Relevant bibliographies by topics / Bayesian VARX

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian VARX'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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Journal articles on the topic "Bayesian VARX"

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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 of noise reduction and predictive accuracy enhancement. It is conclusively deduced that preprocessing through wavelet-based techniques markedly improves the reliability of VARX models, as it removes short-term noise without affecting the long-term trends in the economy. Application of Daubechies and Reverse Biorthogonal wavelets has emerged better in all size categories in reducing AIC and BIC values. Thus, the study lauds wavelet denoising techniques in associating industries with finance to provide sound arguments for policymakers and economists to analyze more accurately complex economic relations. This work advances development in econometric modeling, detailing the importance of wavelet transformations in improving economic forecasts. Future studies should explore combining machine learning techniques with wavelet-based VARX models for further improvements in forecasting capabilities.
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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 relationships showcasing its promise in dynamic urban transit settings. Moreover the Bayesian Fourier model excels across routes underscoring its ability to accommodate trends effectively. These results suggest that incorporating LSTM networks into on demand transportation systems can notably enhance arrival time forecasts ultimately benefiting passengers and operational efficiency. This study demonstrates the usefulness of machine learning methods in urban transport planning and management by demonstrating how LSTM networks can improve optimization, on demand transportation.
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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, securities and diplomatic relations.  When the trade surplus, bringing economic benefits by generating foreign currency. In inverse situation, for trade deficit causes potentially economic imbalances. We can explore in this thesis how Myanmar border trade between these three countries has shifted post-coup, assessing whether the changes lead to the trade surplus or deficit by investigating and analyzing export and imports volumes, particularly using a Bayesian framework.
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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 meio de um modelo de regressão Bayesiano que utiliza a variável independente para entender a percepção dos gestores sobre a influência dos seus stakeholders através de seus atributos. |Por fim, como variável moderadora a quantificação da cultura organizacional através de uma revisão sobre sua conceitualização e aplicação de um modelo de diagnóstico. O trabalho busca contribuir identificando a relevância dada por cooperativas aos seus stakeholders e apresentar um cenário amostral das cooperativas brasileiras e como essas se comportam em relação ao seu contexto.
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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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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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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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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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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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Dissertations / Theses on the topic "Bayesian VARX"

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Huber, Florian, Tamás Krisztin, and Philipp Piribauer. "Forecasting Global Equity Indices Using Large Bayesian VARs." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4318/1/wp184.pdf.

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This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-movements in the stock markets. The time-varying specification of the covariance structure moreover accounts for sudden shifts in the level of volatility. In an out-of-sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of root mean squared errors as well as Bayesian log predictive scores. The BVAR model without stochastic volatility, on the other hand, underperforms relative to the random walk. In a portfolio allocation exercise we moreover show that it is possible to use the forecasts obtained from our BVAR model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy-and-hold strategy. (authors' abstract)<br>Series: Department of Economics Working Paper Series
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Houghton, Adrian James. "Variational Bayesian inference for comparison Var(1) models." Thesis, University of Newcastle Upon Tyne, 2009. http://hdl.handle.net/10443/790.

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Suppose that we wish to determine which models in a candidate set are most likely to have given rise to a set of observed data. Then, it is well-established that, from a Bayesian viewpoint, evaluation of the marginal likelihood for each candidate is a crucial step to this end. For the purposes of model comparison, this will enable subsequent computation of both Bayes’ factors and posterior model probabilities. Given its evident significance in this area, it is thus regrettable that analytic calculation of the marginal likelihood is often not possible. To tackle this problem, one recent addition to the literature is the variational Bayesian approach. In this thesis, it is seen that variational Bayes provides efficient, accurate approximations to both the marginal likelihood and the parameter posterior distribution, conditioned on each model. In particular, the theory is applied to ranking sparse, vector autoregressive graphical models of order 1 in both the zero and non-zero mean case. That is, our primary aim is to estimate the unknown sparsity structure of the autoregressive matrix in the process. Moreover, approximate, marginal posterior information about the coefficients of this matrix is also of interest. To enable rapid exploration of higher-dimensional graphical spaces, a Metropolis-Hastings algorithm is presented so that a random walk can be made between neighbouring graphs. The scheme is then tested on both simulated and real datasets of varying dimension.
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Siu, Wai-shing. "On a subjective modelling of VaR fa Bayesian approach /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22823785.

