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Journal articles on the topic 'Vector autoregressive moving average'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Fitrianti, H., S. M. Belwawin, M. Riyana, and R. Amin. "Climate modeling using vector moving average autoregressive." IOP Conference Series: Earth and Environmental Science 343 (November 6, 2019): 012201. http://dx.doi.org/10.1088/1755-1315/343/1/012201.

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2

Iwok, I. A., and E. H. Etuk. "On the Comparative Performance of Pure Vector Autoregressive-Moving Average and Vector Bilinear Autoregressive-Moving Average Time Series Models." Asian Journal of Mathematics & Statistics 2, no. 2 (April 15, 2009): 33–40. http://dx.doi.org/10.3923/ajms.2009.33.40.

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3

Iwok, I. A., and E. H. Etuk. "On the Comparative Performance of Pure Vector Autoregressive-Moving Average and Vector Bilinear Autoregressive-Moving Average Time Series Models." Asian Journal of Mathematics & Statistics 3, no. 3 (June 15, 2010): 179–86. http://dx.doi.org/10.3923/ajms.2010.179.186.

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4

Tsay, Ruey S. "Parsimonious Parameterization of Vector Autoregressive Moving Average Models." Journal of Business & Economic Statistics 7, no. 3 (July 1989): 327. http://dx.doi.org/10.2307/1391530.

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5

Tsay, Ruey S. "Parsimonious Parameterization of Vector Autoregressive Moving Average Models." Journal of Business & Economic Statistics 7, no. 3 (July 1989): 327–41. http://dx.doi.org/10.1080/07350015.1989.10509742.

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6

Anggraeni, Wiwik, and Leivina Kartika Dewi. "PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)." JUTI: Jurnal Ilmiah Teknologi Informasi 7, no. 2 (July 1, 2008): 107. http://dx.doi.org/10.12962/j24068535.v7i2.a180.

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7

Ben, Marta Garcia, Elena J. Martinez, and Victor J. Yohai. "Robust Estimation in Vector Autoregressive Moving-Average Models." Journal of Time Series Analysis 20, no. 4 (July 1999): 381–99. http://dx.doi.org/10.1111/1467-9892.00144.

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8

Koreisha, Sergio G., and Tarmo Pukkila. "The specification of vector autoregressive moving average models." Journal of Statistical Computation and Simulation 74, no. 8 (August 2004): 547–65. http://dx.doi.org/10.1080/00949650310001616559.

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9

Yozgatligil, Ceylan, and William W. S. Wei. "Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models." American Statistician 63, no. 4 (November 2009): 328–34. http://dx.doi.org/10.1198/tast.2009.08040.

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10

Jouini, Tarek. "Linear bootstrap methods for vector autoregressive moving-average models." Journal of Statistical Computation and Simulation 85, no. 11 (June 17, 2014): 2214–57. http://dx.doi.org/10.1080/00949655.2014.925898.

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11

Dugré, Jean-Pierre, Louis L. Scharf, and Claude Gueguen. "Exact likelihood for stationary vector autoregressive moving average processes." Signal Processing 11, no. 2 (September 1986): 105–18. http://dx.doi.org/10.1016/0165-1684(86)90030-7.

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12

Paparoditis, Efstathios. "Bootstrapping Autoregressive and Moving Average Parameter Estimates of Infinite Order Vector Autoregressive Processes." Journal of Multivariate Analysis 57, no. 2 (May 1996): 277–96. http://dx.doi.org/10.1006/jmva.1996.0034.

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13

Yang, Haimin, Zhisong Pan, Qing Tao, and Junyang Qiu. "Online learning for vector autoregressive moving-average time series prediction." Neurocomputing 315 (November 2018): 9–17. http://dx.doi.org/10.1016/j.neucom.2018.04.011.

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14

Zhang, Ning, Yunlong Zhang, and Haiting Lu. "Seasonal Autoregressive Integrated Moving Average and Support Vector Machine Models." Transportation Research Record: Journal of the Transportation Research Board 2215, no. 1 (January 2011): 85–92. http://dx.doi.org/10.3141/2215-09.

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15

Reinsel, Gregory C. "Finite sample forecast results for vector autoregressive moving average models." Journal of Forecasting 14, no. 4 (July 1995): 405–12. http://dx.doi.org/10.1002/for.3980140407.

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16

Koreisha, Sergio, and Tarmo Pukkila. "FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS." Journal of Time Series Analysis 10, no. 4 (July 1989): 325–39. http://dx.doi.org/10.1111/j.1467-9892.1989.tb00032.x.

