Relevant bibliographies by topics / Bayesian econometrics

Contents

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

Academic literature on the topic 'Bayesian econometrics'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 January 2025

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Journal articles on the topic "Bayesian econometrics"

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QIN, Duo. "BAYESIAN ECONOMETRICS: The First Twenty Years." Econometric Theory 12, no. 3 (August 1996): 500–516. http://dx.doi.org/10.1017/s0266466600006836.

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This paper sketches the history of how Bayesian inference was adopted and utilized in econometrics during its first 20 years. It focuses on the causes of the Bayesian movement, the ways in which Bayesian inference was applied, the problems that the application was intended to solve, and the results achieved. It shows that Bayesian research has largely followed mainstream econometric development as far as the major econometric ideas and methods are concerned and that Bayesian reformulation of mainstream econometrics has nevertheless helped in deepening econometricians' understanding of many modeling problems by presenting them from a different angle.
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Bernardi, Mauro, Stefano Grassi, and Francesco Ravazzolo. "Bayesian Econometrics." Journal of Risk and Financial Management 13, no. 11 (October 29, 2020): 257. http://dx.doi.org/10.3390/jrfm13110257.

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The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics.
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Zellner, Arnold. "Bayesian Econometrics." Econometrica 53, no. 2 (March 1985): 253. http://dx.doi.org/10.2307/1911235.

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Lenk, Peter, and Michel Wedel. "Bayesian econometrics:." Journal of Econometrics 100, no. 1 (January 2001): 79–80. http://dx.doi.org/10.1016/s0304-4076(00)00061-0.

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Davidson, Russell. "An Agnostic Look at Bayesian Statistics and Econometrics." Review of Economic Analysis 2, no. 2 (August 6, 2010): 153–68. http://dx.doi.org/10.15353/rea.v2i2.1470.

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Bayesians and non-Bayesians, often called frequentists, seem to be perpetually at loggerheads on fundamental questions of statistical inference. This paper takes as agnostic a stand as is possible for a practising frequentist, and tries to elicit a Bayesian answer to questions of interest to frequentists. The argument is based on my presentation at a debate organised by the Rimini Centre for Economic Analysis, between me as the frequentist “advocate”, and Christian Robert on the Bayesian side.
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Jacobi, Liana. "Introduction to Bayesian Econometrics." Economic Record 85, no. 270 (September 2009): 364–66. http://dx.doi.org/10.1111/j.1475-4932.2009.00578.x.

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Geweke, John. "Bayesian econometrics and forecasting." Journal of Econometrics 100, no. 1 (January 2001): 11–15. http://dx.doi.org/10.1016/s0304-4076(00)00046-4.

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Zellner, Arnold. "Bayesian analysis in econometrics." Journal of Econometrics 37, no. 1 (January 1988): 27–50. http://dx.doi.org/10.1016/0304-4076(88)90072-3.

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Otter, Thomas. "Contemporary Bayesian Econometrics and Statistics." Journal of the American Statistical Association 101, no. 475 (September 2006): 1313–14. http://dx.doi.org/10.1198/jasa.2006.s128.

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Geweke, John, and William McCausland. "Bayesian Specification Analysis in Econometrics." American Journal of Agricultural Economics 83, no. 5 (December 2001): 1181–86. http://dx.doi.org/10.1111/0002-9092.00264.

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Dissertations / Theses on the topic "Bayesian econometrics"

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KIM, DONG-HYUK. "Bayesian Econometrics for Auction Models." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/193663.

