Log in

Relevant bibliographies by topics / Vector autoregressive model / Journal articles

To see the other types of publications on this topic, follow the link: Vector autoregressive model.

Journal articles on the topic 'Vector autoregressive model'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 May 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Vector autoregressive model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Shapor, Maria Alexandrovna, and Rafael Rubenovich Gevogyan. "Features of the vector autoregression models application in macroeconomic research." Mezhdunarodnaja jekonomika (The World Economics), no. 8 (August 10, 2021): 634–49. http://dx.doi.org/10.33920/vne-04-2108-05.

Full text

Abstract:

In this paper, we analyzed articles by foreign authors that use various vector autoregression models to calculate the impact of qualitative indicators on the economic processes of countries or a group of countries. In particular, the article analyzed the classical model of vector autoregression (VAR), panel model of autoregressive (PVAR), Bayesian model of autoregressive (BVAR), structural model of autoregressive (SVAR), and the global model of autoregressive (GVAR). Among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is because financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. The studied articles analyze the data of both developed and developing states or groups of states in different periods. The studied articles were classified according to several criteria, which were selected by the author to structure the work. Note that among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is since financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. In the conclusion of this study, the author presented conclusions based on the analysis of autoregressive models.

APA, Harvard, Vancouver, ISO, and other styles

2

Euán, Carolina, and Ying Sun. "Bernoulli vector autoregressive model." Journal of Multivariate Analysis 177 (May 2020): 104599. http://dx.doi.org/10.1016/j.jmva.2020.104599.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Barr, G. D. I. "“A Vector Autoregressive Model” - Reply." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 89. http://dx.doi.org/10.1080/03796205.1990.12128994.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Bouwer, B. "“A Vector Autoregressive Model” - Kommentaar." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 87. http://dx.doi.org/10.1080/03796205.1990.12128993.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Koop, Gary, and Dimitris Korobilis. "Model uncertainty in Panel Vector Autoregressive models." European Economic Review 81 (January 2016): 115–31. http://dx.doi.org/10.1016/j.euroecorev.2015.09.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Grynkiv, Galyna, and Lars Stentoft. "Stationary Threshold Vector Autoregressive Models." Journal of Risk and Financial Management 11, no. 3 (August 5, 2018): 45. http://dx.doi.org/10.3390/jrfm11030045.

Full text

Abstract:

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 assessing the stationarity of threshold models based on simulations might well lead to wrong conclusions.

APA, Harvard, Vancouver, ISO, and other styles

7

Zhu, Huafeng, Xingfa Zhang, Xin Liang, and Yuan Li. "On a vector double autoregressive model." Statistics & Probability Letters 129 (October 2017): 86–95. http://dx.doi.org/10.1016/j.spl.2017.05.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Fong, P. W., W. K. Li, C. W. Yau, and C. S. Wong. "On a mixture vector autoregressive model." Canadian Journal of Statistics 35, no. 1 (March 2007): 135–50. http://dx.doi.org/10.1002/cjs.5550350112.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Chapman, David, Mark A. Cane, Naomi Henderson, Dong Eun Lee, and Chen Chen. "A Vector Autoregressive ENSO Prediction Model." Journal of Climate 28, no. 21 (October 30, 2015): 8511–20. http://dx.doi.org/10.1175/jcli-d-15-0306.1.

Full text

Abstract:

Abstract The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.

APA, Harvard, Vancouver, ISO, and other styles

10

Bouwer, B. "“A Vector Autoregressive Model” - Verdere Kommentaar." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 91–92. http://dx.doi.org/10.1080/03796205.1990.12128995.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Tovstik, T. M. "Vector autoregression process. Stationarity and simulation." Journal of Physics: Conference Series 2099, no. 1 (November 1, 2021): 012068. http://dx.doi.org/10.1088/1742-6596/2099/1/012068.

Full text

Abstract:

Abstract For vector discrete-parameter random autoregressive processes and for a mixed autoregression/moving-average model, we obtain conditions which should be satisfied by the correlation functions or the model coefficients in order that the process be weakly stationary. Fairly simple tests are used. Algorithms for modeling such vector stationary processes are given. Examples are presented clarifying testing criteria for stationarity of models defned in terms of the coefficients or the correlation functions of the process.