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17

Paparoditis, Efstathios. "Testing the Fit of a Vector Autoregressive Moving Average Model." Journal of Time Series Analysis 26, no. 4 (July 2005): 543–68. http://dx.doi.org/10.1111/j.1467-9892.2005.00419.x.

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18

Kascha, Christian. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models." Econometric Reviews 31, no. 3 (May 2012): 297–324. http://dx.doi.org/10.1080/07474938.2011.607343.

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19

Gallego, Jose L. "The exact likelihood function of a vector autoregressive moving average process." Statistics & Probability Letters 79, no. 6 (March 2009): 711–14. http://dx.doi.org/10.1016/j.spl.2008.10.030.

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20

Poskitt, D. S. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology." Journal of Econometrics 192, no. 2 (June 2016): 468–84. http://dx.doi.org/10.1016/j.jeconom.2016.02.011.

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21

Dhrymes, Phoebus J. "Autoregressive Errors in Singular Systems of Equations." Econometric Theory 10, no. 2 (June 1994): 254–85. http://dx.doi.org/10.1017/s0266466600008410.

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We consider a system of m general linear models, where the system error vector has a singular covariance matrix owing to various “adding up” requirements and, in addition, the error vector obeys an autoregressive scheme. The paper reformulates the problem considered earlier by Berndt and Savin [8] (BS), as well as others before them; the solution, thus obtained, is far simpler, being the natural extension of a restricted least-squares-like procedure to a system of equations. This reformulation enables us to treat all parameters symmetrically, and discloses a set of conditions which is different from, and much less stringent than, that exhibited in the framework provided by BS.Finally, various extensions are discussed to (a) the case where the errors obey a stable autoregression scheme of order r; (b) the case where the errors obey a moving average scheme of order r; (c) the case of “dynamic” vector distributed lag models, that is, the case where the model is formulated as autoregressive (in the dependent variables), and moving average (in the explanatory variables), and the errors are specified to be i.i.d.

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22

Atmaja, Dinul Darma, Widowati Widowati, and Budi Warsito. "FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD." MEDIA STATISTIKA 14, no. 1 (March 8, 2021): 98–107. http://dx.doi.org/10.14710/medstat.14.1.98-107.

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Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.

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23

Ayudhiah, Moudy Puspita, Syamsul Bahri, and Nurul Fitriyani. "Peramalan Indeks Harga Konsumen Kota Mataram Menggunakan Vector Autoregressive Integrated Moving Average." EIGEN MATHEMATICS JOURNAL 1, no. 2 (June 7, 2020): 1. http://dx.doi.org/10.29303/emj.v1i2.61.

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24

Meimela, A., S. S. S. Lestari, I. F. Mahdy, T. Toharudin, and B. N. Ruchjana. "Modeling of covid-19 in Indonesia using vector autoregressive integrated moving average." Journal of Physics: Conference Series 1722 (January 2021): 012079. http://dx.doi.org/10.1088/1742-6596/1722/1/012079.

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25

Paparoditis, Efstathios. "Bootstrapping periodogram and cross periodogram statistics of vector autoregressive moving average models." Statistics & Probability Letters 31, no. 3 (January 1997): 243. http://dx.doi.org/10.1016/s0167-7152(96)00201-5.

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26

Ansley, Craig F., and Robert Kohn. "A NOTE ON SQUARE ROOT FILTERING FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS." Journal of Time Series Analysis 11, no. 3 (May 1990): 181–83. http://dx.doi.org/10.1111/j.1467-9892.1990.tb00050.x.

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27

Paparoditis, Efstathios. "Bootstrapping periodogram and cross periodogram statistics of vector autoregressive moving average models." Statistics & Probability Letters 27, no. 4 (May 1996): 385–91. http://dx.doi.org/10.1016/0167-7152(95)00047-x.

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28

Dias, Gustavo Fruet, and George Kapetanios. "Estimation and forecasting in vector autoregressive moving average models for rich datasets." Journal of Econometrics 202, no. 1 (January 2018): 75–91. http://dx.doi.org/10.1016/j.jeconom.2017.06.022.

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29

Meimela, A., S. S. S. Lestari, I. F. Mahdy, T. Toharudin, and B. N. Ruchjana. "Modeling of covid-19 in Indonesia using vector autoregressive integrated moving average." Journal of Physics: Conference Series 1722 (January 2021): 012079. http://dx.doi.org/10.1088/1742-6596/1722/1/012079.

