Relevant bibliographies by topics / Bayesian VAR

Academic literature on the topic 'Bayesian VAR'

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Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Bayesian VAR"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Bayesian VAR"

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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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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.
Ph. D.
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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.
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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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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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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Solcan, Mihaela. "Essays on Macroeconomic Price Adjustments." Thesis, Lyon 2, 2013. http://www.theses.fr/2013LYO22014.

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Au cours de la dernière décennie, les prix des logements ont augmenté de façon spectaculaire dans plusieurs pays à travers le monde. Par exemple, les prix des logements aux États-Unis, en Espagne et en Irlande ont été marqués par des cycles d'expansion et de récession les plus marquants de leur histoire. L'augmentation concomitante des prix des logements (et dans certains cas l’occurrence des épisodes d'expansion - récession) dans de nombreuses économies avancées soulève quelques questions importantes. Y a t-il eu une bulle immobilière dans les pays avancés? Quels sont les principaux déterminants de l'évolution des prix des logements dans ces pays? Est-ce que les marchés immobiliers des pays avancés sont-ils interdépendants? Le premier chapitre propose une modélisation structurelle des modèles VAR Bayésiens pour les États-Unis, la France, l'Espagne et la Grèce qui examinent les effets relatifs de l'évolution du secteur réel de production, du secteur financier et des flux internationaux de capitaux sur les marchés du logement. Un deuxième exercice tente d'identifier la présence de régimes de bulles immobilières à partir d’une modélisation Markovienne à deux états. Les principaux résultats liés au marche américain montrent que les entrées de capitaux étrangers, mesurées par le solde de la balance courante en pourcentage du PIB, comptent pour plus de 30 % de la variance des chocs qui frappent les prix des logements, tandis que les taux d’intérêt contribuent pour environ 38 %. En France, la politique monétaire a le plus grand pouvoir explicatif des évolutions du marché du logement, tandis qu'en Espagne et en Grèce, les taux hypothécaires variables et les investissements dans le logement exercent la plus grande influence sur le marché du logement. Tous les pays ont connu un régime de bulle immobilière sur la majeure partie des années 2000.Le deuxième chapitre utilise une approche de type Global VAR (ou GVAR) qui porte sur la modélisation des interdépendances internationales des prix immobiliers. Le modèle GVAR a été estimé empiriquement en utilisant des données trimestrielles de sept pays, pour la période 1987-2011. Les résultats montrent que les chocs des prix immobiliers originaires des États-Unis ont de fortes répercussions sur tous les pays, avec les plus fortes magnitudes observées pour l'Irlande. Ce résultat suggère que les marchés immobiliers pourraient être soumis à des effets de contagion du comportement des marchés financiers et que le secteur immobilier peut être analysé comme un actif spéculatif. Les liens entre les taux d'intérêt réels à long terme sont positifs et statistiquement significatifs dans tous les pays, même si ils ont un rôle limité sur l'évolution des prix immobiliers. Les chocs négatifs sur les prix immobiliers aux États-Unis ont des effets négatifs et statistiquement significatifs sur le PIB réel aux États-Unis, le Canada et l'Irlande.Le troisième chapitre est consacré au financement de la première guerre mondiale par les Etats-Unis et le rôle de la War Finance Corporation (WFC). Plus spécifiquement, on s’intéresse aux fluctuations des rendements des bons du Trésor américain émis entre novembre 1917 et décembre 1920. L’analyse économétrique est basée sur des techniques de séries temporelles Bayésiennes. Les principaux résultats montrent que les chocs positifs sur les achats de la WFC engendrent une réponse négative et statistiquement significative sur tous les types de rendements des bons de guerre. En outre, les achats de la WFC des bons Liberty et Victory, à l'exception du premier prêt des bons Liberty, ont eu un effet statistiquement significatif sur l'évolution des taux à court terme. Les achats de la WFC de la deuxième et de la quatrième émission des bons Liberty ont eu des effets significatifs et positifs sur les taux à court terme, ce qui suggère une déformation de la courbe des taux
During the last decade, housing prices have increased dramatically in several countries around the world. For instance, housing prices in the United States, Spain, and Ireland have been marked by one of the most striking boom-bust cycles in their history. The concomitant increase in housing prices (and in some cases boom-bust episodes) across many advanced economies raises the following important questions. Was there a housing bubble across advanced countries? What are the main determinants of the housing price movements in these countries? Are the advanced countries' housing markets interrelated? The first chapter of the dissertation estimates a set of structural Bayesian VAR models for the U.S., France, Spain, and Greece that examine the relative effects of developments in the real production sector, the financial sector, and international capital flows on the housing market. A second exercise attempts to identify the presence of housing price bubble regimes by estimating a set of two state Markov-switching Bayesian VAR models. The main results for the U.S. show that foreign capital inflows, measured by the current account balance as a percentage of GDP, account for more than 30\% of the variance of the shocks hitting housing prices, while adjustable mortgage rates contribute about 38\%. In France, monetary policy has the largest explanatory power for the housing market evolutions, while in Spain and Greece, the variable mortgage rates and housing investments exert the largest influence on the housing market. All the countries experienced a bubble regime over most of the 2000s. The second chapter uses a Global VAR model estimated using quarterly data from seven countries, for the period 1987-2011, to analyze the interdependencies that exist between domestic and international factors in housing markets. We find that housing price shocks originating in the U.S. have large spillover effects on all the countries, with the largest magnitudes on Ireland. This result suggests that housing markets may be subject to contagion effects and that housing can be analyzed as a speculative asset, based on international data spanning the past two decades. Linkages in long-run real interest rates are positive and statistically significant across all the countries, although they have a limited role on the evolution of housing prices. Negative shocks to the U.S. housing prices have negative and statistically significant effects on real GDP in the U.S., Canada, and Ireland. The third chapter studies the price fluctuations of war bonds issued by the U.S Treasury in order to finance the World War I between November 1917 and December 1920. Bayesian time series techniques are used to carry out the analyses. We are focusing on the effects that the bond-purchasing program of the War Finance Corporation (WFC) had on the evolution of war bond yields. Our main results show that positive shocks to WFC purchases display a negative and statistically significant effect on all types of war bond yields. Furthermore, WFC purchases of Liberty and Victory Bonds, except the First Liberty Loan, had a statistically significant effect on the evolution of commercial paper rates. WFC purchases of the Second and Fourth Liberty Bonds had significant and positive effects on commercial paper rates, suggesting a twist in the bond yield curve
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Sun, Lixin. "Monetary transmission mechanisms and the macroeconomy in China : VAR/VECM approach and Bayesian DSGE model simulation." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/2900/.

