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Journal articles on the topic 'Bayesian VAR'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Poghosyan, Karen. "A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia." Journal of Central Banking Theory and Practice 5, no. 2 (May 1, 2016): 81–99. http://dx.doi.org/10.1515/jcbtp-2016-0012.

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Abstract We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than tra

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2

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

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3

Korobilis, Dimitris. "VAR FORECASTING USING BAYESIAN VARIABLE SELECTION." Journal of Applied Econometrics 28, no. 2 (October 26, 2011): 204–30. http://dx.doi.org/10.1002/jae.1271.

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4

Williams, John T. "Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis." Political Analysis 4 (1992): 97–125. http://dx.doi.org/10.1093/pan/4.1.97.

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The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide

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5

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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6

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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7

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

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8

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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9

Koop, Gary. "Bayesian Methods for Empirical Macroeconomics with Big Data." Review of Economic Analysis 9, no. 1 (April 9, 2017): 33–56. http://dx.doi.org/10.15353/rea.v9i1.1434.

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Bayesian econometric methods are increasingly popular in empirical macroeconomics. They have been particularly popular among macroeconomists working with Big Data (where the number of variables under study is large relative to the number of observations). This paper, which is based on a keynote address at the Rimini Centre for Economic Analysis' 2016 Money-Macro-Finance Workshop, explains why this is so. It discusses the problems that arise with conventional econometric methods and how Bayesian methods can successfully overcome them either through use of prior shrinkage or through model averag

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Ward, Eric J., Kristin Marshall, and Mark D. Scheuerell. "Regularizing priors for Bayesian VAR applications to large ecological datasets." PeerJ 10 (November 8, 2022): e14332. http://dx.doi.org/10.7717/peerj.14332.

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Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freed

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11

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

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12

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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13

Sinha, Pankaj, and Shalini Agnihotri. "Bayesian and EVT Value-At-Risk Estimates of India's Non-Financial Firms." Journal of International Business and Economy 19, no. 1 (July 1, 2018): 50–75. http://dx.doi.org/10.51240/jibe.2018.1.3.

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The Companies Act 2013 has made it mandatory for firm’s Board of Directors Report to include a statement indicating elements of risk faced by companies. In the IMF report of March 2015, it is mentioned that India’s non-financial company’s external commercial borrowings rose by 107% between March 2010 to March 2014. The stress test based on exchange rate and profits demonstrated continuing high vulnerabilities of the firms. Looking at both the important factors, the current study estimates the Value-at-Risk (VaR) of 106 non-financial Indian firms. It is well a documented fact that return series

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14

Ampountolas, Apostolos. "Forecasting hotel demand uncertainty using time series Bayesian VAR models." Tourism Economics 25, no. 5 (October 4, 2018): 734–56. http://dx.doi.org/10.1177/1354816618801741.

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Demand uncertainty is a fundamental characteristic of the hospitality industry. Hotel room inventory is fixed, and devising an accurate daily demand measurement is a key operational challenge. In practice, it is difficult to predict the industry stability and capture demand uncertainty, so the industry relies on demand estimates. This process of estimation affects revenue maximization, as it is sensitive to incremental costs. In this article, we implemented vector autoregressive (VAR) models and compared them to the Bayesian VAR to examine the accuracy of predicting demand. We evaluated the re

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15

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 (September 30, 2020): 85–93. http://dx.doi.org/10.21739/ibr.2020.09.24.3.85.

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16

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

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17

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

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18

Makatjane, Katleho, and Tshepiso Tsoku. "Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods." International Journal of Financial Studies 10, no. 1 (January 27, 2022): 10. http://dx.doi.org/10.3390/ijfs10010010.

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This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a continuously ranked probability score, are developed. Developed backtesting procedures revealed that an estimated Seasonal autoregressive integrated moving average-generalized autoregressive score-generalized extreme value distribution (SARIMA–GAS–GEVD) with a skewed student-t distribution had

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19

Heaton, Chris, Natalia Ponomareva, and Qin Zhang. "Forecasting models for the Chinese macroeconomy: the simpler the better?" Empirical Economics 58, no. 1 (November 7, 2019): 139–67. http://dx.doi.org/10.1007/s00181-019-01788-0.

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Abstract We consider the problem of macroeconomic forecasting for China. Our objective is to determine whether well-established forecasting models that are commonly used to compute forecasts for Western macroeconomies are also useful for China. Our study includes 19 different forecasting models, ranging from simple approaches such as the naive forecast to more sophisticated techniques such as ARMA, Bayesian VAR, and factor models. We use these models to forecast two different measures of price inflation and two different measures of real activity, with forecast horizons ranging from 1 to 12 mo

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20

Miftahurrohmah, Brina, Catur Wulandari, and Yogantara Setya Dharmawan. "Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk." Journal of Information Systems Engineering and Business Intelligence 7, no. 1 (April 27, 2021): 11. http://dx.doi.org/10.20473/jisebi.7.1.11-21.