APA, Harvard, Vancouver, ISO, and other styles

12

Wong, C. S. "On a constrained mixture vector autoregressive model." Mathematics and Computers in Simulation 93 (July 2013): 19–28. http://dx.doi.org/10.1016/j.matcom.2013.05.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Chen, Cathy W. S., Hong Than-Thi, and Mike K. P. So. "On hysteretic vector autoregressive model with applications." Journal of Statistical Computation and Simulation 89, no. 2 (October 30, 2018): 191–210. http://dx.doi.org/10.1080/00949655.2018.1540619.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Cheng, Tsung-Chi, Ping-Hung Hsieh, and Su-Fen Yang. "Process Control for the Vector Autoregressive Model." Quality and Reliability Engineering International 30, no. 1 (December 17, 2012): 57–81. http://dx.doi.org/10.1002/qre.1477.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Liu, Yonghui, Guocheng Ji, and Shuangzhe Liu. "Influence diagnostics in a vector autoregressive model." Journal of Statistical Computation and Simulation 85, no. 13 (October 14, 2014): 2632–55. http://dx.doi.org/10.1080/00949655.2014.967243.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Tang, Han. "Uncertain vector autoregressive model with imprecise observations." Soft Computing 24, no. 22 (May 16, 2020): 17001–7. http://dx.doi.org/10.1007/s00500-020-04991-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Liao, Zhipeng, and Peter C. B. Phillips. "AUTOMATED ESTIMATION OF VECTOR ERROR CORRECTION MODELS." Econometric Theory 31, no. 3 (March 13, 2015): 581–646. http://dx.doi.org/10.1017/s026646661500002x.

Full text

Abstract:

Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, discusses implementation, reports simulations, and presents an empirical illustration with macroeconomic aggregates.

APA, Harvard, Vancouver, ISO, and other styles

18

Lanne, Markku, and Pentti Saikkonen. "NONCAUSAL VECTOR AUTOREGRESSION." Econometric Theory 29, no. 3 (November 12, 2012): 447–81. http://dx.doi.org/10.1017/s0266466612000448.

Full text

Abstract:

In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.

APA, Harvard, Vancouver, ISO, and other styles

19

Framroze Møller, Niels. "Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model." Economics: The Open-Access, Open-Assessment E-Journal 2, no. 2008-36 (2008): 1. http://dx.doi.org/10.5018/economics-ejournal.ja.2008-36.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Sathyanarayana, S., and Sudhindra Gargesa. "Modeling Cryptocurrency (Bitcoin) using Vector Autoregressive (Var) Model." SDMIMD Journal of Management 10, no. 2 (September 12, 2019): 47–64. http://dx.doi.org/10.18311/sdmimd/2019/23181.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Johansen, Søren. "Modelling of cointegration in the vector autoregressive model." Economic Modelling 17, no. 3 (August 2000): 359–73. http://dx.doi.org/10.1016/s0264-9993(99)00043-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Winegarden, C. R. "AFDC and illegitimacy ratios: a vector-autoregressive model." Applied Economics 20, no. 12 (December 1988): 1589–601. http://dx.doi.org/10.1080/00036848800000090.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Hansen, Peter Reinhard. "Structural changes in the cointegrated vector autoregressive model." Journal of Econometrics 114, no. 2 (June 2003): 261–95. http://dx.doi.org/10.1016/s0304-4076(03)00085-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

RAMIREZ-BELTRAN, NAZARIO D. "A vector autoregressive model to predict hurricane tracks." International Journal of Systems Science 27, no. 1 (January 1996): 1–10. http://dx.doi.org/10.1080/00207729608929183.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Shin, Andrew Jaeho, Minsu Park, and Changryong Baek. "Sparse vector heterogeneous autoregressive model with nonconvex penalties." Communications for Statistical Applications and Methods 29, no. 1 (January 31, 2022): 733–44. http://dx.doi.org/10.29220/csam.2022.29.1.733.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Shin, Andrew Jaeho, Minsu Park, and Changryong Baek. "Sparse vector heterogeneous autoregressive model with nonconvex penalties." Communications for Statistical Applications and Methods 29, no. 1 (January 31, 2022): 53–64. http://dx.doi.org/10.29220/csam.2022.29.1.053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

28

Piltan, Farzin, Bach Phi Duong, and Jong-Myon Kim. "Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification." Sensors 21, no. 6 (March 17, 2021): 2102. http://dx.doi.org/10.3390/s21062102.