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This dissertation develops Bayesian methods to analyze data from auctions and produce policy recommendations for auction design. The essay, "Auction Design Using Bayesian Methods," proposes a decision theoretic method to choose a reserve price in an auction using data from past auctions. Our method formally incorporates parameter uncertainty and the payoff structure into the decision procedure. When the sample size is modest, it produces higher expected revenue than the plug-in methods. Monte Carlo evidence for this is provided. The second essay, "Flexible Bayesian Analysis of First Price Auctions Using Simulated Likelihood," develops an empirical framework that fully exploits all the shape restrictions arising from economic theory: bidding monotonicity and density affiliation. We directly model the valuation density so that bidding monotonicity is automatically satisfied, and restrict the parameter space to rule out all the nonaffiliated densities. Our method uses a simulated likelihood to allow for a very exible specification, but the posterior analysis is exact for the chosen likelihood. Our method controls the smoothness and tail behavior of the valuation density and provides a decision theoretic framework for auction design. We reanalyze a dataset of auctions for drilling rights in the Outer Continental Shelf that has been widely used in past studies. Our approach gives significantly different policy prescriptions on the choice of reserve price than previous methods, suggesting the importance of the theoretical shape restrictions. Lastly, in the essay, "Simple Approximation Methods for Bayesian Auction Design," we propose simple approximation methods for Bayesian decision making in auction design problems. Asymptotic posterior distributions replace the true posteriors in the Bayesian decision framework, which are typically a Gaussian model (second price auction) or a shifted exponential model (first price auction). Our method first approximates the posterior payoff using the limiting models and then maximizes the approximate posterior payoff. Both the approximate and exact Bayes rules converge to the true revenue maximizing reserve price under certain conditions. Monte Carlo studies show that my method closely approximates the exact procedure even for fairly small samples.
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Kalli, Maria. "Bayesian Nonparametrics and Applications in Financial Econometrics." Thesis, University of Kent, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499786.

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Cornwall, Gary J. "Three Essays on Bayesian Econometric Methods." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504801632767553.

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Santos, Fernando Genta dos. "Ensaios sobre macroeconometria bayesiana aplicada." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12138/tde-04042012-201945/.

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Os três artigos que compõe esta Tese possuem em comum a utilização de técnicas macroeconométricas bayesianas, aplicadas a modelos dinâmicos e estocásticos de equilíbrio geral, para a investigação de problemas específicos. Desta forma, esta Tese busca preencher importantes lacunas presentes na literatura nacional e internacional. No primeiro artigo, estimou-se a importância do canal de custo da política monetária por meio de um modelo novo-keynesiano dinâmico e estocástico de equilíbrio geral. Para tanto, alteramos o modelo convencional, assumindo que uma parcela das firmas precise contrair empréstimos para pagar sua folha salarial. Desta forma, a elevação da taxa nominal de juro impacta positivamente o custo unitário do trabalho efetivo, podendo acarretar em aumento da inflação. Este artigo analisa as condições necessárias para que o modelo gere esta resposta positiva da inflação ao aperto monetário, fenômeno esse que ficou conhecido como price puzzle. Devido ao uso da metodologia DSGE-VAR, os resultados aqui encontrados podem ser comparados tanto com a literatura que trata o puzzle como um problema de identificação dos modelos VAR como com a literatura que avalia o canal de custo por meio de modelos novo-keynesianos. No segundo artigo, avaliamos até que ponto as expectativas de inflação geradas por um modelo dinâmico e estocástico de equilíbrio geral são compatíveis com as expectativas coletadas pelo Banco Central do Brasil (BCB). Este procedimento nos permite analisar a racionalidade das expectativas dos agentes econômicos brasileiros, comparando-as não à inflação observada, mas sim à projeção de um modelo desenvolvido com a hipótese de expectativas racionais. Além disso, analisamos os impactos do uso das expectativas coletadas pelo BCB na estimação do nosso modelo, no que se refere aos parâmetros estruturais, função de resposta ao impulso e análise de decomposição da variância. Por fim, no terceiro artigo desta Tese, modificamos o modelo novo-keynesiano convencional, de forma a incluir a teoria do desemprego proposta pelo economista Jordi Galí. Com isso, procuramos preencher uma lacuna importante na literatura nacional, dominada por modelos que não contemplam a possibilidade de desequilíbrios no mercado de trabalho capazes de gerar desemprego involuntário. A interpretação alternativa do mercado de trabalho aqui utilizada permite superar os problemas de identificação notoriamente presentes na literatura, tornando o modelo resultante mais robusto. Desta forma, utilizamos o modelo resultante para, dentre outras coisas, avaliar os determinantes da taxa de desemprego ao longo da última década.
The three articles that comprise this thesis have in common the use of macroeconometric bayesian techniques, applied to dynamic stochastic general equilibrium models, for the investigation of specific problems. Thus, this thesis seeks to fill important gaps present in the national and international literatures. In the first article, I estimated the importance of the cost-push channel of monetary policy through a new keynesian dynamic stochastic general equilibrium model. To this end, we changed the conventional model, assuming now that a share of firms needs to borrow to pay its payroll. Thus, an increase in the nominal interest rate positively impacts the effective unit labor cost and may result in an inflation hike. This article analyzes the necessary conditions for the model to exhibit a positive response of inflation to a monetary tightening, a phenomenon that became known as the price puzzle. Because I use the DSGE-VAR methodology, the present results can be compared both with the empirical literature dealing with the puzzle as an identification problem of VAR models and with the theoretical literature that evaluates the cost-push channel through new keynesian models. In the second article, we assess the extent to which inflation expectations generated by a dynamic stochastic general equilibrium model are consistent with expectations compiled by the Central Bank of Brazil (BCB). This procedure allows us to analyze the rationality of economic agents\' expectations in Brazil, comparing them not with the observed inflation, but with the forecasts of a model developed with the hypothesis of rational expectations. In addition, we analyze the impacts of using expectations compiled by the BCB in the estimation of our model, looking at the structural parameters, the impulse response function and variance decomposition analysis. Finally, the third article in this thesis, I modified the conventional new keynesian model, to include unemployment as proposed by the economist Jordi Galí. With that, I fill an important gap in the national literature, dominated by models that do not contemplate the possibility of disequilibrium in the labor market that can generate involuntary unemployment. The alternative interpretation of the labor market used here overcomes the identification problems notoriously present in the literature, making the resulting model more robust to the Lucas critique. Thus, I use the resulting model to assess the determinants of the unemployment rate over the last decade, among other points.
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Wang, Jiahui. "Three essays on econometrics /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/7477.