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Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.Objective: This study

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21

Kim, Sunghwan, and Kabsung KIM. "Developing Bayesian VAR Model to Predict Korean Housing Business Index." International Journal of IT-based Management for Smart Business 3, no. 1 (December 30, 2016): 37–46. http://dx.doi.org/10.21742/ijitmsb.2016.3.06.

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22

Sheefeni, Johannes PS. "Monetary Policy Transmission Mechanism in Namibia: A Bayesian VAR Approach." Journal of Economics and Behavioral Studies 9, no. 5 (October 21, 2017): 169–84. http://dx.doi.org/10.22610/jebs.v9i5.1921.

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This study analyzed the interest rate channel, credit channel, exchange rate channel and asset price channel for monetary policy transmission mechanism in Namibia. The idea behind this study is to have a comprehensive study that covers a variety of channels for monetary policy transmission mechanism. The study utilized a Bayesian vector autoregression (BVAR) technique on quarterly time-series data covering the period 2000:Q1 to 2016:Q4. In particular, the validity of the data used is checked and verified by using two sets of prior distributions suggested by Sims and Zha as well as prior distri

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23

Kocięcki, Andrzej. "A Prior for Impulse Responses in Bayesian Structural VAR Models." Journal of Business & Economic Statistics 28, no. 1 (January 2010): 115–27. http://dx.doi.org/10.1198/jbes.2009.07278.

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24

Domit, Sílvia, Francesca Monti, and Andrej Sokol. "Forecasting the UK economy with a medium-scale Bayesian VAR." International Journal of Forecasting 35, no. 4 (October 2019): 1669–78. http://dx.doi.org/10.1016/j.ijforecast.2018.11.004.

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25

KADIYALA, K. RAO, and SUNE KARLSSON. "NUMERICAL METHODS FOR ESTIMATION AND INFERENCE IN BAYESIAN VAR-MODELS." Journal of Applied Econometrics 12, no. 2 (March 1997): 99–132. http://dx.doi.org/10.1002/(sici)1099-1255(199703)12:2<99::aid-jae429>3.0.co;2-a.

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26

Cuestas, Juan C. "The EU real exchange rates: A structural Bayesian VAR. A note." Revista de Economía y Estadística 56, no. 1 (December 1, 2018): 43–57. http://dx.doi.org/10.55444/2451.7321.2018.v56.n1.29387.

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In this paper we contribute to the long literature on the real exchange determination by estimating a Bayesian structural vector autoregressive model. We aim at identifying the effect on the EU-28 RER of shock originating in its main fundamental variables, namely, current account, government consumptions, investment and real income. We find in most of the shocks that the RER moves away for long periods, proving yet again, that the purchasing power parity condition is rarely fulfilled empirically.

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27

Lee, Young-Soo. "Monetary Policy and Housing Market: Bayesian VAR Analysis using Sign Restrictions." Korean Association for Housing Policy Studies 27, no. 1 (February 28, 2019): 113–36. http://dx.doi.org/10.24957/hsr.2019.27.1.113.

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28

Kwon, Yongjae, Hamparsum Bozdogan, and Halima Bensmail. "Performance of Model Selection Criteria in Bayesian Threshold VAR (TVAR) Models." Econometric Reviews 28, no. 1-3 (November 18, 2008): 83–101. http://dx.doi.org/10.1080/07474930802387894.

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29

Canova, Fabio, and Matteo Ciccarelli. "Forecasting and turning point predictions in a Bayesian panel VAR model." Journal of Econometrics 120, no. 2 (June 2004): 327–59. http://dx.doi.org/10.1016/s0304-4076(03)00216-1.

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30

Kling, Gerhard, Charles Harvey, and Mairi Maclean. "Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables." Organizational Research Methods 20, no. 4 (November 30, 2015): 770–99. http://dx.doi.org/10.1177/1094428115618760.

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Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker’s qualitative vector autoregression (QVAR) and Lunn, Osorio, and Whittaker’s multivariate probit model to develop a solution to these problems in the form of a qualitati

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31

Ibrahim, Ahmed, Rasha Kashef, Menglu Li, Esteban Valencia, and Eric Huang. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables." Journal of Risk and Financial Management 13, no. 9 (August 19, 2020): 189. http://dx.doi.org/10.3390/jrfm13090189.

