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Artykuły w czasopismach na temat „Vector autoregression”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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

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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 fitt

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2

Zhu, Xuening, Rui Pan, Guodong Li, Yuewen Liu, and Hansheng Wang. "Network vector autoregression." Annals of Statistics 45, no. 3 (2017): 1096–123. http://dx.doi.org/10.1214/16-aos1476.

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3

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

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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

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4

Härdle, W., A. Tsybakov, and L. Yang. "Nonparametric vector autoregression." Journal of Statistical Planning and Inference 68, no. 2 (1998): 221–45. http://dx.doi.org/10.1016/s0378-3758(97)00143-2.

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5

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.

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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-tr

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6

Makletsov, Sergey V., and Nadezda A. Opokina. "The use of structural vector autoregression to assess the mutual impact of consumption and average wage levels." Journal Of Applied Informatics 20, no. 1 (2025): 5–15. https://doi.org/10.37791/2687-0649-2025-20-1-5-15.

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At present, it is an urgent task to describe macroeconomic phenomena through the construction of qualitative mathematical models. In the current economic landscape, the interaction among economic factors is of significant importance. Therefore, it is necessary to understand the relationships between key indicators that significantly contribute to economic growth. In particular, the interactions between these indicators can be investigated using vector autoregressive models, in which they are treated as endogenous variables. The strength of the relationship between variables can be determined t

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7

Feifei, Wang, Zhu Xuening, and Pan Rui. "Generalized network vector autoregression." SCIENTIA SINICA Mathematica 51, no. 8 (2020): 1253. http://dx.doi.org/10.1360/scm-2018-0839.

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8

Kalliovirta, Leena, Mika Meitz, and Pentti Saikkonen. "Gaussian mixture vector autoregression." Journal of Econometrics 192, no. 2 (2016): 485–98. http://dx.doi.org/10.1016/j.jeconom.2016.02.012.

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9

Lanne, Markku, and Jani Luoto. "Noncausal Bayesian Vector Autoregression." Journal of Applied Econometrics 31, no. 7 (2016): 1392–406. http://dx.doi.org/10.1002/jae.2497.

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10

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

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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.

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11

Elbourne, Adam, and Jakob de Haan. "Modeling Monetary Policy Transmission in Acceding Countries: Vector Autoregression Versus Structural Vector Autoregression." Emerging Markets Finance and Trade 45, no. 2 (2009): 4–20. http://dx.doi.org/10.2753/ree1540-496x450201.

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12

Harris, Glen R. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions." ASTIN Bulletin 29, no. 1 (1999): 47–79. http://dx.doi.org/10.2143/ast.29.1.504606.

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AbstractFinancial time series data are typically found to possess leptokurtic frequency distributions, time varying volatilities, outliers and correlation structures inconsistent with linear generating processes, nonlinear dependence, and dependencies between series that are not stable over time. Regime Switching Vector Autoregressions are of interest because they are capable of explaining the observed features of the data, can capture a variety of interactions between series, appear intuitively reasonable, are vector processes, and are now tractable.This paper considers a vector autoregressio

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13

Wang, Lei, and Shanshan Ding. "Vector autoregression and envelope model." Stat 7, no. 1 (2018): e203. http://dx.doi.org/10.1002/sta4.203.

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14

Kim, Sangtae, and Changryong Baek. "Banded vector heterogeneous autoregression models." Korean Journal of Applied Statistics 36, no. 6 (2023): 529–45. http://dx.doi.org/10.5351/kjas.2023.36.6.529.

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15

Tang, Yiming, Yang Bai, and Tao Huang. "Network vector autoregression with individual effects." Metrika 84, no. 6 (2021): 875–93. http://dx.doi.org/10.1007/s00184-020-00805-y.

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16

Brandt, Patrick T., and Todd Sandler. "A Bayesian Poisson Vector Autoregression Model." Political Analysis 20, no. 3 (2012): 292–315. http://dx.doi.org/10.1093/pan/mps001.

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Multivariate count models are rare in political science despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns a

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17

Teapon, Rizal Rahman H., and Rachman Dano Mustafa. "Shock of Monetary Policy Transmission and Macroeconomic Variable in Indonesia: A Structural VAR Approach." Jurnal Economia 14, no. 2 (2018): 177–96. http://dx.doi.org/10.21831/economia.v14i2.21480.