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30

Galbraith, John W., Aman Ullah, and Victoria Zinde-Walsh. "ESTIMATION OF THE VECTOR MOVING AVERAGE MODEL BY VECTOR AUTOREGRESSION." Econometric Reviews 21, no. 2 (January 10, 2002): 205–19. http://dx.doi.org/10.1081/etc-120014349.

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31

Dong, Yinfeng, Yingmin Li, and Ming Lai. "Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model." Soil Dynamics and Earthquake Engineering 30, no. 3 (March 2010): 133–45. http://dx.doi.org/10.1016/j.soildyn.2009.10.002.

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32

Lütkepohl, Helmut. "Asymptotic Distribution of the Moving Average Coefficients of an Estimated Vector Autoregressive Process." Econometric Theory 4, no. 1 (April 1988): 77–85. http://dx.doi.org/10.1017/s0266466600011865.

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The coefficients of the moving average (MA) representation of a vector autoregressive (VAR) process are the dynamic multipliers of the system. These quantities are often used to analyze the relationships between the variables involved. Assuming that the actual data generation process is stationary and has a VAR representation of unknown and possibly infinite order, the asymptotic distribution of the MA coefficients is derived. A computationally simple formula for the asymptotic co variance matrix is obtained.

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33

Ansley, Craig F., and Robert Kohn. "A note on reparameterizing a vector autoregressive moving average model to enforce stationarity." Journal of Statistical Computation and Simulation 24, no. 2 (June 1986): 99–106. http://dx.doi.org/10.1080/00949658608810893.

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34

Shea, B. L. "Algorithm AS 242: The Exact Likelihood of a Vector Autoregressive Moving Average Model." Applied Statistics 38, no. 1 (1989): 161. http://dx.doi.org/10.2307/2347692.

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35

de Gooijer, Jan G., and André Klein. "On the cumulated multi-step-ahead predictions of vector autoregressive moving average processes." International Journal of Forecasting 7, no. 4 (March 1992): 501–13. http://dx.doi.org/10.1016/0169-2070(92)90034-7.

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36

LAZARIU, VICTORIA, CHENGXUAN YU, and CRAIG GUNDERSEN. "FORECASTING WOMEN, INFANTS, AND CHILDREN CASELOADS: A COMPARISON OF VECTOR AUTOREGRESSION AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE APPROACHES." Contemporary Economic Policy 29, no. 1 (January 2011): 46–55. http://dx.doi.org/10.1111/j.1465-7287.2010.00203.x.

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37

Guo, Runxia, Jiaqi Wang, Na Zhang, and Jiankang Dong. "State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 5 (March 19, 2018): 622–34. http://dx.doi.org/10.1177/0959651818762978.

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Relevance vector machine is a newly proposed and effective state prediction algorithm proved by practical applications; however, the accuracy of the single relevance vector machine model for the long-term prediction is unable to achieve satisfactory results with time goes by. Then, an autoregressive integrated moving average model is introduced to correct the prediction error caused by the single relevance vector machine, and a fusion framework based on the combination of relevance vector machine and autoregressive integrated moving average model is adopted to improve the accuracy of long-term prediction. In addition, a targeted approach for retraining the old model is put forward so that the state prediction model can be updated in time and suits the actual situation better. The effectiveness of the proposed fusion framework is illustrated via an aircraft actuator, and the experiments based on a model of civil aircraft actuator data set show that the proposed method yields a satisfied performance in state prediction of aircraft actuators.

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38

Gutiérrez-Gutiérrez, Jesús, Marta Zárraga-Rodríguez, Pedro Crespo, and Xabier Insausti. "Rate Distortion Function of Gaussian Asymptotically WSS Vector Processes." Entropy 20, no. 9 (September 19, 2018): 719. http://dx.doi.org/10.3390/e20090719.

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In this paper, we obtain an integral formula for the rate distortion function (RDF) of any Gaussian asymptotically wide sense stationary (AWSS) vector process. Applying this result, we also obtain an integral formula for the RDF of Gaussian moving average (MA) vector processes and of Gaussian autoregressive MA (ARMA) AWSS vector processes.

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39

Paparoditis, Efstathios. "Addendum to “Bootstrapping periodogram and cross periodogram statistics of vector autoregressive moving average models”." Statistics & Probability Letters 37, no. 1 (January 1998): 109. http://dx.doi.org/10.1016/s0167-7152(97)80002-8.