Full text

Abstract:

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

APA, Harvard, Vancouver, ISO, and other styles

29

Jiang, Han, Yajie Zou, Shen Zhang, Jinjun Tang, and Yinhai Wang. "Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/9236156.

Full text

Abstract:

Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN), nonlinear autoregressive model with exogenous inputs neural network (NARXNN), support vector machine with radial basis function as kernel function (SVM-RBF), Support Vector Machine with Linear Function (SVM-LIN), and Multilinear Regression (MLR) as candidate. Three statistical models are also selected: Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Space-Time (ST) model. From the prediction results, we find the following meaningful results: (1) the prediction accuracy of speed deteriorates as the prediction time steps increase for all models; (2) the BPNN, NARXNN, and SVM-RBF can clearly outperform two traditional statistical models: ARIMA and VAR; (3) the prediction performance of ANN is superior to that of SVM and MLR; (4) as time step increases, the ST model can consistently provide the lowest MAE comparing with ARIMA and VAR.

APA, Harvard, Vancouver, ISO, and other styles

30

Dufour, Jean-Marie. "Unbiasedness of Predictions from Etimated Vector Autoregressions." Econometric Theory 1, no. 3 (December 1985): 387–402. http://dx.doi.org/10.1017/s0266466600011270.

Full text

Abstract:

Forecasts from a univariate autoregressive model estimated by OLS are unbiased, irrespective of whether the model fitted has the correct order; this property only requires symmetry of the distribution of the innovations. In this paper, this result is generalized to vector autoregressions and a wide class of multivariate stochastic processes (which include Gaussian stationary multivariate stochastic processes) is described for which unbiasedness of predictions holds: specifically, if a vector autoregression of arbitrary finite order is fitted to a sample from any process in this class, the fitted model will produce unbiased forecasts, in the sense that the prediction errors have distributions symmetric about zero. Different numbers of lags may be used for each variable in each autoregression and variables may even be missing, without unbiasedness being affected. This property is exact in finite samples. Similarly, the residuals from the same autoregressions have distributions symmetric about zero.

APA, Harvard, Vancouver, ISO, and other styles

31

Otter, Pieter W. "On model reduction and multiperiod ahead prediction in vector autoregressive models." Economic Modelling 12, no. 4 (October 1995): 339–41. http://dx.doi.org/10.1016/0264-9993(95)00023-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Saikkonen, Pentti, and HELMUT Lütkepohl. "Infinite-Order Cointegrated Vector Autoregressive Processes." Econometric Theory 12, no. 5 (December 1996): 814–44. http://dx.doi.org/10.1017/s0266466600007179.

Full text

Abstract:

Estimation of cointegrated systems via autoregressive approximation is considered in the framework developed by Saikkonen (1992, Econometric Theory 8, 1-27). The asymptotic properties of the estimated coefficients of the autoregressive error correction model (ECM) and the pure vector autoregressive (VAR) representations are derived under the assumption that the autoregressive order goes to infinity with the sample size. These coefficients are often used for analyzing the relationships between the variables; therefore, they are important for applied work. Tests for linear restrictions on the coefficients of both the ECM and the pure VAR representation are considered under the present assumptions. It is found that they have limiting x2 distributions. Tests are also derived under the assumption that the number of restrictions goes to infinity with the sample size.

APA, Harvard, Vancouver, ISO, and other styles

33

Ningrum, Dewi Kusuma, and Sugiyarto Surono. "Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)." JURNAL EKSAKTA 18, no. 2 (September 27, 2018): 167–77. http://dx.doi.org/10.20885/eksakta.vol18.iss2.art8.