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Fantinatti, Marcos da Costa. "Modelo de equilíbrio geral estocástico e o mercado de trabalho brasileiro." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/12/12138/tde-25022016-112933/.

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Os três artigos desta tese focam no mercado de trabalho. O primeiro artigo calculou a probabilidade com que um trabalhador deixa o emprego e a probabilidade com que um desempregado encontra trabalho no Brasil. A metodologia utilizada foi a desenvolvida por Shimer (2012). O objetivo foi determinar qual destes dois fatores seria o mais importante para explicar as flutuações da taxa de desemprego no Brasil. Os resultados mostraram que é a dinâmica da probabilidade com que um desempregado encontra emprego que explica o comportamento da taxa de desemprego. Este resultado é distinto daquele encontrado normalmente na literatura. No segundo artigo, log-linearizamos e estimados o modelo de Christiano, Eichenbaum e Trabandt (2013) para o Brasil. Este modelo difere dos modelos novos keynesianos tradicionais ao colocar uma estrutura de searching (busca) para o mercado de trabalho. A ideia foi comparar este modelo com o modelo de rigidez de preços e salários tradicional e analisar se esta estrutura para o mercado de trabalho é capaz de fazer o papel das rigidezes tradicionais, no que se refere a propagação dos choques na economia. As funções impulso resposta a um choque contracionista de política monetária mostraram que o modelo explicou o comportamento esperado para variáveis como PIB, inflação e taxa de desemprego. Ainda, a estimação do modelo mostrou, no geral, que os preços no Brasil são reajustados com uma frequência menor do que a frequência indicada pelos modelos novos keynesianos com rigidez de preços e salários. Por sua vez, ao desligar a rigidez da utilização do capital e a do working capital channel, este modelo mais completo, maior e mais detalhado para mercado de trabalho pareceu não ser capaz de dar conta do movimento inercial e persistente observado para as variáveis macroeconômicas como PIB e inflação. Por fim, no terceiro artigo, estimamos novamente o modelo Christiano, Eichenbaum e Trabandt (2013), mas agora para os Estados Unidos. Entretanto, adotamos uma estratégia de estimação diferente: optamos por primeiro log-linearizar o modelo para depois fazer a estimação, para dois períodos: até 2008, assim como no artigo original, e até 2014. O objetivo principal foi comparar os resultados da nossa estimativa com os resultados de Christiano, Eichenbaum e Trabandt (2013). Para o conjunto de dados até 2008, os resultados indicam que os valores estimados estão em linha com os encontrados na literatura e, no geral, não estão muito distantes das estimações do artigo original. Mas, os parâmetros estimados apontaram para um modelo com um pouco mais de rigidez de preços, uma maior persistência de consumo e com uma regra de política monetária um pouco menos inercial em relação à do artigo original. Entretanto, esta regra mostrou uma reação muito maior à inflação do que ao produto, assim como em Christiano, Eichenbaum e Trabandt (2013). Considerando a amostra toda, isto é, até o final de 2014, observamos que o modelo estimado continuou a ter uma maior rigidez de preço em relação ao modelo original e uma regra de política monetária menos inercial. Além disso, os dados mais recentes afetaram de modo mais expressivo os valores estimados para variáveis do mercado de trabalho. Por sua vez, as funções impulso resposta refletiram esta menor inércia da política monetária e, no geral, apresentaram as trajetórias esperadas.
The three articles of this thesis focus on the labor market. The first article calculated the probability of a worker leaving his job and the probability of an unemployed person finding a job in Brazil, using the methodology developed by Shimer (2012). The aim was to determine which of these factors was the most important to explain the unemployment rate fluctuations. The results showed that the probability of an unemployed worker finding a job is more important to explain the dynamic of the unemployment rate. Commonly, the literature has found an opposite result in Brazil. In the second article, we log linearized and estimated the model built by Christiano, Eichenbaum and Evans (2013) for Brazil. This model is different from the traditional New Keynesian models because it has a structure of searching in the labor market. The idea was to compare this