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Lanteri, Luis. "Modelos de VAR alternativos para pronósticos (VAR bayesianos y FAVAR): el caso de las exportaciones argentinas." Economía, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/117477.

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Exports are one of the key aggregates in the Argentina’s economy, both because to its links with thedomestic demand and by its influence on the behaviour of the trade balance and current account.Have adequate forecasts for this variable is useful to design policies to keep surpluses in the externalsector and prevent recurring crises seen in the past. In this work, we considered some modelsfor forecasting the performance of this aggregate, which could be an alternative to the estimationof structural econometric models. For this purpose, we used two approaches: the first is based instandard and Bayesian VARs (Minnesota prior, Gibbs sampler, partial BVAR and BVAR-Kalman). Thelatter combines the evidence in the data with any prior information that may also be available. Thesecond approach considers the FAVAR (Factor-augmented VAR) models, which combines the standardVAR with factor analysis. Finally, we evaluated the forecasting ability of different models.<br>Las exportaciones representan uno de los agregados más importantes de la economía argentina,tanto por su vinculación con la demanda doméstica como por su influencia en el comportamientode la balanza comercial y de la cuenta corriente. Disponer de adecuados pronósticos deesta variable resulta útil a fin de diseñar políticas que permitan mantener superávit en el sectorexterno y evitar las recurrentes crisis observadas en el pasado. En este trabajo, se consideran algunosmodelos destinados a la realización de pronósticos de dicho agregado, los cuales podrían seruna alternativa a la estimación de sistemas econométricos estructurales. A tal efecto, se utilizandos propuestas: la primera se basa en modelos de VAR sin restricciones y Bayesianos (‘Minnesota’prior, ‘Gibbs sampler’, parcial BVAR y BVAR-Kalman). Estos últimos consideran supuestos a priori(‘prior’) e información histórica de las series de tiempo empleadas. La segunda propuesta descansaen modelos FAVAR (Factor-aumentado VAR), que combinan los VAR con el análisis de factores.Finalmente, se evalúa la capacidad de pronóstico de los distintos modelos.
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Kim, Jae-yoon. "Essays on DSGE Models and Bayesian Estimation." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83515.

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This thesis explores the theory and practice of sovereignty. I begin with a conceptual analysis of sovereignty, examining its theological roots in contrast with its later influence in contestations over political authority. Theological debates surrounding God’s sovereignty dealt not with the question of legitimacy, which would become important for political sovereignty, but instead with the limits of his ability. Read as an ontological capacity, sovereignty is coterminous with an existent’s activity in the world. As lived, this capacity is regularly limited by the ways in which space is produced via its representations, its symbols, and its practices. All collective appropriations of space have a nomos that characterizes their practice. Foucault’s account of “biopolitics” provides an account of how contemporary materiality is distributed, an account that can be supplemented by sociological typologies of how city space is typically produced. The collective biopolitical distribution of space expands the range of practices that representationally legibilize activity in the world, thereby expanding the conceptual limits of existents and what it means for them to act up to the borders of their capacity, i.e., to practice sovereignty. The desire for total authorial capacity expresses itself in relations of domination and subordination that never erase the fundamental precarity of subjects, even as these expressions seek to disguise it. I conclude with a close reading of narratives recounting the lives of residents in Chicago’s Englewood, reading their activity as practices of sovereignty which manifest variously as they master and produce space.<br>Ph. D.
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Hauer, Mariana. "Os modelos VAR e VEC espaciais : uma abordagem bayesiana." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/12585.