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Abstract: Shock of Monetary Policy Transmission and Macroeconomic Variable in Indonesia: A Structural VAR Approach. The purpose of this paper is to find out how much the shock of monetary policy transmission affects macroeconomic variables in Indonesia and vice versa by using Structural Vector Autoregression (SVAR) model. The results showed that the transmission of monetary policy in Indonesia only gives a weak influence toward inflation, but it greatly stimulates economic growth. However, the shock of macroeconomic variables influences the transmission of monetary policy in Indonesia signific

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18

Akylbekov, A. A., A. M. Seitkaziyeva, and Zh Sh Kenzhalina. "APPLICATION OF VECTOR AUTOREGRESSIONS FOR FORECASTING MONETARY POLICY." Central Asian Economic Review, no. 3 (October 6, 2023): 54–69. http://dx.doi.org/10.52821/2789-4401-2023-3-54-69.

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The purpose of the study is to consider the theoretical and empirical application of methods and models of vector autoregressions to analyze the infl uence of various macroeconomic variables in the construction of a monetary policy model.Methodology. The research methods used are generalization of experience regarding the use of vector autoregressions, factor analysis, methodology for evaluating VAR models containing fi fteen real, price, monetary and external variables. A number of tests were conducted to assess the quality of the analyzed model: impulse response analysis, forecasting and sim

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19

Fu, Wanying, Barry R. Smith, Patrick Brewer, and Sean Droms. "Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting." Risks 11, no. 9 (2023): 152. http://dx.doi.org/10.3390/risks11090152.

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We apply a Markov-switching Bayesian vector autoregression (MSBVAR) model to mortality forecasting. MSBVAR has not previously been applied in this context, and our results show that it is a promising tool for mortality forecasting. Our model shows better forecasting accuracy than the Lee–Carter and Bayesian vector autoregressive (BVAR) models without regime-switching and while retaining the advantages of BVAR. MSBVAR provides more reliable estimates for parameter uncertainty and more flexibility in the shapes of point-forecast curves and shapes of confidence intervals than BVAR. Through regime

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20

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

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21

Khatraty, Yahjeb Bouha, Nédra Mellouli Nauwynck, Mamadou Tourad Diallo, and Mohamedade Farouk Nanne. "Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture." International Journal of Emerging Technology and Advanced Engineering 12, no. 11 (2022): 79–88. http://dx.doi.org/10.46338/ijetae1122_09.

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Managing time series data generated by the intelligent objects around us requires cleaning and processing techniques, as well as a prediction model with high accuracy and low complexity. This study attempts to fuse climate time series data (Temperature, Humidity, Wind speed and Rainfall) and remote sensing data to predict rice yield. We performed statistical analysis of the data, additive decomposition, and measured the correlation between the different variables. We checked the stationarity of the data by using the Augmented Dickey-Fuller ADF test to apply statistical prediction models based

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22

Balatskiy, E. V., and M. A. Yurevich. "Inflation Forecasting: The Practice of Using Synthetic Procedures." World of new economy 12, no. 4 (2019): 20–31. http://dx.doi.org/10.26794/2220-6469-2018-12-4-20-31.

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The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the integrated model of autoregression with moving average. Also, we discussed the possibilities of various modifications of models based on the Phillips curve (including the “triangle model”), vector autoregressive models (including the factor-extended model of B. Bernanke’s vector autoregression), dynamic general equilibrium models and neural netwo

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23

Patel, Pratham, Dhruv Parmar, and Gaurav Kulkarni. "Temporal Regularized Matrix Factorization for High-Dimensional Time Series Forecasting." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43415.

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Time series forecasting plays a critical role in numerous domains, including finance, economics, climatology, and retail. The ability to predict future values based on historical patterns enables better decision-making, resource allocation, and risk management. Traditional approaches to time series forecasting include statistical methods such as autoregressive integrated moving average (ARIMA) models, exponential smoothing, and vector autoregression (VAR)[1]

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24

Rendu, Christel, and Ramana Ramaswamy. "Japan's Stagnant Nineties: A Vector Autoregression Retrospective." IMF Working Papers 99, no. 45 (1999): 1. http://dx.doi.org/10.5089/9781451846454.001.

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25

Ericsson, Neil R., and Erica L. Reisman. "Evaluating a Global Vector Autoregression for Forecasting." International Finance Discussion Paper 2012, no. 1056 (2012): 1–20. http://dx.doi.org/10.17016/ifdp.2012.1056.

