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Journal articles on the topic 'Autoregressives Modell'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Christ, Oliver, Peter Schmidt, Elmar Schlüter, and Ulrich Wagner. "Analyse von Prozessen und Veränderungen." Zeitschrift für Sozialpsychologie 37, no. 3 (2006): 173–84. http://dx.doi.org/10.1024/0044-3514.37.3.173.

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Zusammenfassung: Komplexe Veränderungen von sozialpsychologischen Merkmalen über längere Zeiträume können nur mittels aufwändiger Panel-Studien untersucht werden. Zur Analyse solcher Zusammenhänge werden angemessene Analysemodelle benötigt. Im vorliegenden Beitrag soll das von Bollen und Curran (2004) entwickelte autoregressive Wachstumskurvenmodell vorgestellt werden. Das Modell stellt eine Kombination von zwei etablierten Analysemethoden für Längsschnittdaten dar. Diese sind das autoregressive Modell und das latente Wachstumskurvenmodell. Es werden die Vorteile des autoregressiven Wachstumsk

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2

Saikkonen, Pentti, and Ritva Luukkonen. "Estimating multivariate autoregressive moving average models by fitting long autoregressions." Communications in Statistics - Theory and Methods 18, no. 4 (1989): 1589–615. http://dx.doi.org/10.1080/03610928908829987.

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3

MUIANGA, CARLOS ALBERTO, JOEL AUGUSTO MUNIZ, MICHERLANIA DA SILVA NASCIMENTO, TALES JESUS FERNANDES, and TACIANA VILELLA SAVIAN. "DESCRIÇÃO DA CURVA DE CRESCIMENTO DE FRUTOS DO CAJUEIRO POR MODELOS NÃO LINEARES." Revista Brasileira de Fruticultura 38, no. 1 (2016): 22–32. http://dx.doi.org/10.1590/0100-2945-295/14.

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RESUMO O objetivo do trabalho foi avaliar o ajuste dos modelos Gompertz e Logístico, com estrutura de erros independentes e autoregressivos, no desenvolvimento de frutos de caju, com base em medidas de comprimento e largura do fruto, tomados ao longo do tempo. A estimação dos parâmetros foi feita por meio de rotinas no software R, utilizando-se o método dos mínimos quadrados e o processo iterativo de Gauss- Newton. Os modelos foram comparados pelos critérios: coeficiente de determinação ajustado (R2 aj), desvio padrão residual (DPR), critério de informação Akaike (AIC) ecritériobayesiano de Sc

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4

Burgan, Halil Ibrahim. "AFYON BÖLGESİ’NDE YAĞIŞ VERİSİ TAHMİNİ İÇİN OTOREGRESİF MODELLER." e-Journal of New World Sciences Academy 1, no. 1 (2016): 41–49. http://dx.doi.org/10.12739/nwsa.2016.1a1pb.

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5

Saturnino, Odilon, Pierre Lucena, and Valéria Saturnino. "LIQUIDEZ E VALOR NO MERCADO DE AÇÕES BRASILEIRO: MODELO DE CINCO FATORES." REAd. Revista Eletrônica de Administração (Porto Alegre) 23, no. 2 (2017): 191–224. http://dx.doi.org/10.1590/1413.2311.036.61349.

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RESUMO O artigo teve como objetivo explicar as causas dos desvios dos preços de ações em relação aos fundamentos a partir de variáveis analisadas por meio de modelos tradicionais de apreçamento de ativos, buscando explicar os desequilíbrios a partir da inclusão de um índice de liquidez. A partir de aplicações e modificações de modelos multifatoriais clássicos, foi verificada a eficácia desses modelos no Brasil. As carteiras de ativos foram formadas a partir de retorno, valor de mercado, razão Patrimônio Líquido/Valor de Mercado e liquidez, com rebalanceamento anual e análise correspondente ao

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6

Bitseki Penda, S. Valère, and Adélaïde Olivier. "Autoregressive functions estimation in nonlinear bifurcating autoregressive models." Statistical Inference for Stochastic Processes 20, no. 2 (2016): 179–210. http://dx.doi.org/10.1007/s11203-016-9140-6.

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7

Umar, S. M., and S. Bala. "Multivariate geometric autoregressive and autoregressive moving average models." Bayero Journal of Pure and Applied Sciences 12, no. 2 (2021): 12–18. http://dx.doi.org/10.4314/bajopas.v12i2.2.