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The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR)

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32

Jeřábek, Tomáš, and Radka Šperková. "A Predictive Likelihood Approach to Bayesian Averaging." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1269–76. http://dx.doi.org/10.11118/actaun201563041269.

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Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. In this paper we combine multivariate density forecasts of GDP growth, inflation and real interest rates from four various models, two type of Bayesian vector autoregression (BVAR) models, a New Keynesian dynamic stochastic general equilibrium (DSGE) model of small open economy and DSGE-VAR model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is rep

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33

Dajcman, Silvo. "Uncertainty and demand for business loans: A study of selected countries in the euro area." Panoeconomicus, no. 00 (2022): 13. http://dx.doi.org/10.2298/pan180725013d.

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This paper studies the effect of uncertainty shocks on the demand for business loans in individual euro area countries. The results of Bayesian vector autoregression (VAR) model impulse response functions show that in some countries the overall demand for business loans, and particularly the demand for business loans for fixed-investment financing, respond significantly negatively to the shock.

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34

Irinyi, László, György Kövics, and Erzsébet Sándor. "Phylogenetic studies of soybean pathogen Phoma species by Bayesian analysis." Acta Agraria Debreceniensis, no. 35 (October 20, 2009): 53–61. http://dx.doi.org/10.34101/actaagrar/35/2809.

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We carried out phylogenetic study analyzing sequences of genetic markers in the taxonomy of Phoma and Phoma-like fungi. Different species of Phoma and Phoma-like fungi occurring on soybean (Phoma pinodella, Phoma sojicola, Phyllosticta sojicola, Phoma exigua var. exigua) are difficult to identy because of their high morphological and symptomatic similarities.Twenty-two isolates of nine different Phoma species were obtained from reference culture collections. Seven of them were isolated from soybean, the others were collected from different hosts.The Phoma isolates were firstly characterised by

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35

Choe, Jong-Il, and Soon-Chan Park. "A Study on the ICT Industry Export Forecast Using Bayesian VAR Model." Korea International Trade Research Institute 12, no. 2 (April 25, 2016): 515–27. http://dx.doi.org/10.16980/jitc.12.2.201604.515.

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36

Nain, Zulquar, and Bandi Kamaiah. "Uncertainty and Effectiveness of Monetary Policy: A Bayesian Markov Switching-VAR Analysis." Journal of Central Banking Theory and Practice 9, s1 (July 1, 2020): 237–65. http://dx.doi.org/10.2478/jcbtp-2020-0030.

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AbstractThere is a growing body of literature examining the effectiveness of the monetary policy on the macroeconomy in different contexts for developed and developing countries. However, lately, especially after the GFC, the focus of research shifted to examine the role of uncertainty in economic activity and on the monetary policy effectiveness. Both theoretical and empirical studies suggest that uncertainty does influence monetary policy effectiveness. However, until now, empirical studies are restricted to only developed countries. To this end, the present study examines the influence of u

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37

Fang, Zheng, Yang Yang, Yanyan Xu, and Wei Li. "Boost Movie Ticket Sales by Location-Based Advertising: A Bayesian VAR Approach." Journal of Media Economics 29, no. 3 (July 2, 2016): 125–38. http://dx.doi.org/10.1080/08997764.2016.1206906.

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38

Šljivić, Nuša Mikuljan. "Cross-entropy method for estimation of posterior expectation in Bayesian VAR models." Communications in Statistics - Theory and Methods 46, no. 23 (August 29, 2017): 11933–47. http://dx.doi.org/10.1080/03610926.2017.1288252.

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39

Chow, Hwee Kwan, and Keen Meng Choy. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach." International Journal of Forecasting 22, no. 2 (April 2006): 301–15. http://dx.doi.org/10.1016/j.ijforecast.2005.07.002.

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40

Petrova, Katerina. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models." Journal of Econometrics 212, no. 1 (September 2019): 286–306. http://dx.doi.org/10.1016/j.jeconom.2019.04.031.

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41

Chen, Jianhua, Quangang Liu, Caiyun Lu, Qingbai Liu, Jingjing Pan, Jian Zhang, and Shengjun Dong. "Genetic diversity of Prunus armeniaca L. var. ansu Maxim. germplasm revealed by simple sequence repeat (SSR) markers." PLOS ONE 17, no. 6 (June 3, 2022): e0269424. http://dx.doi.org/10.1371/journal.pone.0269424.

