CONVERGENCE RATES." Chinese Annals of Mathematics 20, no. 02 (April 1999): 173–84. http://dx.doi.org/10.1142/s0252959999000205.

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45

Davis, Richard A., Thomas C. M. Lee, and Gabriel A. Rodriguez-Yam. "Break Detection for a Class of Nonlinear Time Series Models." Journal of Time Series Analysis 29, no. 5 (August 14, 2008): 834–67. http://dx.doi.org/10.1111/j.1467-9892.2008.00585.x.

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46

Nassiuma, D. K., and A. Thavaneswaran. "Smoothed estimates for nonlinear time series models with irregular data." Communications in Statistics - Theory and Methods 21, no. 8 (January 1992): 2247–59. http://dx.doi.org/10.1080/03610929208830910.

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47

Rasmussen, David A., Oliver Ratmann, and Katia Koelle. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series." PLoS Computational Biology 7, no. 8 (August 25, 2011): e1002136. http://dx.doi.org/10.1371/journal.pcbi.1002136.

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48

Chan, Wai-Sum, Albert C. S. Wong, and Howell Tong. "Some Nonlinear Threshold Autoregressive Time Series Models for Actuarial Use." North American Actuarial Journal 8, no. 4 (October 2004): 37–61. http://dx.doi.org/10.1080/10920277.2004.10596170.

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49

Brown, Tim C., Paul D. Feigin, and Diana L. Pallant. "Estimation for a class of positive nonlinear time series models." Stochastic Processes and their Applications 63, no. 2 (November 1996): 139–52. http://dx.doi.org/10.1016/0304-4149(96)00071-3.

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50

Ngatchou-Wandji, Joseph. "Estimation in a class of nonlinear heteroscedastic time series models." Electronic Journal of Statistics 2 (2008): 40–62. http://dx.doi.org/10.1214/07-ejs157.

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