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We present Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate geometric (MG) distribution. The theory of positive dependence is used to show that in many cases, multivariate geometric autoregressive (MGAR) and multivariate autoregressive moving average (MGARMA) models consist of associated random variables. We also provide a special case of the multivariate geometric autoregressive model in which it is stationary and has multivariate geometric distribution.

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8

García-Centeno, María-Carmen. "The importance of asymmetric autoregressive stochastic volatility models in financial markets." AESTIMATIO 13, no. 2016 (2016): 24–45. http://dx.doi.org/10.5605/ieb.13.2.

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9

Kyung, Minjung. "Directional conditionally autoregressive models." Korean Journal of Applied Statistics 29, no. 5 (2016): 835–47. http://dx.doi.org/10.5351/kjas.2016.29.5.835.

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10

Nguyen, Hien D., Geoffrey J. McLachlan, Jeremy F. P. Ullmann, and Andrew L. Janke. "Laplace mixture autoregressive models." Statistics & Probability Letters 110 (March 2016): 18–24. http://dx.doi.org/10.1016/j.spl.2015.11.006.

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11

Scotto, Manuel G., Christian H. Weiß, Maria Eduarda Silva, and Isabel Pereira. "Bivariate binomial autoregressive models." Journal of Multivariate Analysis 125 (March 2014): 233–51. http://dx.doi.org/10.1016/j.jmva.2013.12.014.

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12

Chen, Rong, and Ruey S. Tsay. "Functional-Coefficient Autoregressive Models." Journal of the American Statistical Association 88, no. 421 (1993): 298. http://dx.doi.org/10.2307/2290725.

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13

Ciołek, Gabriela, and Paweł Potorski. "Bootstrapping periodically autoregressive models." ESAIM: Probability and Statistics 21 (2017): 394–411. http://dx.doi.org/10.1051/ps/2017017.

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The main objective of this paper is to establish the residual and the wild bootstrap procedures for periodically autoregressive models. We use the least squares estimators of model’s parameters and generate their bootstrap equivalents. We prove that the bootstrap procedures for causal periodic autoregressive time series with finite fourth moments are weakly consistent. Finally, we confirm our theoretical considerations by simulations.

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14

Alheraish, A. "Autoregressive video conference models." International Journal of Network Management 14, no. 5 (2004): 329–37. http://dx.doi.org/10.1002/nem.528.

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15

Chen, Rong, and Ruey S. Tsay. "Functional-Coefficient Autoregressive Models." Journal of the American Statistical Association 88, no. 421 (1993): 298–308. http://dx.doi.org/10.1080/01621459.1993.10594322.

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16

Abraham, B., and N. Balakrishna. "Inverse Gaussian Autoregressive Models." Journal of Time Series Analysis 20, no. 6 (1999): 605–18. http://dx.doi.org/10.1111/1467-9892.00161.

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17

Hong-zhi, An. "NON-NEGATIVE AUTOREGRESSIVE MODELS." Journal of Time Series Analysis 13, no. 4 (1992): 283–95. http://dx.doi.org/10.1111/j.1467-9892.1992.tb00108.x.

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18

Galvao Jr., Antonio F., Gabriel Montes-Rojas, and Jose Olmo. "Threshold quantile autoregressive models." Journal of Time Series Analysis 32, no. 3 (2010): 253–67. http://dx.doi.org/10.1111/j.1467-9892.2010.00696.x.

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19

Yin, Zheng, Conall O’Sullivan, and Anthony Brabazon. "An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting." Journal of Artificial Intelligence and Soft Computing Research 6, no. 3 (2016): 155–72. http://dx.doi.org/10.1515/jaiscr-2016-0012.

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AbstractTraditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified i

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20

Barbosa, S. M., M. E. Silva, and M. J. Fernandes. "Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry." Nonlinear Processes in Geophysics 13, no. 2 (2006): 177–84. http://dx.doi.org/10.5194/npg-13-177-2006.

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Abstract. This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitt

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21

Catania, Leopoldo, and Anna Gloria Billé. "Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances." Journal of Applied Econometrics 32, no. 6 (2017): 1178–96. http://dx.doi.org/10.1002/jae.2565.