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In this thesis, by employing VAR/VECM approach and Bayesian Dynamic Stochastic General Equilibrium (DSGE) Model we have studied and tested the transmission mechanisms of China’s monetary policy and measured the effects of the monetary policy shocks and other exogenous macro shocks on the real macro economy to uncover the attributes of China’s business cycle. On the basis of the specified VAR/VEC Models, a bank lending channel, an interest rate channel and an asset price channel have been identified by using the time series (monthly) data of banks balance sheet variables (deposits, loans, securitises) across bank categories (aggregate banks, state banks, non-state banks) and the macroeconomic variables (output, CPI inflation, exports, imports, foreign exchange reserves) from 1996 to 2006. We’ve estimated a benchmark Bayesian DSGE Model with Taylor’s Rule and a modified Smets-Wouters Model with money growth rule by using China’s quarterly data from 1996 to 2006 to simulate the business cycle. The estimated values of the parameters demonstrate many unique features of China’s economy and policies operations. We find that investment and preference shocks drive the forecasted GDP variance in the long run in Taylor’s rule model, but in the money growth rule model, the main contributions to the variations of the output are government expenditure, preference and productive shocks.
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Ribeiro, Ramos Francisco Fernando, and fr1960@clix pt. "Essays in time series econometrics and forecasting with applications in marketing." RMIT University. Economics, Finance and Marketing, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20071220.144516.

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This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models. I use Autoregressive Integrated Moving Average (ARIMA) intervention models and the Pierce and Haugh statistical test to model the impact of a single marketing instrument, mainly price promotions, to measure own and cross-short term sales effects, and to study asymmetric marketing competition. I develop and apply Vector AutoRegressive (VAR) and Bayesian Vector AutoRegressive (BVAR) models to estimate dynamic relationships in the market and to forecast market share. Especially, BVAR models are advantageous because they contain all relevant dynamic and interactive effects. They accommodate not only classical competitive reaction effects, but also own and cross-market share brand feedback effects and internal decision rules and provided substantively useful insights into the dynamics of demand. The integrative essay is structured in four main parts. The introduction sets the basic ideas behind the published papers, with particular focus on the motivation of the essay, the types of competitive reaction effects analysed, an overview of the time series econometric models in marketing, a short discussion of the basic methodology used in the research and a brief description of the inter-relationships across the published papers and structure of the essay. The discussion is centred on how to model the effects of marketing actions at the selective demand or brand level and at the primary demand or product level. At the brand level I discuss the research contribution of my work on (i) modelling promotional short-term effects of price and non-price actions on sales and market share for consumer packaged goods, with no competition, (ii) how to measure own and cross short-term sales effects of advertising and price, in particular, cross-lead and lag effects, asymmetric sales behaviour and competition without retaliatory actions, in an automobile market, (iii) how to model the marketing-mix effectiveness at the short and long-term on market shares in a car market, (iv) what is the best method to forecast market share, and (v) the study of causal linkages at different time horizons between sales and marketing activity for a particular brand. At the product or commodity level, I propose a way to model the flows of tourists that come from different origins (countries) to the same country-destination as market segments defining the primary demand of a commodity - the product
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Books on the topic "Bayesian VAR"

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Kenny, Geoff. Bayesian VAR models for forecasting Irish inflation. Dublin: 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. Philadelphia: 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. [Wellington]: Reserve Bank of New Zealand, 1993.

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M, Smith Adrian F., ed. Bayesian theory. Chichester, Eng: Wiley, 1994.

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1956-, Allenby Greg M., and McCulloch Robert E, eds. Bayesian statistics and marketing. Hoboken, NJ: Wiley, 2005.