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The genetic diversity and genetic structure of P. armeniaca var. ansu were analyzed based on SSR markers. The aim was to provide scientific basis for conservation, efficient utilization, molecular marker assisted breeding and improved variety selection of P. armeniaca var. ansu germplasm resources. The results showed that the level of genetic diversity within the population was high. Among the 30 SSR markers, the mean number of observed alleles was 11.433, the mean number of effective alleles was 4.433, the mean of Shannon information index was 1.670, and the mean of polymorphic information co

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42

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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43

Sokoloff, Paul C., and Lynn J. Gillespie. "Taxonomy of Astragalus robbinsii var. fernaldii (Fabaceae): molecular and morphological analyses support transfer to Astragalus eucosmus." Botany 90, no. 1 (January 2012): 11–26. http://dx.doi.org/10.1139/b11-077.

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Astragalus robbinsii var. fernaldii , Fernald’s milkvetch, is a taxon of conservation concern currently due for reassessment of its provincial and federal conservation status. Restricted to a narrow region spanning Newfoundland and Labrador and Quebec, its taxonomic position with respect to two congeners, Astragalus eucosmus and Astragalus robbinsii var. minor , is poorly understood. To clarify the taxonomy of Fernald’s milkvetch, we studied variation in the ycf6–trnC and trnC–rpoB chloroplast DNA (cpDNA) spacers, generated amplified fragment length polymorphism (AFLP) genotypes, and conducted

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44

JITJAK, Wuttiwat, and Niwat SANOAMUANG. "Phylogenetic Trees of Aecial-Stage Rust Fungus, Puccinia paederiae (Dietel) Gorlenko Causing Gall on Paederia linearis Hook f." Walailak Journal of Science and Technology (WJST) 15, no. 10 (November 17, 2017): 739–52. http://dx.doi.org/10.48048/wjst.2018.2460.

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A rust fungus, Puccinia paederiae (Dietel) Gorlenko causing galls on the stem of the skunk vine (Paederia linearis Hook. f. var. linealis and P. linealis var. palida (Craib) Puff) was collected for phylogenetic study as no molecular data was exclusively available for this fungus. Three regions of ribosomal DNA sequences, small subunit (SSU), large subunit (LSU) and internal transcribed spacer region 1 (ITS1) were employed. The results of maximum parsimony and Bayesian methods suggested that among the trees with these sequences, this fungus was nested in Pucciniaceae clades and Puccinia species

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45

Mambo, Lewis N. K. "From Multidimensional Ornstein - Uhlenbeck Process to Bayesian Vector Autoregressive Process." Journal of Mathematics Research 15, no. 1 (February 1, 2023): 32. http://dx.doi.org/10.5539/jmr.v15n1p32.

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The main purpose of&nbsp; this paper is to make the connexion between stochastic analysis, the Bayesian Statistics, and time series analysis for policy analysis. This approach solves the problem of mathematical modelling - the presence of uncertainties in the models and parameters -&nbsp; that reduces the&nbsp; policy analysis and forecasting&nbsp;&nbsp; effectiveness. By using the multiple It\^o&nbsp; integral, the multidimensional Ornstein - Uhlenbeck process can be written as a Vector Autoregressive with lag 1 (VAR(1)) that is the generalization of Vector Autoregress

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46

Bekiros, Stelios D., and Alessia Paccagnini. "MACROPRUDENTIAL POLICY AND FORECASTING USING HYBRID DSGE MODELS WITH FINANCIAL FRICTIONS AND STATE SPACE MARKOV-SWITCHING TVP-VARS." Macroeconomic Dynamics 19, no. 7 (June 17, 2014): 1565–92. http://dx.doi.org/10.1017/s1365100513000953.

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We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-va

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47

Cuestas, Juan Carlos. "House prices and capital inflows in Spain during the boom: Evidence from a cointegrated VAR and a structural Bayesian VAR." Journal of Housing Economics 37 (September 2017): 22–28. http://dx.doi.org/10.1016/j.jhe.2017.04.002.

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48

Pacifico, Antonio. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure." Econometrics 10, no. 3 (July 12, 2022): 28. http://dx.doi.org/10.3390/econometrics10030028.

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This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis,

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49

Chun, Haejung. "Effects of Macroeconomic Variables on Regional Housing Prices Using Bayesian Panel VAR Model." Journal of Humanities and Social sciences 21 10, no. 6 (December 31, 2019): 1349–62. http://dx.doi.org/10.22143/hss21.10.6.100.

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50

Bhuiyan, Rokon. "The Effects of Monetary Policy Shocks in Bangladesh: A Bayesian Structural VAR Approach." International Economic Journal 26, no. 2 (June 2012): 301–16. http://dx.doi.org/10.1080/10168737.2011.552514.

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