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22

Najeeb, A. R., M. J. E. Salami, T. Gunawan, and A. M. Aibinu. "Review of Parameter Estimation Techniques for Time-Varying Autoregressive Models of Biomedical Signals." International Journal of Signal Processing Systems 4, no. 3 (2016): 220–25. http://dx.doi.org/10.18178/ijsps.4.3.220-225.

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23

Block, H. W., N. A. Langberg, and D. S. Stoffer. "Bivariate exponential and geometric autoregressive and autoregressive moving average models." Advances in Applied Probability 20, no. 04 (1988): 798–821. http://dx.doi.org/10.1017/s0001867800018383.

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We present autoregressive (AR) and autoregressive moving average (ARMA) processes with bivariate exponential (BE) and bivariate geometric (BG) distributions. The theory of positive dependence is used to show that in various cases, the BEAR, BGAR, BEARMA, and BGARMA models consist of associated random variables. We discuss special cases of the BEAR and BGAR processes in which the bivariate processes are stationary and have well-known bivariate exponential and geometric distributions. Finally, we fit a BEAR model to a real data set.

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24

Wang, Wei, Junhong Li, and Ruifeng Ding. "Maximum likelihood parameter estimation algorithm for controlled autoregressive autoregressive models." International Journal of Computer Mathematics 88, no. 16 (2011): 3458–67. http://dx.doi.org/10.1080/00207160.2011.598514.

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25

Block, H. W., N. A. Langberg, and D. S. Stoffer. "Bivariate exponential and geometric autoregressive and autoregressive moving average models." Advances in Applied Probability 20, no. 4 (1988): 798–821. http://dx.doi.org/10.2307/1427361.

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We present autoregressive (AR) and autoregressive moving average (ARMA) processes with bivariate exponential (BE) and bivariate geometric (BG) distributions. The theory of positive dependence is used to show that in various cases, the BEAR, BGAR, BEARMA, and BGARMA models consist of associated random variables. We discuss special cases of the BEAR and BGAR processes in which the bivariate processes are stationary and have well-known bivariate exponential and geometric distributions. Finally, we fit a BEAR model to a real data set.

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26

Nademi, Arash, and Rahman Farnoosh. "Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach." Journal of Applied Statistics 41, no. 2 (2013): 275–93. http://dx.doi.org/10.1080/02664763.2013.839129.

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27

Hasan, Evan Abdulmajeed. "Bayesian Analysis Influences Autoregressive Models." International Journal Of Engineering, Business And Management 3, no. 3 (2019): 65–76. http://dx.doi.org/10.22161/ijebm.3.3.2.

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28

Franses, Philip Hans, and Michael McAleer. "Testing periodically integrated autoregressive models." Mathematics and Computers in Simulation 43, no. 3-6 (1997): 457–65. http://dx.doi.org/10.1016/s0378-4754(97)00032-3.

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29

Astatkie, T., D. G. Watts, and W. E. Watt. "Nested threshold autoregressive (NeTAR) models." International Journal of Forecasting 13, no. 1 (1997): 105–16. http://dx.doi.org/10.1016/s0169-2070(96)00716-9.

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30

Ding, Jie, Shahin Shahrampour, Kathryn Heal, and Vahid Tarokh. "Analysis of Multistate Autoregressive Models." IEEE Transactions on Signal Processing 66, no. 9 (2018): 2429–40. http://dx.doi.org/10.1109/tsp.2018.2811757.

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31

Patterson, K. D. "Bias reduction in autoregressive models." Economics Letters 68, no. 2 (2000): 135–41. http://dx.doi.org/10.1016/s0165-1765(00)00233-0.

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32

Mikusheva, Anna. "Uniform Inference in Autoregressive Models." Econometrica 75, no. 5 (2007): 1411–52. http://dx.doi.org/10.1111/j.1468-0262.2007.00798.x.

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33

Djuric, P. M., and S. M. Kay. "Order selection of autoregressive models." IEEE Transactions on Signal Processing 40, no. 11 (1992): 2829–33. http://dx.doi.org/10.1109/78.165674.

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34

Bauldry, Shawn, and Kenneth A. Bollen. "Nonlinear Autoregressive Latent Trajectory Models." Sociological Methodology 48, no. 1 (2018): 269–302. http://dx.doi.org/10.1177/0081175018789441.

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Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed. In this paper we focus

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35

Al-Osh, M. A., and A. A. Alzaid. "Binomial autoregressive moving average models." Communications in Statistics. Stochastic Models 7, no. 2 (1991): 261–82. http://dx.doi.org/10.1080/15326349108807188.

