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Articles de revues sur le sujet « Bayesian VARX »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Belomestny, Denis, Ekaterina Krymova, and Andrey Polbin. "Bayesian TVP-VARX models with time invariant long-run multipliers." Economic Modelling 101 (August 2021): 105531. http://dx.doi.org/10.1016/j.econmod.2021.105531.

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Elias, Intisar, and Taha Hussein Ali. "Choosing an Appropriate Wavelet for VARX Time Series Model Analysis." Journal of Economics and Administrative Sciences 31, no. 146 (2025): 174–96. https://doi.org/10.33095/px3b7908.

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The paper purports to improve the accuracy of VARX (vector autoregressive with exogenous variables) models adopted for economic time series analysis through wavelet transform techniques applied for noise reduction. The research assessed various wavelet types, including Coiflets, Daubechies, Symlets, Biorthogonal, and Reverse Biorthogonal, for the most appropriate wavelet to be used for improving the performance of the models. Furthermore, it made use of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for evaluating the efficacy of each wavelet in the dimension o

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Marchev, Angel, and Boyan Lomev. "Forecasting of the Event-driven Processes Using LSTM Network in the Context of Time of Arrival of On-demand City Transport." IOP Conference Series: Materials Science and Engineering 1317, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1757-899x/1317/1/012006.

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Abstract The precise forecasting of bus arrival times is an important element of implementing on demand city transport. This research uses of Long Short Term Memory (LSTM) networks for predicting bus arrival times in Sofia, Bulgaria. We evaluate the LSTM model against advanced models such as ARIMAX, VARX SARIMAX with Fourier terms Vector Autoregression, Bayesian Fourier models and Backpropagation Neural Networks using Root Mean Squared Error (RMSE) as the performance measure. The results points towards LSTM being better than approaches on routes by adeptly capturing intricate temporal relation

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Oo, May Zun, Chukiat Chaiboonsri, and Kanchana Chokethaworn. "The Impact of Political Transition on Myanmar's Border Trade with Thailand, China, and India after 2021 Myanmar Military Coup: A Panel Analysis." International Journal of Science and Social Science Research 3, no. 1 (2025): 119–28. https://doi.org/10.5281/zenodo.15385589.

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This study investigates Myanmar benefited from competitive advantages in the ASEAN Community through data collection and observation before and after the 2021 Myanmar Military Coup. In our study, it consists of the exchange of goods and services between Myanmar and its neighboring counties, Thailand, China and India through their shared borders often important for economically interdependent regions. The trade involves agricultural products, raw materials, and manufactured goods. Myanmar Military Coup in 2021 can impact especially on the trade because of the changes of customs policies, securi

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Marcos, Vinícius Monteiro da Rocha. "OS STAKEHOLDERS DAS COOPERATIVAS." Revistaft 28, no. 131 (2024): 21. https://doi.org/10.5281/zenodo.10695021.

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O objetivo deste trabalho é, por meio da teoria proposta por Freeman (1984), identificar e analisar a relação dos stakeholders das cooperativas utilizando como referência a teoria da saliência dos stakeholders. Os dados foram coletados através de questionário online direcionado aos gestores de alto escalão da organização. Foi coletado um grupo de variáveis independentes (atributos das cooperativas) e variáveis independentes (percepção dos stakeholders) e a análise de dados será por mei

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Eraker, Bjørn, Ching Wai (Jeremy) Chiu, Andrew T. Foerster, Tae Bong Kim, and Hernán D. Seoane. "Bayesian Mixed Frequency VARs." Journal of Financial Econometrics 13, no. 3 (2014): 698–721. http://dx.doi.org/10.1093/jjfinec/nbu027.

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7

Billio, Monica, Roberto Casarin, and Luca Rossini. "Bayesian nonparametric sparse VAR models." Journal of Econometrics 212, no. 1 (2019): 97–115. http://dx.doi.org/10.1016/j.jeconom.2019.04.022.

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8

Yoon, Byung-Jo. "A Study on Economic Policy Uncertainty and Stock Market Using Bayesian Time-Varying Parameter VAR Model." INTERNATIONAL BUSINESS REVIEW 24, no. 3 (2020): 85–93. http://dx.doi.org/10.21739/ibr.2020.09.24.3.85.

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9

Chan, Joshua C. C. "Asymmetric conjugate priors for large Bayesian VARs." Quantitative Economics 13, no. 3 (2022): 1145–69. http://dx.doi.org/10.3982/qe1381.

