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Journal articles on the topic 'Bayesian VAR'

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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 traditional unrestricted VAR model.

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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 a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

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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 averaging. The discussion is kept at a relatively non-technical level, providing the main ideas underlying and motivation for the models and methods used. It begins with single-equation models (such as regression) with many explanatory variables, then moves on to multiple equation models (such as Vector Autoregressive, VAR, models) before tacking the challenge caused by parameter change (e.g. changes in VAR coefficients or volatility). It concludes with an example of how the Bayesian can address all these challenges in a large multi-country VAR involving 133 variables: 7 variables for each of 19 countries.

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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 freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.

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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 model and is recommendable to forecasting practice.

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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 is nonnormal, therefore taking bivariate distribution of return and foreign exchange rate. VaR is calculated using the extreme value theory method and Bayesian method. The results suggest that Bayesian method provides the best VaR estimates

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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 results using a new measure of forecasting accuracy, the mean arctangent absolute percentage error (MAAPE). The results generated from the forecasts confirm the significant improvement in forecasting performance that can be obtained using the Bayesian model. It is noteworthy that the VAR performs the best for the lower horizons. The results also suggest that MAAPE outperforms other existing accuracy measures, in terms of error rates.

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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 the best prediction performance in forecasting and bootstrapping VaR and ES. Extension of this non-stationary distribution in literature is quite complicated since it requires specifications not only on how the usual Bayesian parameters change over time but also those with bulk distribution components. This implies that the combination of a stochastic econometric model with extreme value theory (EVT) procedures provides a robust basis necessary for the statistical backtesting and bootstrapping density predictions for VaR and ES.

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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 months, over a period that stretches from March 2005 to December 2018. We test null hypotheses of equal mean squared forecasting error between each candidate model and a simple benchmark. We find evidence that AR, ARMA, VAR, and Bayesian VAR models provide superior 1-month-ahead forecasts of the producer price index when compared to simple benchmarks, but find no evidence of superiority over simple benchmarks at longer horizons, or for any of our other variables.

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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 aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).

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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 distribution of Koop and Korobilis. The variables used in this study are real output (Yt), real effective exchange rate (Et), inflation rate (P t), repo rate (Rt), housing price index (Ht) and credit extended to private sector (Lt). The findings revealed that interest rate and credit channels remain important in the transmission mechanism to this day. Notably the exchange rate and asset price channels are also slowly gaining prominence in monetary policy transmission mechanism. Therefore, the study provides useful information to the monetary authorities regarding the process of transmission mechanisms. This is quite important especially that the Central Bank (Bank of Namibia) is very serious about financial stability within the financial system, given the fragility of the financial systems in the world due to financial crisis.

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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 qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation, and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks, and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.

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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) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.

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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 represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models.

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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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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 morphologically, and then we employed a part of the gene responsible for the synthesis of translation elongation factor 1 subunit alpha protein (tef1), ITS region, as well as β-tubulin partial sequences as potential genetic markers to inferphylogenetic relationships among different Phoma species..Finally, their ITS and tef1 sequences were sequenced and analysed by Bayesian approaches.According to phylogenetic trees inferred by Bayesian analysis of tef1, ITS and β-tubulin sequences, different Phoma species can be separated proving that these phylogenetic markers are well suited for phylogenetic studies of Phoma species. However, the phylogenetic tree does not support the traditional Phoma sections based on morphological characterization.Bayesian analyses of the three sequences confirmed that the Phyllosticta sojicola species is clustered with the Phoma exigua var. exigua group and the Phoma sojicola is grouped with Phoma pinodella group. The molecular data provide evidence for reclassification of formerly mentioned soybean pathogens.

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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 uncertainty on monetary policy effectiveness for a developing country, namely India. We applied a non-linear VAR, which allows us to examine the effect of monetary policy shocks during high and low uncertainty periods. The results exhibit that uncertainty influences the effectiveness of monetary policy shocks. We find weaker effects of the monetary policy shocks during high uncertainty regime relative to low uncertainty regime.

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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 content was 0.670. Among the eight provenances, Tuanjie Township, Xinyuan County, Xinjiang had the highest genetic diversity. The observed alleles, effective alleles, Shannon information index and Nei’s gene diversity index among provenances were higher than those within provenances. Based on Bayesian mathematical modeling and UPGMA cluster analysis, 86 P. armeniaca var. ansu accessions were divided into three subpopulations and four groups, which reflected individual differences in provenances. Subpopulations classified by Bayesian mathematical modeling and groups classified by UPGMA cluster analysis were significantly correlated with geographical provenance (Sig<0.01) and the provenances significantly impacted classification of groups. The provenances played an important role in classification of groups. The genetic distance between Tuanjie Township of Xinyuan County and Alemale Township of Xinyuan County was the smallest, while the genetic relationship between them was the closest and the degree of genetic differentiation was small.

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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‐form expression of the marginal likelihood. This new prior also leads to fast posterior simulation—for a BVAR with 100 variables and 4 lags, obtaining 10,000 posterior draws takes less than half a minute on a standard desktop. We demonstrate the usefulness of the new prior via a structural analysis using a 15‐variable VAR with sign restrictions to identify 5 structural shocks.

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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 a morphometric analysis. Parsimony and Bayesian analysis of the cpDNA data distinguished A. robbinsii var. minor from A. eucosmus and the majority of Fernald's milkvetch samples; both cpDNA and AFLP analysis were highly suggestive of gene flow between taxa and populations. Morphometric analysis indicates that Fernald's milkvetch is closer to A. eucosmus than to A. robbinsii var. minor in overall form and stipe length, while pubescence was not taxonomically informative. Based on these results, the recognition of Fernald's milkvetch is unwarranted; we recommend that the taxon be transferred to A. eucosmus.

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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 with supportive statistical values. This is the first report on the phylogenetic analysis using multiple genes of the rust, P. paederiae.

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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  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 -  that reduces the  policy analysis and forecasting   effectiveness. By using the multiple It\^o  integral, the multidimensional Ornstein - Uhlenbeck process can be written as a Vector Autoregressive with lag 1 (VAR(1)) that is the generalization of Vector Autoregressive process. The  limit of this approach is in fact it requires  the strong foundations of stochastic analysis, the Bayesian Statistics, and time series analysis.

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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-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models.

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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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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, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy.

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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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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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