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Structural equation modeling: A Bayesian approach. Chichester, England: Wiley, 2007.

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Box, George E. P. Bayesian inference in statistical analysis. New York: Wiley, 1992.

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Nick, Chater, and Oaksford M, eds. The probabilistic mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press, 2008.

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Nick, Chater, and Oaksford M, eds. The probabilistic mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press, 2008.

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An introduction to Bayesian inference in econometrics. New York: John Wiley & Sons, Inc., 1996.

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Book chapters on the topic "Bayesian VAR"

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Polasek, Wolfgang, and Hideo Kozumi. "The VAR-VARCH model: A Bayesian approach." In Modelling and Prediction Honoring Seymour Geisser, 402–13. New York, NY: 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, 117–36. Dordrecht: 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, 73–84. Cham: 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, 685–703. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38253-7_46.

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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, 1–23. Singapore: 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, 85–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60354-0_6.

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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, 339–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_23.

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ANDERSSON, M., and S. KARLSSON. "Bayesian forecast combination for VAR models☆." In Bayesian Econometrics, 501–24. Elsevier, 2008. http://dx.doi.org/10.1016/s0731-9053(08)23015-x.

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Aye, Goodness, Pami Dua, and Rangan Gupta. "Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models." In Current Trends in Bayesian Methodology with Applications, 37–57. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18502-3.

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"Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models." In Current Trends in Bayesian Methodology with Applications, 77–98. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18502-9.

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Conference papers on the topic "Bayesian VAR"

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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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3560089.3560106.

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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). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/ssmi-19.2019.9.

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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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Sernaqué, Humberto, Moly Meca, Eduardo Zapata, Berenise Marchan, Junior Medina, Denis Nole, Cristhian Aldana, 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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Tuan, Nguyen Ngoc, and Huynh Quyet Thang. "ITERATION SCHEDULING USING BAYESIAN NETWORKS IN AGILE SOFTWARE DEVELOPMENT." In NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN. Publishing House for Science and Technology, 2017. http://dx.doi.org/10.15625/vap.2017.00038.

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Yang, Hojin, Tianshu Shen, and Scott Sanner. "Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3463108.

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Pereira, Ricardo Cardoso, Pedro Henriques Abreu, and Pedro Pereira Rodrigues. "VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridge Imputation of Missing Data." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206615.

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Reports on the topic "Bayesian VAR"

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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, November 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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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 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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Rincón-Castro, Hernán, and Norberto Rodríguez-Niño. Nonlinear pass-through of exchange rate shocks on inflation : a bayesian smooth transition VAR approach. Bogotá, Colombia: Banco de la República, March 2016. http://dx.doi.org/10.32468/be.930.

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Hajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, March 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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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Madrid: Banco de España, November 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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He, Yuping, Xiaolan He, Chunrong Li, Xiuqing Lu, Cuimin Shi, Junbing He, and Yao Lin. The Conservative Management for Improving Visual Analogue Pain Score (VAS) in Greater Trochanteric Pain Syndrome: A Bayesian analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0068.

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Rijgersberg, Hajo, Frank van de Geijn, Alex van Schaik, Don Willems, and Esther Hogeveen. Grip op kwaliteit van Conference-peren met behulp van een Bayesiaans netwerk : GreenCHAINge G&F DP5 Export peren verre bestemmingen. Wageningen: Wageningen Food & Biobased Research, 2018. http://dx.doi.org/10.18174/465143.

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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), September 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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LI, Zhendong, Hangjian Qiu, xiaoqian Wang, chengcheng Zhang, and Yuejuan Zhang. Comparative Efficacy of 5 non-pharmaceutical Therapies For Adults With Post-stroke Cognitive Impairment: Protocol For A Bayesian Network Analysis Based on 55 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0036.

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Review question / Objective: This study will provide evidence-based references for the efficacy of 5 different non-pharmaceutical therapies in the treatment of post-stroke cognitive impairment(PSCI). 1. Types of studies. Only randomized controlled trials (RCTs) of Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) and Computer-assisted cognitive rehabilitation(CA) for PSCI will be recruited. Additionally, Studies should be available in full papers as well as peer reviewed and the original data should be clear and adequate. 2. Types of participants. All adults with a recent or previous history of ischaemic or hemorrhagic stroke and diagnosed according to clearly defined or internationally recognized diagnostic criteria, regardless of nationality, race, sex, age, or educational background. 3.Types of interventions and controls. The control group takes non-acupuncture treatment, including conventional rehabilitation or in combination with symptomatic support therapy. The experimental group should be treated with acupuncture on basis of the control group. 4.The interventions of the experimental groups were Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) or Computer-assisted cognitive rehabilitation(CA), and the interventions of the control group takes routine rehabilitation and cognition training or other therapies mentioned above that were different from the intervention group. 5.Types of outcomes. The primary outcomes are measured with The Mini-Mental State Examination (MMSE) and/or The Montreal Cognitive Assessment Scale (MoCA), which have been widely used to evaluate the cognitive abilities. The secondary outcome indicator was the Barthel Index (BI) to assess independence in activities of daily living (ADLs).
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