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Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross‐variable shrinkage, that is, shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross‐variable shrinkage, while maintaining many useful analytical results, such as a closed‐

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Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino. "Common Drifting Volatility in Large Bayesian VARs." Journal of Business & Economic Statistics 34, no. 3 (2016): 375–90. http://dx.doi.org/10.1080/07350015.2015.1040116.

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11

Carriero, Andrea, Michael P. Clements, and Ana Beatriz Galvão. "Forecasting with Bayesian multivariate vintage-based VARs." International Journal of Forecasting 31, no. 3 (2015): 757–68. http://dx.doi.org/10.1016/j.ijforecast.2014.05.007.

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12

Koop, Gary M. "Forecasting with Medium and Large Bayesian VARS." Journal of Applied Econometrics 28, no. 2 (2011): 177–203. http://dx.doi.org/10.1002/jae.1270.

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Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino. "Bayesian VARs: Specification Choices and Forecast Accuracy." Journal of Applied Econometrics 30, no. 1 (2013): 46–73. http://dx.doi.org/10.1002/jae.2315.

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14

Korobilis, Dimitris. "VAR FORECASTING USING BAYESIAN VARIABLE SELECTION." Journal of Applied Econometrics 28, no. 2 (2011): 204–30. http://dx.doi.org/10.1002/jae.1271.

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15

Shaffer, Michael J. "Bayesianism, Convergence and Social Epistemology." Episteme 5, no. 2 (2008): 203–19. http://dx.doi.org/10.3366/e1742360008000324.

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ABSTRACTFollowing the standard practice in sociology, cultural anthropology and history, sociologists, historians of science and some philosophers of science define scientific communities as groups with shared beliefs, values and practices. In this paper it is argued that in real cases the beliefs of the members of such communities often vary significantly in important ways. This has rather dire implications for the convergence defense against the charge of the excessive subjectivity of subjective Bayesianism because that defense requires that communities of Bayesian inquirers share a signific

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16

Huber, Florian, Tamás Krisztin, and Philipp Piribauer. "FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS." Bulletin of Economic Research 69, no. 3 (2016): 288–308. http://dx.doi.org/10.1111/boer.12094.

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Bodnar, Taras, Mathias Lindholm, Vilhelm Niklasson, and Erik Thorsén. "Bayesian portfolio selection using VaR and CVaR." Applied Mathematics and Computation 427 (August 2022): 127120. http://dx.doi.org/10.1016/j.amc.2022.127120.

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18

Sun, Dongchu, and Shawn Ni. "A Bayesian analysis of normalized VAR models." Journal of Multivariate Analysis 124 (February 2014): 247–59. http://dx.doi.org/10.1016/j.jmva.2013.11.004.

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19

George, Edward I., Dongchu Sun, and Shawn Ni. "Bayesian stochastic search for VAR model restrictions." Journal of Econometrics 142, no. 1 (2008): 553–80. http://dx.doi.org/10.1016/j.jeconom.2007.08.017.

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20

Chin, Kuo-Hsuan, and Xue Li. "Bayesian forecast combination in VAR-DSGE models." Journal of Macroeconomics 59 (March 2019): 278–98. http://dx.doi.org/10.1016/j.jmacro.2018.12.004.

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21

Chan, Joshua C. C. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs." International Journal of Forecasting 37, no. 3 (2021): 1212–26. http://dx.doi.org/10.1016/j.ijforecast.2021.01.002.

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Chan, Joshua C. C. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure." Journal of Business & Economic Statistics 38, no. 1 (2018): 68–79. http://dx.doi.org/10.1080/07350015.2018.1451336.

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23

Cobb, Marcus P. A. "Aggregate density forecasting from disaggregate components using Bayesian VARs." Empirical Economics 58, no. 1 (2019): 287–312. http://dx.doi.org/10.1007/s00181-019-01720-6.

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24

Gefang, Deborah, Gary Koop, and Aubrey Poon. "Computationally efficient inference in large Bayesian mixed frequency VARs." Economics Letters 191 (June 2020): 109120. http://dx.doi.org/10.1016/j.econlet.2020.109120.

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25

Carriero, A., G. Kapetanios, and M. Marcellino. "Forecasting exchange rates with a large Bayesian VAR." International Journal of Forecasting 25, no. 2 (2009): 400–417. http://dx.doi.org/10.1016/j.ijforecast.2009.01.007.