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O objetivo deste trabalho é apresentar o Modelo Vetorial Autorregressivo (VAR) e uma das suas variações, o Modelo Vetorial de Correções de Erros (VEC), segundo uma abordagem Bayesiana, considerando componentes regionais, que serão inseridos nos modelos apresentados através de informações a priori que levam em consideração a localização dos dados. Para formar tais informações a priori são utilizados conceitos referentes à econometria espacial, como por exemplo, as relações de contigüidade e as implicações que estas trazem. Como exemplo ilustrativo, o modelo em questão será aplicado a um conjunto de dados regionais, coletados por estados brasileiros. Este conjunto de dados consiste em observações da variável produção industrial para oito estados, no período de janeiro de 1991 a setembro de 2006. Em função da escolha do modelo adequado, a questão central foi descobrir em que medida a incorporação destas informações a priori no modelo VEC Bayesiano é coerente quando estimamos modelos que consideram informações localizacionais.<br>The main goal of this work is to present the Vector Autoregressive Model (VAR) and one of its variations, the Vector Error Correction Model (VEC), according to a Bayesian variant, considering regional components that will be inserted in the models presented through prior information, which takes in consideration the data localization. To form such prior information, spatial econometrics is used, as for example the contiguity relations and the implications that these bring to the modeling. As illustrative example, the model in question will be applied to a regional data set, collected for Brazilian states. This data set consists of industrial production for eight states, in the period between January 1991 and September 2006. The central question is to uncover whether the incorporation of these prior informations in the Bayesian VEC Model is coherent when we use models that consider contiguity information.
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Contino, Christian. "A Bayesian Approach to Risk Management in a World of High-Frequency Data." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14728.

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A Realised Volatility GARCH model using high-frequency data is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. A Skewed Student-t return distribution is combined with a Student-t distribution in the measurement equation in a GARCH framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH. A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio tail risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using traditional GARCH-copula models, and GARCH on an aggregated portfolio. Copula models implementing a Realised Volatility GARCH framework show an improvement over traditional GARCH models. A Bayesian detection of regime changes utilizing high-frequency data is developed, once again for the purpose of forecasting portfolio tail risk. The use of high-frequency data improves the accuracy of regime change detection compared to daily data. Monte Carlo sampling schemes are employed for the estimation of these models.
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蕭偉成 and Wai-shing Siu. "On a subjective modelling of VaR: fa Bayesianapproach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225159.

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Samuel, Marco Antonio Castelo Branco. "Mudanças de Estado e Multiplicadores Fiscais no Brasil entre 1999-2012." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=9006.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Este trabalho avalia o comportamento dos multiplicadores fiscais no Brasil entre 1999-2012. Para tanto, utiliza a metodologia desenvolvida por Sims, Waggoner e Zha (2008), que é um procedimento Bayesiano de estimação no qual os parâmetros do modelo mudam com alterações no estado da economia e os estados (regimes) seguem um processo de mudança de regime markoviano. Ou seja, foi estimado um modelo VAR Estrutural Bayesiano com mudança de regimes Markoviana (Markov Switching Structural Bayesian Vector Autoregression - MS-SBVAR). A base de dados é composta pelo consumo da administração pública, pela formação bruta de capital fixo da administração pública, pela carga tributária líquida e pelo Produto Interno Bruto (PIB), das três esferas do governo (federal, estadual, incluindo o Distrito Federal, e municipal). O software MATLAB/Dynare foi utilizado na estimação dos modelos e os resultados sugerem a ocorrência de 2 ou 3 regimes nos dois modelos que melhor se ajustaram aos dados. Os multiplicadores estimados apresentaram os sinais esperados e os diferentes tipos de multiplicadores fiscais calculados apresentaram valores maiores para a resposta do PIB a choques na formação bruta de capital fixo da administração pública que são eficazes, uma vez que possuem valores maiores do que um e impacto de longo prazo no PIB - quando comparado aos choques no consumo da administração pública, que possuem pouca persistência e são ineficazes (menores do que um), além de uma resposta negativa e persistente do PIB a choques na carga tributária líquida. Os resultados obtidos não indicam, ainda, multiplicadores fiscais maiores em regimes com maior variância nos resíduos do modelo.<br>This dissertation evaluates the behavior of fiscal multipliers in Brazil from 1999 to 2012. It uses a methodology developed by Sims, Waggoner e Zha (2008), which is a Bayesian estimation procedure that allows for state (regime) dependent endogenous change in models parameters and the states follow a markovian process of regime change. It estimates a Structural Bayesian VAR model with Markov Switching regimes (MS-SBVAR). The database comprises the consumption of public administration, the fixed capital gross formation of the public administration, the net tax burden and the Gross Domestic Product (GDP) of the three levels of government (federal, state, including the Federal District, and municipalities). The software MATLAB / Dynare was used to estimate the model and the results suggest the occurrence of 2 or 3 regimes in the two best data fitting models. The different estimated multipliers show the correct signs and, as expected, they are higher for exogenous shocks to public administrations fixed capital gross formation which are effective, since they have values higher than one and long-term impact on GDP - when compared with exogenous shocks to public administrations consumption, which have a small persistence and are ineffective (less than one), and a negative and persistence response of GDP to shocks in net tax burden. The results do not also show a higher fiscal multiplier in regimes with higher models residuals variance.
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Unosson, Måns. "A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297406.