model with the traditional one with sticky wage and sticky prices. Moreover, the idea was to analyze if this model with searching structure in the labor market was able to substitute some traditional rigidity when the concern is the propagation of shocks. The impulse response functions to a contractionist monetary policy shock showed that this model explains the dynamic that is normally found in GDP, inflation and unemployment rate. Furthermore, the estimation showed that, in general, the prices are readjusted less frequently than the frequency estimated by New Keynesian models with sticky wage and sticky prices. Besides, when the rigidities (capital utilization and working capital channel) are eliminated, this model did not properly explain the inertial and persistence dynamic of the macroeconomics variables, such as GDP and inflation. Finally, in the last article, we estimated the Christiano, Eichenbaum and Trabandt (2013) model for the United States, but we adopted a different estimation strategy. We log linearized the model and estimated it with Bayesian methods. Moreover, we estimated for two different periods. The aim was to compare our results with the original model. When the model was estimated with data up to 2008, the results showed that the estimations were in line with the values found in the literature and, in general, they were not too far from the values estimated in the original article. However, the parameters estimated showed a model in which the prices are more rigid, the consumption habit is higher and the monetary rule is less inertial than observed in the original model. However, the monetary authority reacted much more to inflation than GDP, as it happened in the original article. When we considered the data until 2014, we observed that the estimated model remained with more sticky prices and a more inertial monetary rule. Moreover, we noted that this more recent data affected more expressively the estimated values of the labor market. The analysis of impulse response function showed this less inertial dynamic of the monetary rule and, overall, they followed the expected dynamics
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Jarocinski, Marek. "Essays on bayesian and classical econometrics with small samples." Doctoral thesis, Universitat Pompeu Fabra, 2006. http://hdl.handle.net/10803/7339.

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Esta tesis se ocupa de los problemas de la estimación econométrica con muestras pequeñas, en los contextos del los VARs monetarios y de la investigación empírica del crecimiento. Primero, demuestra cómo mejorar el análisis con VAR estructural en presencia de muestra pequeña. El primer capítulo adapta la especificación con prior intercambiable (exchangeable prior) al contexto del VAR y obtiene nuevos resultados sobre la transmisión monetaria en nuevos miembros de la Unión Europea. El segundo capítulo propone un prior sobre las tasas de crecimiento iniciales de las variables modeladas. Este prior resulta en la corrección del sesgo clásico de la muestra pequeña en series temporales y reconcilia puntos de vista Bayesiano y clásico sobre la estimación de modelos de series temporales. El tercer capítulo estudia el efecto del error de medición de la renta nacional sobre resultados empíricos de crecimiento económico, y demuestra que los procedimientos econométricos robustos a incertidumbre acerca del modelo son muy sensibles al error de medición en los datos.
This thesis deals with the problems of econometric estimation with small samples, in the contexts of monetary VARs and growth empirics. First, it shows how to improve structural VAR analysis on short datasets. The first chapter adapts the exchangeable prior specification to the VAR context, and obtains new findings about monetary transmission in New Member States. The second chapter proposes a prior on initial growth rates of modeled variables, which tackles the Classical small-sample bias in time series, and reconciles Bayesian and Classical points of view on time series estimation. The third chapter studies the effect of measurement error in income data on growth empirics, and shows that econometric procedures which are robust to model uncertainty are very sensitive to measurement error of the plausible size and properties.
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Wu, Yue. "Bayesian dynamic covariance models with applications to finance and econometrics." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708037.