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40

Mauricio, José Alberto. "Algorithm AS 311: The Exact Likelihood Function of a Vector Autoregressive Moving Average Model." Journal of the Royal Statistical Society: Series C (Applied Statistics) 46, no. 1 (January 1997): 157–71. http://dx.doi.org/10.1111/1467-9876.00056.

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41

MAURICIO, JOSE ALBERTO. "An algorithm for the exact likelihood of a stationary vector autoregressive-moving average model." Journal of Time Series Analysis 23, no. 4 (July 2002): 473–86. http://dx.doi.org/10.1111/1467-9892.00273.

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42

Menéndez García, Luis Alfonso, Fernando Sánchez Lasheras, Paulino José García Nieto, Laura Álvarez de Prado, and Antonio Bernardo Sánchez. "Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms." Mathematics 8, no. 12 (December 11, 2020): 2205. http://dx.doi.org/10.3390/math8122205.

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Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

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43

Spanos, Pol D., and Marc P. Mignolet. "Simulation of Homogeneous Two-Dimensional Random Fields: Part II—MA and ARMA Models." Journal of Applied Mechanics 59, no. 2S (June 1, 1992): S270—S277. http://dx.doi.org/10.1115/1.2899500.

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Alternatively to the autoregressive (AR) models examined in Part I, the determination of moving average (MA) algorithms for simulating realizations of twodimensional random fields with a specified (target) power spectrum is presented. First, the mathematical form of these models is addressed by considering infinitevariate vector processes of an appropriate spectral matrix. Next, the MA parameters are determined by relying on the maximization of an energy-like quantity. Then, a technique is formulated to derive an autoregressive moving average (ARMA) simulation algorithm from a prior MA approximation by relying on the minimization of frequency domain errors. Finally, these procedures are critically assessed and an example of application is presented.

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44

Hung, Ken, and Frank B. Alt. "The Effect of Misspecification in Vector Autoregressive Moving Average Models on Parameter Estimation and Forecasting." Communications in Statistics - Simulation and Computation 18, no. 2 (January 1989): 467–79. http://dx.doi.org/10.1080/03610918908812771.

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45

Effiong Usoro, Anthony. "Necessary Conditions for Isolation of Special Classes of Bilinear Autoregressive Moving Average Vector (BARMAV) Models." American Journal of Theoretical and Applied Statistics 7, no. 5 (2018): 180. http://dx.doi.org/10.11648/j.ajtas.20180705.13.

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46

Yap, Sook Fwe, and Gregory C. Reinsel. "Estimation and Testing for Unit Roots in a Partially Nonstationary Vector Autoregressive Moving Average Model." Journal of the American Statistical Association 90, no. 429 (March 1995): 253–67. http://dx.doi.org/10.1080/01621459.1995.10476509.

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47

Das, Samarjit. "Modelling money, price and output in India: a vector autoregressive and moving average (VARMA) approach." Applied Economics 35, no. 10 (June 30, 2003): 1219–25. http://dx.doi.org/10.1080/0003684032000090726.

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48

Shea, B. L. "A NOTE ON THE GENERATION OF INDEPENDENT REALIZATIONS OF A VECTOR AUTOREGRESSIVE MOVING-AVERAGE PROCESS." Journal of Time Series Analysis 9, no. 4 (July 1988): 403–10. http://dx.doi.org/10.1111/j.1467-9892.1988.tb00479.x.

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49

Boudjellaba, Hafida, Jean-Marie Dufour, and Roch Roy. "Testing Causality between Two Vectors in Multivariate Autoregressive Moving Average Models." Journal of the American Statistical Association 87, no. 420 (December 1992): 1082–90. http://dx.doi.org/10.1080/01621459.1992.10476263.

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50

Franchi, Massimo. "A REPRESENTATION THEORY FOR POLYNOMIAL COFRACTIONALITY IN VECTOR AUTOREGRESSIVE MODELS." Econometric Theory 26, no. 4 (November 4, 2009): 1201–17. http://dx.doi.org/10.1017/s0266466609990508.

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We extend the representation theory of the autoregressive model in the fractional lag operator of Johansen (2008, Econometric Theory 24, 651–676). A recursive algorithm for the characterization of cofractional relations and the corresponding adjustment coefficients is given, and it is shown under which condition the solution of the model is fractional of order d and displays cofractional relations of order d − b and polynomial cofractional relations of order d − 2b,…, d − cb ≥ 0 for integer c; the cofractional relations and the corresponding moving average representation are characterized in terms of the autoregressive coefficients by the same algorithm. For c = 1 and c = 2 we find the results of Johansen (2008).

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