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This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.
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Books on the topic "Bayesian VARX"

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Kenny, Geoff. Bayesian VAR models for forecasting Irish inflation. Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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Crone, Theodore M. A Bayesian VAR forecasting model for the Philadelphia Metropolitan Area. Federal Reserve Bank of Philadelphia, Economic Research Division, 1999.

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Wong, Jason. Forecasting inflation and real GDP: Bayesian VAR models of the New Zealand economy. Reserve Bank of New Zealand, 1993.

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Ciccarelli, Matteo. Bayesian VARs: A survey of the recent literature with an application to the European monetary system. International Monetary Fund, Research Department, 2003.

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sterholm, Pr, and Helge Berger. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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Berger, Helge, and Pär Österholm. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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sterholm, Pr, and Helge Berger. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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Abrego, Lisandro, and Pär Österholm. External Linkages and Economic Growth in Colombia: Insights from a Bayesian VAR Model. International Monetary Fund, 2008.

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sterholm, Pr, and Lisandro Abrego. External Linkages and Economic Growth in Colombia: Insights from a Bayesian Var Model. International Monetary Fund, 2008.

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sterholm, Pr, and Lisandro Abrego. External Linkages and Economic Growth in Colombia: Insights from a Bayesian Var Model. International Monetary Fund, 2008.

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Book chapters on the topic "Bayesian VARX"

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Polasek, Wolfgang, and Hideo Kozumi. "The VAR-VARCH model: A Bayesian approach." In Modelling and Prediction Honoring Seymour Geisser. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_26.

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Bijak, Jakub. "Bayesian VAR Modelling ‘from General to Specific’." In Forecasting International Migration in Europe: A Bayesian View. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8897-0_6.

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Ebrahimijam, Saeed, Cahit Adaoglu, and Korhan K. Gokmenoglu. "Inter-Market Sentiment Analysis Using Markov Switching Bayesian VAR Analysis." In Regulation of Finance and Accounting. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99873-8_6.

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Mokrzycka, Justyna. "VaR and ES Calculation with a Bayesian Dynamic tCopula-GARCH Model." In Advances in Cross-Section Data Methods in Applied Economic Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38253-7_46.

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Safi, Samir K., Olajide Idris Sanusi, and Afreen Arif. "Comparing MIDAS and Bayesian VAR Models for GDP Forecasting: Insights from Simulation and Empirical Studies." In Studies in Big Data. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71213-5_63.

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Kato, Hisakazu. "Low Fertility and Female Labor Supply in Japan—Time Series Analysis Using Bayesian VAR Approach." In Macro-econometric Analysis on Determinants of Fertility Behavior. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3927-2_1.

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Mitra, Rajarshi, and Maria Evgenievna Guseva. "Does Population Ageing Reduce FDI Inflows in OECD Countries? Evidence from Bayesian Panel VAR Estimates." In Advances in Innovation, Trade and Business. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60354-0_6.

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Diep, Nguyen Thi Ngoc, Tran Quang Canh, and Nguyen Ngọc Thach. "Market Share Forecast of Vietnam and of the World’s Leading Textile and Garment Exporters by VAR Bayesian Model." In Optimal Transport Statistics for Economics and Related Topics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35763-3_30.

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Srichaikul, Wilawan, and Woraphon Yamaka. "Interdependence of Macroeconomic Factors and Economic Growth in OECD Countries: Evidence Based on a Bayesian Panel VAR Model." In Credible Asset Allocation, Optimal Transport Methods, and Related Topics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_23.

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"Bayesian VARs." In Methods for Applied Macroeconomic Research. Princeton University Press, 2011. http://dx.doi.org/10.2307/j.ctvcm4hrv.13.

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Conference papers on the topic "Bayesian VARX"

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Kumar, Kundan, Aditya Akilesh Mantha, and Gelli Ravikumar. "Bayesian Optimization for Deep Reinforcement Learning for Robust Volt-Var Control." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688889.