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26

Daniel Felix Ahelegbey, Monica Billio, and Roberto Casarin. "Sparse Graphical Vector Autoregression: A Bayesian Approach." Annals of Economics and Statistics, no. 123/124 (2016): 333. http://dx.doi.org/10.15609/annaeconstat2009.123-124.0333.

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27

Freeman, John R., John T. Williams, and Tse-min Lin. "Vector Autoregression and the Study of Politics." American Journal of Political Science 33, no. 4 (1989): 842. http://dx.doi.org/10.2307/2111112.

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28

Huang, Xiao. "Panel vector autoregression under cross-sectional dependence." Econometrics Journal 11, no. 2 (2008): 219–43. http://dx.doi.org/10.1111/j.1368-423x.2008.00240.x.

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29

Davidson, James. "THE COINTEGRATION PROPERTIES OF VECTOR AUTOREGRESSION MODELS." Journal of Time Series Analysis 12, no. 1 (2008): 41–62. http://dx.doi.org/10.1111/j.1467-9892.1991.tb00067.x.

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30

Abrigo, Michael R. M., and Inessa Love. "Estimation of Panel Vector Autoregression in Stata." Stata Journal: Promoting communications on statistics and Stata 16, no. 3 (2016): 778–804. http://dx.doi.org/10.1177/1536867x1601600314.

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31

Spencer, David E. "Developing a Bayesian vector autoregression forecasting model." International Journal of Forecasting 9, no. 3 (1993): 407–21. http://dx.doi.org/10.1016/0169-2070(93)90034-k.

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32

Njenga, Carolyn Ndigwako, and Michael Sherris. "Modeling mortality with a Bayesian vector autoregression." Insurance: Mathematics and Economics 94 (September 2020): 40–57. http://dx.doi.org/10.1016/j.insmatheco.2020.05.011.

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33

Phillips, Peter C. B. "Fully Modified Least Squares and Vector Autoregression." Econometrica 63, no. 5 (1995): 1023. http://dx.doi.org/10.2307/2171721.

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34

Wong, J. K., and P. L. Jolly. "A Bayesian vector autoregression model of inflation." New Zealand Economic Papers 28, no. 2 (1994): 117–31. http://dx.doi.org/10.1080/00779959409544220.

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35

Burbidge, John B., L. Magee, and Michael R. Veall. "On the seasonality of vector autoregression residuals." Economics Letters 18, no. 2-3 (1985): 137–41. http://dx.doi.org/10.1016/0165-1765(85)90168-5.

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36

Chu, Chi-Hsiang, Mong-Na Lo Huang, Shih-Feng Huang, and Ray-Bing Chen. "Bayesian structure selection for vector autoregression model." Journal of Forecasting 38, no. 5 (2019): 422–39. http://dx.doi.org/10.1002/for.2573.

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37

Ericsson, Neil R., and Erica L. Reisman. "Evaluating a Global Vector Autoregression for Forecasting." International Advances in Economic Research 18, no. 3 (2012): 247–58. http://dx.doi.org/10.1007/s11294-012-9357-0.

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38

Ramaswamy, Ramana, and Christel Rendu. "Japan's Stagnant Nineties: A Vector Autoregression Retrospective." IMF Staff Papers 47, no. 2 (2000): 259–77. http://dx.doi.org/10.2307/3867661.

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39

Ding, Yishan, Dongwei He, Jun Wu, and Xiang Xu. "Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression." Complexity 2022 (November 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5011174.

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This paper proposes a forecasting methodology that investigates a set of different sparse structures for the vector autoregression (VAR) model using the Ivanov-based least absolute shrinkage and selection operator (LASSO) framework. The variant auxiliary problem principle method is used to solve the various Ivanov-based LASSO-VAR variants, which is supported by parallel computing with simple closed-form iteration and linear convergence rate. A test case with ten crude oil spot prices is used to demonstrate the improvement in forecasting skills gained from exploring sparse structures. The propo

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40

Zarepour, M., and S. M. Roknossadati. "MULTIVARIATE AUTOREGRESSION OF ORDER ONE WITH INFINITE VARIANCE INNOVATIONS." Econometric Theory 24, no. 3 (2008): 677–95. http://dx.doi.org/10.1017/s0266466608080286.