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26

Fadlilah, Annastia Abqiatul, and Mahrus Lutfi Adi Kurniawan. "Analisis Struktural Perdagangan di Indonesia: Pendekatan Bayesian VAR." Cendekia Niaga 8, no. 1 (2024): 1–12. https://doi.org/10.52391/jcn.v8i1.875.

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This research aims to determine the response of central government expenditure, government debt, government capital expenditure, population growth to international trade. The dependent variable in this research is the international trade variable and the independent variables include government debt, central government spending, government capital spending and population growth. The data in this research includes time series data in the form of percentages from 1971-2020 which were analyzed using the VAR (Vector Autoregressive) method. The results of this research are that all variables have a

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27

Yun, Seong-Jun, and Hee-Chan Lee. "Estimating the influence of COVID-19 on domestic tourism demand in Korea using the Bayesian VAR model: Difference in influence of indoor/outdoor, man-made/natural, large/small tourist attractions." Journal of Tourism Sciences 46, no. 1 (2022): 83–104. http://dx.doi.org/10.17086/jts.2022.46.1.83.104.

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28

Österholm, Pär, and Helge Berger. "Does Money Matter for U.S. Inflation? Evidence from Bayesian VARs." IMF Working Papers 08, no. 76 (2008): 1. http://dx.doi.org/10.5089/9781451869385.001.

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29

Berger, H., and P. Osterholm. "Does Money matter for U.S. Inflation? Evidence from Bayesian VARs." CESifo Economic Studies 57, no. 3 (2011): 531–50. http://dx.doi.org/10.1093/cesifo/ifr001.

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30

Seong, Byunghee. "Forecasting with the Optimal Choice of Hyperparameters for Bayesian VARs." Journal of Money & Finance 38, no. 3 (2024): 71–99. http://dx.doi.org/10.21023/jmf.38.3.3.

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31

Gupta, Rangan, and Xiaojin Sun. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs." Economics Letters 186 (January 2020): 108677. http://dx.doi.org/10.1016/j.econlet.2019.108677.

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32

Chan, Joshua C. C., Eric Eisenstat, Chenghan Hou, and Gary Koop. "Composite likelihood methods for large Bayesian VARs with stochastic volatility." Journal of Applied Econometrics 35, no. 6 (2020): 692–711. http://dx.doi.org/10.1002/jae.2793.

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33

Kung, Syang Ke, and Chi Hsiu Wang. "Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models." Applied Mechanics and Materials 529 (June 2014): 621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.529.621.

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This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive

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34

Siklar, Ilyas. "Bayesian VAR Estimates for the Fiscal Multipliers in Turkiye." International Journal of Economics, Business and Management Research 08, no. 12 (2024): 43–59. https://doi.org/10.51505/ijebmr.2024.81204.

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Studying the economic growth effects of each type of tax and expenditure through fiscal multiplier analysis is crucial for responding to the economic cycle while maintaining fiscal soundness. This approach may help maximize the effectiveness of fiscal policy. This study estimates a structural VAR model to measure the impact of expansionary fiscal policy shocks— specifically, tax cuts and public spending increases—on GDP. It employs a Bayesian method that imposes range constraints on the parameters. While the initial impact of tax cuts and public spending increases is limited, their influence o

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35

Österholm, Pär. "A structural Bayesian VAR for model-based fan charts." Applied Economics 40, no. 12 (2008): 1557–69. http://dx.doi.org/10.1080/00036840600843947.

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36

Gefang, Deborah. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage." International Journal of Forecasting 30, no. 1 (2014): 1–11. http://dx.doi.org/10.1016/j.ijforecast.2013.04.004.

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37

Thioune, Thierno. "Écart de production dans la zone UEMOA : analyse comparative d'une estimation par la fonction de production, le filtre de Kalman et le var structurel bayésien." Revue Internationale des Économistes de Langue Française 6, no. 2 (2021): 77–105. http://dx.doi.org/10.18559/rielf.2021.2.4.

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The potential output and output gap concepts are important tools for central banks, and in particular the Central Bank of West African States (BCEAO), to forecast inflation in pursuit of their priority objective of inflation control. The choice of a method for estimating inflation is a delicate one. This paper proposes an estimation of potential output by the unobservable component methods, Watson's (1986) and Kuttner's (1994) approach, and by an economic modelling method, namely the Bayesian structural VAR. It also proposes a comparison of these different methods with the production function,

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38

Ahelegbey, Daniel Felix. "Inference of Impulse Responses via Bayesian Graphical Structural VAR Models." Econometrics 13, no. 2 (2025): 15. https://doi.org/10.3390/econometrics13020015.