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Wang, Hui, Sreelakshmi Sreeharan, and Homero Casteneda. "Indirect Inspection-based Bayesian Machine Learning Model for Probabilistic Coating Defect Severity Interpretation." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00400.

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Abstract Corrosion pit nucleation and propagation in underground pipelines vary by geographic position along the pipeline right of way (RoW). Effective pipeline integrity management requires both the modeling of corrosion initiation/propagation and the integration of field-acquired data. To mitigate external corrosion, onshore pipelines use barrier coatings and either sacrificial or impressed current cathodic protection (CP) systems. Pipeline integrity is monitored through direct and/or indirect assessments. In this work, we focused on leveraging indirect methods, such as close interval potential surveys (CIPS) and direct current voltage gradient (DCVG). CIPS evaluates CP effectiveness by identifying deficiencies in CP-equipped structures via pipe to soil potential and detecting potential 'hot spots' in those with insufficient CP. Meanwhile, the DCVG method identifies coating flaws by measuring voltage gradients using reference electrodes placed in the soil. But the reliability of defect detection is a practical concern. To enhance corrosion assessment reliability, we propose a Bayesian framework combining indirect inspection data, corrosion science, and artificial intelligence. Using Stochastic Variational Inference, the framework predicts corrosion severity and rates. Validation against inline inspection data demonstrates its accuracy and confidence in identifying corrosion-prone areas, thus improving pipeline integrity management and reducing failure risks.
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Chin, Kuo-Hsuan, and Xue Li. "BAYESIAN FORECAST COMBINATION IN VAR-DSGE MODELS." In 32nd International Academic Conference, Geneva. International Institute of Social and Economic Sciences, 2017. http://dx.doi.org/10.20472/iac.2017.032.008.

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Liao, Ruofan, Petchaluck Boonyakunakorn, and Songsak Sriboonchiita. "VaR of SSE returns Based on Bayesian Markov-Switching GARCH Approach." In the 2nd International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3358528.3358545.

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Rojniruttikul, Nuttawut, and Adirek Vajrapatkul. "ICT and Thai Economic Growth Nexus in the Bayesian VAR Model." In IECC 2021: 2021 3rd International Electronics Communication Conference. ACM, 2021. http://dx.doi.org/10.1145/3475971.3475978.

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Rojniruttikul, Nuttawut, and Adirek Vajrapatkul. "The Projection of Thai Manufacturing Export in the Bayesian VAR Model." In IECC 2022: 2022 4th International Electronics Communication Conference. ACM, 2022. http://dx.doi.org/10.1145/3560089.3560106.

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Feng, Wei, Qiaofeng Li, and Qiuhai Lu. "A Hierarchical Bayesian Method for Time Domain Structure Damage Detection." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97026.

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Abstract A time domain structural damage detection method based on hierarchical Bayesian framework is proposed. Due to local stiffness reductions, the responses of damaged structures vary from those in undamaged status under the same external excitation. In this paper, the responses of damaged structures are assumed as the result of a summation of known external forces and unknown virtual forces exerted on corresponding undamaged structures. The damages can thus be detected, located, and quantified by the identification of associated virtual forces. A hierarchical Bayesian formulation considering all undetermined damage-related variables is adopted for the identification of virtual forces. The reasonable values of the variables and their uncertainties are depicted by their posterior distributions, sampled by Markov chain Monte Carlo method. Compared with traditional Bayesian formulations, manual choice of prior parameters is avoided and less prior information is required. The proposed virtual force indicator provides a more intuitive perspective for damage detection tasks and is potentially more operable in engineering practice. These advantages are illustrated by simulation of a cantilever beam under various damage conditions.
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ANTON, George. "THE IMPACT OF ECONOMIC UNCERTAINTY ON HOUSEHOLD CONSUMPTION CHOICES. EVIDENCE FROM EUROPE." In International Management Conference. Editura ASE, 2022. http://dx.doi.org/10.24818/imc/2021/03.18.