Full text

Abstract:

Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of Autoregressive distributed-lag model and Vector Autoregressive (VAR) method for the forecasting for export amount in DIY. It takes export amount in DIY and inflation data, kurs, and Indonesias foreign exchange reserve. Forecasting formation: defining Koyck and Almon distributed-lag dynamic model, then the best model is chosen and distribution-lag dynamic forecasting is performed. After that it is performed stationary test, co-integration test, optimal lag examination, granger causality test, parameter estimation, VAR model stability, and performs forecasting with VAR method. The forecasting result shows MAPE value from ARDL method obtained is 0.475812%, while MAPE value from VAR method is 0.464473%. Thus it can be concluded that Vector Autoregressive (VAR) method is more effective to be used in case study of export amount in DIY forecasting. Keywords: Koyck; Almon; Lag; Autoregressive Distributed-Lag; Vector Autoregressive;

APA, Harvard, Vancouver, ISO, and other styles

34

Wu, Ping, ChunJie Yang, and ZhiHuan Song. "Recursive Subspace Model Identification Based On Vector Autoregressive Modelling." IFAC Proceedings Volumes 41, no. 2 (2008): 8872–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01499.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Melnyk, Igor, Bryan Matthews, Hamed Valizadegan, Arindam Banerjee, and Nikunj Oza. "Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems." Journal of Aerospace Information Systems 13, no. 4 (April 2016): 161–73. http://dx.doi.org/10.2514/1.i010394.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Danuletiu, Adina Elena, Iulia Cristina Iuga, and Adela Socol. ""Investigating Banking Households' Deposits Using Vector Autoregressive Model Var "." Annales Universitatis Apulensis Series Oeconomica 1, no. 16 (June 30, 2014): 85–103. http://dx.doi.org/10.29302/oeconomica.2014.16.1.8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Lee, Wonseok, and Changryong Baek. "The sparse vector autoregressive model for PM10 in Korea." Journal of the Korean Data and Information Science Society 25, no. 4 (July 31, 2014): 807–17. http://dx.doi.org/10.7465/jkdi.2014.25.4.807.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Ren, Yunwen, Zhiguo Xiao, and Xinsheng Zhang. "Two-step adaptive model selection for vector autoregressive processes." Journal of Multivariate Analysis 116 (April 2013): 349–64. http://dx.doi.org/10.1016/j.jmva.2013.01.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Rhee, Kang Hyeon. "Improvement Feature Vector: Autoregressive Model of Median Filter Residual." IEEE Access 7 (2019): 77524–40. http://dx.doi.org/10.1109/access.2019.2921573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Shim, Jooyong, and Hong Chong Cho. "Forecasting LNG prices with the kernel vector autoregressive model." Geosystem Engineering 23, no. 1 (September 11, 2019): 37–42. http://dx.doi.org/10.1080/12269328.2019.1664337.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Ren, Yunwen, and Xinsheng Zhang. "Model Selection for Vector Autoregressive Processes via Adaptive Lasso." Communications in Statistics - Theory and Methods 42, no. 13 (July 3, 2013): 2423–36. http://dx.doi.org/10.1080/03610926.2011.611317.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Rosser, J. Barkley, and Richard G. Sheehan. "A vector autoregressive model of the Saudi Arabian economy." Journal of Economics and Business 47, no. 1 (February 1995): 79–90. http://dx.doi.org/10.1016/0148-6195(94)00025-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Johansen, Søren, and Morten Ørregaard Nielsen. "The cointegrated vector autoregressive model with general deterministic terms." Journal of Econometrics 202, no. 2 (February 2018): 214–29. http://dx.doi.org/10.1016/j.jeconom.2017.10.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Johansen, Søren. "Some identification problems in the cointegrated vector autoregressive model." Journal of Econometrics 158, no. 2 (October 2010): 262–73. http://dx.doi.org/10.1016/j.jeconom.2010.01.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

MATSUOKA, Kodai, and Kiyoyuki KAITO. "HIERARCHICAL BAYESIAN ESTIMATION OF TIME VARYING VECTOR AUTOREGRESSIVE MODEL." Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering ^|^ Earthquake Engineering (SE/EE)) 68, no. 3 (2012): 738–53. http://dx.doi.org/10.2208/jscejseee.68.738.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Iwok, I. A. "Vector bilinear autoregressive time series model and its superiority over its linear autoregressive counterpart." Global Journal of Pure and Applied Sciences 22, no. 1 (October 13, 2016): 51. http://dx.doi.org/10.4314/gjpas.v22i1.7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Sihombing, Pardomuan, and Bekti Endar Susilowati. "Aplikasi Model Vector Autoregressive (VAR) pada Data Tamu Mancanegara di Hotel Bintang dan Non Bintang di Daerah Istimewa Yogyakarta." Jurnal Statistika dan Aplikasinya 3, no. 2 (December 30, 2019): 6–15. http://dx.doi.org/10.21009/jsa.03202.