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Serrano, Fábio Martins. "Impacto regional da política monetária no Brasil: uma abordagem bayesiana." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/12/12138/tde-16032015-132813/.

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Esta dissertação tem como objetivo (i) estimar a resposta das Unidades Federativas brasileiras a um choque de política monetária e, caso as mesmas respondam de forma assimétrica, (ii) compreender os determinantes de tal heterogeneidade. Para tanto, faz-se uso de técnicas de econometria Bayesiana, assim como em Francis et al (2012). Tais técnicas visam contornar o problema de dimensionalidade inerente aos modelos que englobam um número elevado de variáveis, além de permitir modelar formalmente a incerteza existente na escolha do conjunto de covariadas adequado. A resposta das Unidades Federativas foi estimada através de um VAR Bayesiano. A evidência encontrada sugere que as Unidades Federativas brasileiras respondem de forma assimétrica às inovações de política monetária. As regiões Sul e Sudeste apresentam as maiores contrações em resposta ao choque, enquanto a região Norte não apresentou sensibilidade. Para o estudo dos determinantes das assimetrias regionais, utilizou-se o Bayesian Model Averaging. Apesar do grande grau de incerteza acerca dos determinantes da heterogeneidade encontrada, Estados com maior porcentagem de empregos provenientes da indústria de transformação tendem a ser mais sensíveis às variações exógenas de política monetária. O resultado encontrado aponta para a relevância do canal de juros na determinação das assimetrias no nível estadual.
The purpose of this dissertation is to (i) estimate the impact of a monetary policy shock at the Brazilian state level economies and, if they do respond asymmetrically, (ii) to investigate the causes of this heterogeneity. Therefore, Bayesian econometric techniques were used, following Francis et al (2012). These techniques not only overcome the problem of dimensionality, inherent to large size models, but also provide a formal framework to model the uncertainties involving the choice of the appropriate set of covariates. A Bayesian VAR was estimated in order to access the regional responses. The results indicate that the Brazilian state level monetary policy innovation responses are asymmetric. The greatest responses were found at the South and Southeast Regions, while the North Region seems to be insensible to an interest rate shock. The Bayesian Model Averaging technique was implemented to access the determinants of the state level asymmetries. Despite the large degree of uncertainty about the determinants of the response heterogeneity, states with greater share of manufacturing jobs tend to be more sensible to exogenous changes in monetary policy. The results found points to the importance of the interest rate channel in determining Brazilian state level asymmetries.
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Farrell, Patrick John. "Empirical Bayes estimation of small area proportions." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=70301.

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Due to the nature of survey design, the estimation of parameters associated with small areas is extremely problematic. In this study, techniques for the estimation of small area proportions are proposed and implemented. More specifically, empirical Bayes estimation methodologies, where random effects which reflect the complex structure of a multi-stage sample design are incorporated into logistic regression models, are derived and studied.
The proposed techniques are applied to data from the 1950 United States Census to predict local labor force participation rates of females. Results are compared with those obtained using unbiased and synthetic estimation approaches.
Using the proposed methodologies, a sensitivity analysis concerning the prior distribution assumption, conducted with a view toward outlier detection, is performed. The use of bootstrap techniques to correct measures of uncertainty is also studied.
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Books on the topic "Bayesian econometrics"

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Inc, ebrary, ed. Bayesian econometrics. Bingley: Emerald JAI, 2008.

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Greenberg, Edward. Introduction to Bayesian econometrics. Cambridge: Cambridge University Press, 2008.