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This paper is evaluating the impact of uncertainty shocks that are affecting the household behavior in the European Union countries by employing a quantitative approach. By employing a Bayesian VAR model, this paper provides an answer on the importance of the uncertainty shocks on the household consumption choices by using impulse response functions and variance decompositions statistics. The relevance of the study is a major one as it quantifies the impact of the uncertainty pressure on choices consumers make during uncertain times such as the great recession or covid-19 health crisis. Given the current increased focus of the literature on behavioral economics and consumer welfare this paper will provide an answer on consumption by sector increase and decrease as a result of uncertainty shocks.
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Jiang, Zejun. "Assessing the Effect of Quantitative Easing on the US Economy from 2008 to 2015 by a Bayesian-VAR Model." In Proceedings of the 2nd International Symposium on Social Science and Management Innovation (SSMI 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/ssmi-19.2019.9.

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Sernaqué, Humberto, Moly Meca, Eduardo Zapata, et al. "Comparison of Arima and Holt-Winters forecasting models for time series of cereal production in Peru." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001007.

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Agricultural commodities present remarkable volatility in their production levels, which severely affects farmers. The variational dynamics in the prices of the inputs used and the constant variations in weather conditions have a significant influence on the cereal production chain in Peru; therefore, compared to the ARIMA model, the Additive Holt-Winters forecasting model presented a better fit according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), forecasting the production of Oryza sativa, Zea mays L. var. Indurata and Amaranthus caudatus; however, due to the high seasonality, volatility of production, and the greater amount of outliers due to production in certain periods and geographical areas, the Holt-Winters Multiplicative model predicted the national production of Zea mays L. ssp amiláceo and Chenopodium quinoa, in Peru in the period 2000-2021.
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Reports on the topic "Bayesian VARX"

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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Banco de España, 2022. http://dx.doi.org/10.53479/23552.

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We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads to mild improvements in the point forecasts of BVARs for some variables and horizons. However, the density forecast performance considerably deteriorates. Therefore, we recommend taking into account outliers only on pre-specified dates around the onset of the COVID-19 pandemic.
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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
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Kurozumi, Takushi, Ryohei Oishi, and Willem Van Zandweghe. Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach. Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202234.

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Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal likelihood-based model comparison selects a dual stickiness Phillips curve in which, each period, some prices remain unchanged, consistent with micro evidence. Moreover, sticky information is a more plausible source of inflation inertia in the Phillips curve than other sources proposed in previous studies. Sticky information, sticky prices, and unchanged prices in each period are all needed to better describe inflation dynamics.
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McCracken, Michael W., Michael T. Owyang, and Tatevik Sekhposyan. Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR. Federal Reserve Bank of St. Louis, 2015. http://dx.doi.org/10.20955/wp.2015.030.

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Rincón-Castro, Hernán, and Norberto Rodríguez-Niño. Nonlinear pass-through of exchange rate shocks on inflation : a bayesian smooth transition VAR approach. Banco de la República, 2016. http://dx.doi.org/10.32468/be.930.

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de Padua, David, Matteo Lanzafame, Irfan Qureshi, and Kiyoshi Taniguchi. Understanding the Drivers of Remittances to Pakistan. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps240348-2.

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This paper analyzes remittances to Pakistan and identifies the key macroeconomic variables influencing this important source of external financing. Remittances account for approximately 10% of gross domestic product in Pakistan and a better understanding of remittance drivers is needed to inform policies that can bolster their contribution to poverty reduction and other development priorities. To develop this understanding, the authors combined a database of bilateral remittances between Pakistan and its main remittance-sending countries with monthly macroeconomic data over 2003–2021, and applied a Bayesian vector autoregression model to assess the drivers. The macroeconomic variables identified included economic activity, inflation, equity markets, and interest rates— both in Pakistan and migrants’ host countries—and all play a significant role, although their contributions vary over time.
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Hajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202203r.