Full text

Abstract:

Model Vector Autoregressive (VAR) merupakan gabungan dari beberapa model Autoregressive (AR), dimana model membentuk sebuah vektor yang antara variabel-variabelnya saling memengaruhi. Model AR(1) menyatakan bahwa pengamatan waktu sekarang dipengaruhi pengamatan satu waktu sebelumnya dan unsur error. Pada analisis ini, model Vector Autoregressive (VAR) digunakan pada data tamu mancanegara per bulan yang menginap di Hotel Bintang dan Non bintang di Daerah Istimewa Yogyakarta per bulan periode Januari 2008 sampai dengan Desember 2015. Pembentukan model VAR melalui beberapa tahap yaitu: uji stasioneritas, penentuan orde autoregressive, pembentukan model VAR, dan diagnostic checking. Untuk pengolahan data dilakukan dengan program R 3.5.1. Dari analisis data, variabel jumlah tamu wisatawan mancanegara di Hotel Bintang dan Hotel Non Bintang di Daerah Istimewa Yogyakarta memiliki korelasi yang cukup tinggi yaitu sebesar 0,91. Dengan model Vector Autoregressive (VAR) yaitu VAR(1) didapatkan kedua hasil persamaan simultan yang signifikan. Nilai R2 dan Adjusted R2 kedua persamaan parsial model VAR(1) cukup tinggi yaitu untuk persamaan variabel Hotel Bintang didapatkan R2 sebesar 71,13% dan Adjusted R2 70,5%, sedangkan untuk persamaan variabel Hotel Non Bintang didapatkan R2 sebesar 76,56% dan dan Adjusted R2 70,65 %.

APA, Harvard, Vancouver, ISO, and other styles

48

Kim, Yunsun, and Sahm Kim. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models." Mathematics 9, no. 18 (September 21, 2021): 2347. http://dx.doi.org/10.3390/math9182347.

Full text

Abstract:

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study.

APA, Harvard, Vancouver, ISO, and other styles

49

Chen, Cathy W. S., and L. M. Chiu. "Ordinal Time Series Forecasting of the Air Quality Index." Entropy 23, no. 9 (September 4, 2021): 1167. http://dx.doi.org/10.3390/e23091167.

Full text

Abstract:

This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogenous variables and GARCH (generalized autoregressive conditional heteroskedasticity) errors; (2) an autoregressive multinomial logistic regression; (3) multi-class classification by support vector machine (SVM); (4) neural network autoregression with exogenous variable (NNARX). These models relate to lag-1 AQI values and the previous day’s weather covariates (precipitation and temperature), while wind direction serves as an hour-lag effect based on the idea of nowcasting. The results demonstrate that autoregressive multinomial logistic regression and the SVM method are the best choices for AQI-level predictions regarding the high average and low variation accuracy rates.

APA, Harvard, Vancouver, ISO, and other styles

50

Iskandar, Iskandar. "ANALISIS VECTOR AUTOREGRESSION (VAR) TERHADAP INTERRELATIONSHIP ANTARA FINANCING DEPOSIT RATIO (FDR) DAN RETURN ON ASSET (ROA) PADA BANK SYARIAH DI INDONESIA." Jurnal Ekonomi Syariah, Akuntansi dan Perbankan (JESKaPe) 3, no. 2 (November 8, 2019): 19–39. http://dx.doi.org/10.52490/jeskape.v3i2.430.

Full text

Abstract:

The Vector Autoregressive Model (VAR) is a very useful analytical tool in understanding the existence of interrelationships between economic variables and in the formation of a structured economy. This study aims to explain the analysis of the Vector Autoregressive (VAR) model and explain the application of the Vector Autoregressive (VAR) model for influence analysis. The FDR ratio in the Sharia Commercial banks tends to be stable. This is illustrated from the coefficient of determination which is almost close to 100%, namely 91.55%. Cointegration test results show there is no long-term balance relationship between FDR variables with ROA of Islamic banks in Indonesia. ROA has a positive effect on ROA while ROA on FDR is negative, meaning that FDR makes a small contribution to ROA.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!