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Duo, Qin. Rise of Bayesian econometrics. London: London University, Queen Mary and Westfield College, Department of Economics, 1993.

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Bhat, Avanindra Narayan. Bayesian Inference in Econometrics. Delhi, India: B.R. Publishing Corporation, 1999.

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Geweke, John. Contemporary Bayesian Econometrics and Statistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471744735.

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Goel, Prem K., and N. Sreenivas Iyengar, eds. Bayesian Analysis in Statistics and Econometrics. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2944-5.

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1943-, Goel Prem K., Iyengar N. Sreenivas, and Indo-U.S. Workshop on Bayesian Analysis in Statistics and Econometrics (1988 : Bangalore, India), eds. Bayesian analysis in statistics and econometrics. New York: Springer-Verlag, 1992.

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Zellner, Arnold. An introduction to Bayesian inference in econometrics. Malabar, Fla: R.E. Krieger Pub. Co., 1987.

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Gregoriou, Greg N., and Razvan Pascalau, eds. Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295223.

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Ngoc Thach, Nguyen, Vladik Kreinovich, Doan Thanh Ha, and Nguyen Duc Trung, eds. Financial Econometrics: Bayesian Analysis, Quantum Uncertainty, and Related Topics. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98689-6.

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Book chapters on the topic "Bayesian econometrics"

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Poirier, Dale J. "Bayesian Econometrics." In The New Palgrave Dictionary of Economics, 772–82. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2754.

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Poirier, Dale J. "Bayesian Econometrics." In The New Palgrave Dictionary of Economics, 1–11. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2754-1.

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Chan, Joshua, and Justin L. Tobias. "Bayesian Econometrics Methods." In Handbook of Labor, Human Resources and Population Economics, 1–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-57365-6_55-1.

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Zong, Ping. "The Bayesian Approach." In The Art and Science of Econometrics, 190–208. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003273905-8.

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Polasek, Wolfgang. "Remarks on Foundations of Bayesian Statistics and Econometrics." In Probability and Bayesian Statistics, 379–93. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1885-9_39.

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Phaneuf, Daniel J., and George Van Houtven. "Structural Benefit Transfer Using Bayesian Econometrics." In Benefit Transfer of Environmental and Resource Values, 525–49. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9930-0_23.

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Chen, Cathy W. S., and Yu-Wen Sun. "Bayesian Forecasting for Tail Risk." In Predictive Econometrics and Big Data, 122–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_6.

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Tran, Hien D., and Phuong Anh Nguyen. "Towards Generalizing Bayesian Statistics: A Random Fuzzy Set Approach." In Econometrics of Risk, 149–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13449-9_10.

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Zellner, Arnold. "Bayesian Methods and Entropy in Economics and Econometrics." In Maximum Entropy and Bayesian Methods, 17–31. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3460-6_2.

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Geisser, Seymour. "Bayesian Perturbation Diagnostics and Robustness." In Bayesian Analysis in Statistics and Econometrics, 289–301. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2944-5_20.

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Conference papers on the topic "Bayesian econometrics"

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Isaeva, Marta. "Planning machine experiments based on the Bayesian method." In Multivariate statistical analysis, econometrics and simulation of real processes. Proceedings of Xth International School-Seminar. CEMI RAS, 2020. http://dx.doi.org/10.33276/978-5-8211-0786-2-69-70.

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Frischknecht, Bart D. "A Bayesian Approach to Extrinsic Versus Intrinsic Uncertainty in Design for Market Systems." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12712.

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This article illustrates how variance in the predictive distribution of the profit objective function in a design for market systems model can be decomposed into two components using a simulation based Bayesian approach introduced in the econometrics literature. The first component, intrinsic uncertainty, would be retained in the model even if the model calibration parameter values, such as parameters representing customer preferences, were known with certainty. The second component, extrinsic uncertainty, stems from lack of precision regarding model calibration parameters such as customer preferences. The simulation based approach overcomes a key problem in decomposing uncertainty for the typical design for market systems problem by overcoming the difficulties associated with analytical treatment of non-normal distributions. The variance decomposition approach is demonstrated for the design of a handheld grinder power tool. Following the same Bayesian decision analysis framework the variance simulation method can be applied to other design for market system problems with other objective functions and with additional sources of uncertainty.
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DRACHAL, Krzysztof. "Bayesian Symbolic Regression and Other Similar Methods as a Tool for Forecasting Commodities Prices." In The International Conference on Economics and Social Sciences. Editura ASE, 2024. http://dx.doi.org/10.24818/icess/2024/063.