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The paper considers a New Keynesian framework in which agents form expectations based on a combination of autoregressive mis-specified forecasts and myopia. The proposed expectations formation process is shown to be consistent with all three empirical facts on consensus inflation forecasts. However, while mis-specified forecasts can be both sufficient and necessary to match all three facts, myopia alone is neither. The paper then derives the general equilibrium solution consistent with the proposed expectations formation process and estimates the model with likelihood-based Bayesian methods, yielding three novel results: (i) macroeconomic data strongly prefer a combination of autoregressive mis-specified forecasting rules - of the VAR(1) or AR(1) type - and myopia over other alternatives; (ii) no strong evidence is found in favor of VAR(1) forecasts over simple AR(1) rules; and (iii) frictions such as habit in consumption, which are typically necessary for models with full-information rational expectations, are significantly less important, because the proposed expectations generate substantial internal persistence and amplification to exogenous shocks. Simulated inflation expectations data from the estimated general equilibrium model reflect the three empirical facts on forecasting data.
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Rossi, José Luiz, and João Paulo Madureira Horta da Costa. Shock Dependent Exchange Rate Pass-Through - An Analysis for Latin American Countries. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005129.

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This paper investigates the exchange rate pass through considering the source of the shocks that hit the economy. With a Bayesian Global VAR model, the exchange rate pass-through is analyzed for 5 Latin American countries: Brazil, Chile, Colombia, Mexico and Peru. The model is estimated with Bayesian techniques and is identified by sign and zero restrictions. The BGVAR estimation enable us to allow spillover between countries mimicking the real conditions when the shocks hit the economies. Four domestic shocks for each Latin American countries are considered: an exchange rate shock, a risk premium shock, a monetary policy shock and a demand shock. The demand shock has the highest exchange rate pass-through for all the countries and the exchange rate shock has the lowest one. Additionally, two regional shocks are considered: a regional monetary policy shock, an event that all the region rises its interest rate and a regional risk premium shock, where the risk premium rises at the same time. For almost of the countries, the exchange rate pass-through coming from those regional shocks are lower than its domestic counterpart shock. Finally, we investigate two global shocks, an uncertainty shock and a global commodities/demand shock. The uncertainty shock decreases the economic activity and depreciates the exchange rate with a negative exchange rate pass-through in the middle term. The commodities/demand shock increases the economic activity and appreciates the exchange rate passthrough, having a negative or neutral exchange rate pass-through over the time.
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Granados, Camilo, and Daniel Parra-Amado. Output Gap Measurement after COVID for Colombia: Lessons from a Permanent-Transitory Approach. Banco de la República, 2025. https://doi.org/10.32468/be.1295.

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We estimate the output gap for the Colombian economy explicitly accounting for the COVID-19 period. Our estimates reveal a significant $20$\% decline in the output gap but with a faster recovery compared to previous crises. Our empirical strategy follows a two-stage Bayesian vector autoregressive (BSVAR) model where i) a scaling factor in the reduced form of VAR is used to model extreme data, such as those observed around the COVID-19 period, and ii) permanent and transitory shocks are structurally identified. As a result, we obtain that a single structural shock explains the potential GDP, while the remaining shocks within the model are transitory in nature and thus can be used to estimate the output gap. We elaborate on the relative strengths of our method for drawing policy lessons and show that the improved approximation accuracy of our method allows for inflation forecasting gains through the use of Phillips curves, as well as for rule-based policy diagnostics that align more closely with the observed behavior of the Central Bank.
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Angrist, Noam, and Rachael Meager. The role of implementation in generalisability: A synthesis of evidence on targeted educational instruction and a new randomised trial. Centre for Excellence and Development Impact and Learning (CEDIL), 2022. http://dx.doi.org/10.51744/cswp4.

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Targeted instruction is one of the most effective educational interventions in low- and middle-income countries, yet the reported impacts of this approach vary, from 0.07 to 0.78 standard deviations (SDs) across contexts. We study this variation and the contextual factors associated with it by combining an evidence aggregation covering 10 study arms with a new randomised trial. The results show that two factors explain most of the heterogeneity in reported effects: the degree of implementation (intention-to-treat or treatment-on-the-treated effects) and the instruction delivery model (teachers or volunteers). Accounting for these implementation factors enables substantial generalisation of effect sizes across contexts. We introduce a new Bayesian model which incorporates implementation information into the evidence aggregation process. The results show that targeted instruction can deliver 0.39 SD improvements in learning on average when taken up, and 0.80 SD gains when implemented with high fidelity, explaining the upper range of effects in the literature. Given the central role of implementation identified in our synthesis, we conduct a new randomised trial to increase programme fidelity in Botswana. The results show additional 0.22 SD gains relative to standard implementation, revealing concrete mechanisms to enhance implementation and achieve the largest frontier effects identified in the literature.
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