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We consider the limiting behavior of a vector autoregressive model of order one (VAR(1)) with independent and identically distributed (i.i.d.) innovations vector with dependent components in the domain of attraction of a multivariate stable law with possibly different indices of stability. It is shown that in some cases the ordinary least squares (OLS) estimates are inconsistent. This inconsistency basically originates from the fact that each coordinate of the partial sum processes of dependent i.i.d. vectors of innovations in the domain of attraction of stable laws needs a different normalize

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41

Buldygin, V. V., and V. A. Koval. "Convergence to Zero and Boundedness of Operator-Normed Sums of Random Vectors with Application to Autoregression Processes." gmj 8, no. 2 (2001): 221–30. http://dx.doi.org/10.1515/gmj.2001.221.

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Abstract The problems of almost sure convergence to zero and almost sure boundedness of operator-normed sums of different sequences of random vectors are studied. The sequences of independent random vectors, orthogonal random vectors and the sequences of vector-valued martingale-differences are considered. General results are applied to the problem of asymptotic behaviour of multidimensional autoregression processes.

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42

Li, Xiaozhong, and Feng Huang. "Empirical Study on the Relationship between Agricultural Economic Structure Growth and Environmental Pollution Based on Time-Varying Parameter Vector Autoregressive Model." Journal of Environmental and Public Health 2022 (August 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/5684178.

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In order to better demonstrate the relationship between agricultural economic structure growth and environmental pollution, an autoregressive model based on time-varying parameter vector was proposed. In the process of developing the research, this paper introduces the LDMI method, based on the time-varying parameter vector autoregression model, with the help of sampling formula calculation and other methods. Efforts were made to obtain credible conclusions. The experiment result shows that in this study, a total of 10,000 samples were taken. According to this value, 10000/116.15 = 86, which m

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43

Widya, Bernadus Divanda, Agus Rusgiyono, and Iut Tri Utami. "PERAMALAN HARGA PASAR TELUR AYAM RAS MENGGUNAKAN VECTOR AUTOREGRESSIVE (VAR) (Studi Kasus: Harga Pasar Telur Ayam Ras di eks Karesidenan Surakarta Tahun 2020-2022)." Jurnal Gaussian 14, no. 1 (2025): 97–106. https://doi.org/10.14710/j.gauss.14.1.97-106.

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The stabilization of the market price of broiler eggs is one of the points that the government should pay attention to. This is to prevent significant price increases or decreases. Government precautions to keep broiler egg prices stable can be done through forecasting. Vector autoregression (VAR) is a time series model that can be used to model and forecast data containing multiple variables at once. VAR models were constructed to estimate relationships between economic variables without paying attention to exogenous issues. The level of predictive accuracy of vector autoregressive (VAR) mode

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44

KÜÇÜKEFE, Bige, and Dündar Murat DEMİRÖZ. "FAVAR (Factor-Augmented Vector Autoregression) Modeli Literatür Taraması." Fiscaoeconomia 1, no. 2 (2017): 38. http://dx.doi.org/10.25295/fsecon.295547.

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45

Emerencia, Ando Celino, Lian van der Krieke, Elisabeth H. Bos, Peter de Jonge, Nicolai Petkov, and Marco Aiello. "Automating Vector Autoregression on Electronic Patient Diary Data." IEEE Journal of Biomedical and Health Informatics 20, no. 2 (2016): 631–43. http://dx.doi.org/10.1109/jbhi.2015.2402280.

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46

Lanne, Markku, and Jani Luoto. "GMM Estimation of Non-Gaussian Structural Vector Autoregression." Journal of Business & Economic Statistics 39, no. 1 (2019): 69–81. http://dx.doi.org/10.1080/07350015.2019.1629940.

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47

Ni, Shawn, Dongchu Sun, and Xiaoqian Sun. "Intrinsic Bayesian Estimation of Vector Autoregression Impulse Responses." Journal of Business & Economic Statistics 25, no. 2 (2007): 163–76. http://dx.doi.org/10.1198/073500106000000378.

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48

Hyatt, Henry R., and Tucker S. McElroy. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence." LABOUR 33, no. 4 (2019): 463–87. http://dx.doi.org/10.1111/labr.12153.

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49

Crompton, Paul, and Yanrui Wu. "Bayesian Vector Autoregression Forecasts of Chinese Steel Consumption." Journal of Chinese Economic and Business Studies 1, no. 2 (2003): 205–19. http://dx.doi.org/10.1080/1476528032000066703e.

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50

Sanni, T. A. "Vector Autoregression on Nigerian Money and Agricultural Aggregates." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 34, no. 1 (1986): 67–85. http://dx.doi.org/10.1111/j.1744-7976.1986.tb02193.x.

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