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Impulse response functions (IRFs) are crucial for analyzing the dynamic interactions of macroeconomic variables in vector autoregressive (VAR) models. However, traditional IRF estimation methods often have limitations with assumptions on variable ordering and restrictive identification constraints. This paper applies the Bayesian graphical structural vector autoregressive (BGSVAR) model, which integrates structural learning to capture both temporal and contemporaneous dependencies for more accurate impulse response estimation. The BGSVAR framework provides a more efficient and interpretable me

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39

Louzis, Dimitrios P. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs." Empirical Economics 53, no. 2 (2016): 569–98. http://dx.doi.org/10.1007/s00181-016-1128-y.

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40

Berg, Tim O., and Steffen R. Henzel. "Point and density forecasts for the euro area using Bayesian VARs." International Journal of Forecasting 31, no. 4 (2015): 1067–95. http://dx.doi.org/10.1016/j.ijforecast.2015.03.006.

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41

Chan, Joshua C. C., Liana Jacobi, and Dan Zhu. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation." Journal of Forecasting 39, no. 6 (2020): 934–43. http://dx.doi.org/10.1002/for.2660.

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42

Chan, Joshua C. C., and Eric Eisenstat. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility." Journal of Applied Econometrics 33, no. 4 (2018): 509–32. http://dx.doi.org/10.1002/jae.2617.

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43

Djurovic, Gordana, Vasilije Djurovic, and Martin M. Bojaj. "The macroeconomic effects of COVID-19 in Montenegro: a Bayesian VARX approach." Financial Innovation 6, no. 1 (2020). http://dx.doi.org/10.1186/s40854-020-00207-z.

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Abstract This study examines, diagnoses, and assesses appropriate macroeconomic policy responses of the Montenegrin Government to the outbreak of COVID-19. The model econometrically measures the macroeconomic costs using a Bayesian VARX Litterman/Minessota prior to the pandemic disease in terms of demand and supply loss due to illness and closed activities and their effects on GDP growth in various pandemic scenarios. We explore five economic scenarios—shocks—using the available data from January 2006 to December 2019, following real out-of-sample forecasts generated from January 2020 to Decem

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44

Banbura, Marta, Domenico Giannone, and Lucrezia Reichlin. "Large Bayesian VARs." SSRN Electronic Journal, 2008. http://dx.doi.org/10.2139/ssrn.1292332.

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45

Vieira, Jordy Oliveira, and Hitalo Joseferson Batista Nascimento. "Modelagem Bayesiana aplicada ao desenvolvimento de jogos de computadores: Um estudo de caso em MMORPGS." June 1, 2021. https://doi.org/10.5281/zenodo.13310807.

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O Universo dos jogos elétricos est'a em ascêns ̃ao no mundo todo, somente no Brasil entre 2012 e 2016 o crescimento m'édio anual do setor de games foi de 28,7% e a estimativa 'e que continue crescendo 16,5% ao ano at'e o ano de 2021. Um elemento central nos jogos atuais e que t&circ;em sido objeto de muitas pesquisas, consiste na modelagem de personagens inteligentes, atrav'es de estruturas como as redes Bayesianas, que consistem em modelos gr'aficos probabil'ısticos para modelagem de incertezas e s ̃ao representadas como grafos ac'ıclicos direcionados (DAGs), em que os n'o

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46

Ferreira, Leonardo N., Silvia Miranda-Agrippino, and Giovanni Ricco. "Bayesian Local Projections." Review of Economics and Statistics, May 29, 2023, 1–45. http://dx.doi.org/10.1162/rest_a_01334.

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Abstract We propose a Bayesian approach to Local Projections that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLP) regularise LP regressions via informative priors, and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty comparable to Bayesian VARs with standard macroeconomic priors. As regularised direct forecasts, BLPs are also a valuable alte

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Chan, Joshua CC. "Asymmetric Conjugate Priors for Large Bayesian VARs." SSRN Electronic Journal, 2019. http://dx.doi.org/10.2139/ssrn.3424437.

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48

Carriero, Andrea, Todd E. Clark, and Massimiliano Giuseppe Marcellino. "Bayesian VARs: Specification Choices and Forecast Accuracy." SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.1830163.

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49

Domit, Silvia, Francesca Monti, and Andrej Sokol. "A Bayesian VAR Benchmark for COMPASS." SSRN Electronic Journal, 2016. http://dx.doi.org/10.2139/ssrn.2721620.

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Quilis, Enrique M. "BayVAR_R: Bayesian VAR Modeling in R." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4000589.

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