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36

Daoudi, K., A. B. Frakt, and A. S. Willsky. "Multiscale autoregressive models and wavelets." IEEE Transactions on Information Theory 45, no. 3 (1999): 828–45. http://dx.doi.org/10.1109/18.761321.

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37

Benjamin, Michael A., Robert A. Rigby, and D. Mikis Stasinopoulos. "Generalized Autoregressive Moving Average Models." Journal of the American Statistical Association 98, no. 461 (2003): 214–23. http://dx.doi.org/10.1198/016214503388619238.

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38

McQuarrie, Allan D., and Chih-Ling Tsai. "Outlier Detections in Autoregressive Models." Journal of Computational and Graphical Statistics 12, no. 2 (2003): 450–71. http://dx.doi.org/10.1198/1061860031671.

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39

Wood, Sally, Ori Rosen, and Robert Kohn. "Bayesian Mixtures of Autoregressive Models." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 174–95. http://dx.doi.org/10.1198/jcgs.2010.09174.

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40

Li, Guodong, Bo Guan, Wai Keung Li, and Philip L. H. Yu. "Hysteretic autoregressive time series models." Biometrika 102, no. 3 (2015): 717–23. http://dx.doi.org/10.1093/biomet/asv017.

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41

Sato, Takaki, and Yasumasa Matsuda. "Spatial Autoregressive Conditional Heteroskedasticity Models." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 47, no. 2 (2017): 221–36. http://dx.doi.org/10.14490/jjss.47.221.

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42

Brunsdon, C., A. S. Fotheringham, and M. Charlton. "Spatial Nonstationarity and Autoregressive Models." Environment and Planning A: Economy and Space 30, no. 6 (1998): 957–73. http://dx.doi.org/10.1068/a300957.

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Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to investigate spatial variations in spatial association. The particular framework in which spatial association is examined here is the spatial autoregressi

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43

LEE, WEN C., and RUEY S. LIN. "AUTOREGRESSIVE AGE-PERIOD-COHORT MODELS." Statistics in Medicine 15, no. 3 (1996): 273–81. http://dx.doi.org/10.1002/(sici)1097-0258(19960215)15:3<273::aid-sim172>3.0.co;2-r.

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44

PIERCE, DONALD A. "Testing normality in autoregressive models." Biometrika 72, no. 2 (1985): 293–97. http://dx.doi.org/10.1093/biomet/72.2.293.

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45

Garderen, Kees Jan van. "Exact Geometry of Autoregressive Models." Journal of Time Series Analysis 20, no. 1 (1999): 1–21. http://dx.doi.org/10.1111/1467-9892.00122.

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46

Grynkiv, Galyna, and Lars Stentoft. "Stationary Threshold Vector Autoregressive Models." Journal of Risk and Financial Management 11, no. 3 (2018): 45. http://dx.doi.org/10.3390/jrfm11030045.

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This paper examines the steady state properties of the Threshold Vector Autoregressive model. Assuming that the trigger variable is exogenous and the regime process follows a Bernoulli distribution, necessary and sufficient conditions for the existence of stationary distribution are derived. A situation related to so-called “locally explosive models”, where the stationary distribution exists though the model is explosive in one regime, is analysed. Simulations show that locally explosive models can generate some of the key properties of financial and economic data. They also show that assessin

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47

Kalli, Maria, and Jim E. Griffin. "Bayesian nonparametric vector autoregressive models." Journal of Econometrics 203, no. 2 (2018): 267–82. http://dx.doi.org/10.1016/j.jeconom.2017.11.009.

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48

Rocha, Andréa V., and Francisco Cribari-Neto. "Beta autoregressive moving average models." TEST 18, no. 3 (2008): 529–45. http://dx.doi.org/10.1007/s11749-008-0112-z.

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49

Tanaka, Fuyuhiko. "Superharmonic priors for autoregressive models." Information Geometry 1, no. 2 (2017): 215–35. http://dx.doi.org/10.1007/s41884-017-0001-1.

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50

Lau, John W., and Mike K. P. So. "Bayesian mixture of autoregressive models." Computational Statistics & Data Analysis 53, no. 1 (2008): 38–60. http://dx.doi.org/10.1016/j.csda.2008.06.001.

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