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Bayesian Symbolic Regression (BSR) is used to predict spot prices of 56 commodities. BSR is a certain improvement over the symbolic regression technique based on genetic programming. Besides, there has been limited applications of the symbolic regression to forecasting prices in economics and finance. Contrary to prior simulations of BSR with synthetic data, this study discusses an application to the real-world data derived from commodities markets. In particular, forecasting one month ahead spot prices of 56 commodities. Indeed, BSR presents valuable capabilities for addressing the complexities associated with variable selection in econometric modelling. It is expected to also handle also some other challenges smoothly. Therefore, this study is carefully tailored to deal with commodity markets time-series data. Moreover, several alternative techniques are also tested, i.e., the symbolic regression with genetic programming, Dynamic Model Averaging, LASSO and RIDGE regressions, time-varying parameters regression, ARIMA, and no-change method, etc. In particular, the main aim is to focus on forecast accuracy. The obtained outcomes can give valuable insights for both researchers and practitioners interested in implementing BSR in econometric and financial projects in the future.
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Ounsakul, T., T. Techanukul, C. Phasook, and P. Harke. "Spread Rate Forecasting in Well Cost Estimation – A Study of Methods and Applications." In SPE/IADC Asia Pacific Drilling Technology Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/219600-ms.

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Abstract In the realm of well cost estimation, the accurate forecasting of spread rates is pivotal, given the substantial financial implications of erroneous assumptions. This paper, "Spread Rate Forecasting in Well Cost Estimation – A Study of Methods and Applications," delves into the uncertainty inherent. Through a thorough examination of predictive methodologies, the research harnesses both econometric and machine learning models, which are commonly utilized in forecasting crude oil prices. The study formulates models based on publicly available data, such as ‘West Texas Intermediate’ (WTI) and the ‘Baker Hughes Rig Count’, to predict the Spread Rate. The empirical results underscore the efficacy of the proposed models, with the predicted spread rates closely mirroring actual figures. Notably, the models’ precision wanes when extending the forecast horizon beyond a year, a limitation accentuated by the unforeseen WTI and Spread Cost fluctuations during the COVID-19 pandemic. A comparative analysis shows the superiority of RNN, LSTM, Bayesian, and OLS models over the ARIMA model, evidenced by lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. The paper advocates for a probabilistic approach to navigate the uncertainties prevalent in long-term forecasting endeavors.
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Reports on the topic "Bayesian econometrics"

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Giacomini, Raffaella, Toru Kitagawa, and Matthew Read. Robust Bayesian Analysis for Econometrics. Federal Reserve Bank of Chicago, 2021. http://dx.doi.org/10.21033/wp-2021-11.

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Koop, Gary, Jamie Cross, and Aubrey Poon. Introduction to Bayesian Econometrics in MATLAB. Instats Inc., 2022. http://dx.doi.org/10.61700/t3wrch7yujr7a469.

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This seminar provides an introduction to Bayesian econometrics. It covers the general theory underlying Bayesian econometrics and Bayesian inference in the linear regression model including an introduction of Bayesian machine learning methods for Big Data regression. Bayesian computational methods such as Gibbs sampling and the Metropolis-Hastings algorithm will be covered, with hands-on lab sections run using real-world data so that you will be able to apply these methods in your ongoing research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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Koop, Gary, Jamie Cross, and Aubrey Poon. Introduction to Bayesian Econometrics in MATLAB. Instats Inc., 2023. http://dx.doi.org/10.61700/aebi3thp50fr3469.

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This seminar provides an introduction to Bayesian econometrics. It covers the general theory underlying Bayesian econometrics and Bayesian inference in the linear regression model including an introduction of Bayesian machine learning methods for Big Data regression. Bayesian computational methods such as Gibbs sampling and the Metropolis-Hastings algorithm will be covered, with hands-on lab sections run using real-world data so that you will be able to apply these methods in your ongoing research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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