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Journal articles on the topic 'Bayesian econometrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 January 2025

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1

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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2

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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3

Zellner, Arnold. "Bayesian Econometrics." Econometrica 53, no. 2 (March 1985): 253. http://dx.doi.org/10.2307/1911235.

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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bauwens, Luc, Gary Koop, John Maheu, and Yasuhiro Omori. "Special issue on Bayesian econometrics." Computational Statistics & Data Analysis 100 (August 2016): 794. http://dx.doi.org/10.1016/j.csda.2016.02.007.

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12

Bauwens, Luc, Michel Lubrano, and Herman K. van Dijk. "Recent advances in Bayesian econometrics." Journal of Econometrics 123, no. 2 (December 2004): 197–99. http://dx.doi.org/10.1016/j.jeconom.2003.12.001.

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13

Zvyagin, Leonid S. "MATHEMATICAL AND INSTRUMENTAL METHODS FOR CONSTRUCTING BAYESIAN NETWORKS." SOFT MEASUREMENTS AND COMPUTING 9/2, no. 70 (2023): 29–39. http://dx.doi.org/10.36871/2618-9976.2023.09-2.004.

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Currently, there is an active and widespread use of the Bayesian approach in activities with numerical information, both in theoretical and experimental econometric studies. The Bayesian approach has a significant advantage over classical methods in terms of the accuracy of statistical inference in a situation with small amounts of sample data, which is especially common in econometric studies. The main difference between the Bayesian approach and classical methods in econometrics lies in the way the actual parameters of the models are interpreted. From the standpoint of classical methods, model parameters are not assumed to be random, but only their estimates are considered random, and are presented in the form of observation functions that include elements of randomness. From the perspective of Bayesian methods, an extended interpretation of model parameters is considered, namely, that the parameters are random in nature and relate it to the properties of the real world, as well as the fact that physical objects are characterized by continuous random variations. In turn, estimates of these model parameters from the point of view of the Bayesian approach are not accepted as random, and, consequently, their determination is carried out.

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14

Ziegel, Eric R., D. Berry, K. Chaloner, and J. Geweke. "Bayesian Analysis in Statistics and Econometrics." Technometrics 39, no. 1 (February 1997): 111. http://dx.doi.org/10.2307/1270799.

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15

Koop, Gary. "RECENT PROGRESS IN APPLIED BAYESIAN ECONOMETRICS." Journal of Economic Surveys 8, no. 1 (March 1994): 1–34. http://dx.doi.org/10.1111/j.1467-6419.1994.tb00173.x.

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16

Thurman, Walter N. "Bayesian Specification Analysis in Econometrics: Comment." American Journal of Agricultural Economics 83, no. 5 (December 2001): 1187–89. http://dx.doi.org/10.1111/0002-9092.00265.

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17

Geweke, John. "Bayesian reduced rank regression in econometrics." Journal of Econometrics 75, no. 1 (November 1996): 121–46. http://dx.doi.org/10.1016/0304-4076(95)01773-9.

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18

Sims, Christopher A. "Bayesian skepticism on unit root econometrics." Journal of Economic Dynamics and Control 12, no. 2-3 (June 1988): 463–74. http://dx.doi.org/10.1016/0165-1889(88)90050-4.

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19

Koop, Gary, and Herman K. van Dijk. "Editors' Introduction to the Special Issue of Econometric Reviews on Bayesian Dynamic Econometrics." Econometric Reviews 26, no. 2-4 (April 12, 2007): 107–12. http://dx.doi.org/10.1080/07474930701220675.

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20

Hepple, L. W. "Bayesian Techniques in Spatial and Network Econometrics: 2. Computational Methods and Algorithms." Environment and Planning A: Economy and Space 27, no. 4 (April 1995): 615–44. http://dx.doi.org/10.1068/a270615.

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Bayesian theory has been seen as having considerable potential and attractiveness for model estimation and analysis in spatial and network econometrics. However, analytical and computational problems have also been seen as a great barrier. In this paper the analytical simplifications available are developed and the algorithms required are examined. The author argues that, for a broad class of models in spatial econometrics, Bayesian analysis is quite practicable and can be implemented without great cost. The spatial specifications are mapped into the various forms of Bayesian computation available and detailed examples are provided. Recent developments on the frontier of Bayesian computation have potential to expand further the practical applicability of the Bayesian approach to spatial econometrics.

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21

Geweke, John. "Comment on Poirier: Operational Bayesian Methods in Econometrics." Journal of Economic Perspectives 2, no. 1 (February 1, 1988): 159–66. http://dx.doi.org/10.1257/jep.2.1.159.

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[This paper responds to “Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics,” by Dale J. Poirier, in this same issue.] My purpose in this comment is to illustrate that economists can indeed act upon the agenda implied by Poirier's five pragmatic principles of model building. Many economic researchers have had a subjective view of the world for a long time. But now, the argument that Bayesian approaches do not lead to operational methods, often decisive one or two decades ago, is becoming irrelevant in rapidly increasing numbers of applications as cheap and massive desktop computing power spreads. For the foreseeable future there will be substantial returns to developing appropriate numerical methods of Bayesian inference in econometrics, and to using these methods in empirical work. Many econometric problems that are messy and intractable from a classical perspective can become elegant and straightforward from a Bayesian perspective. I am not interested in defending this statement as an abstract proposition. Rather, I am interested in providing solutions to problems that have been stubbornly immune to treatment by classical methods. I will take up two of the scores of such problems here.

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22

Alam, Jessica, Konstantinos Georgalos, and Harrison Rolls. "Risk preferences, gender effects and Bayesian econometrics." Journal of Economic Behavior & Organization 202 (October 2022): 168–83. http://dx.doi.org/10.1016/j.jebo.2022.08.013.

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23

MW and Arnold Zellner. "An Introduction to Bayesian Inference in Econometrics." Journal of the American Statistical Association 92, no. 439 (September 1997): 1227. http://dx.doi.org/10.2307/2965616.

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24

Bivand, Roger S., Virgilio Gómez-Rubio, and Håvard Rue. "Approximate Bayesian inference for spatial econometrics models." Spatial Statistics 9 (August 2014): 146–65. http://dx.doi.org/10.1016/j.spasta.2014.01.002.

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25

Wang, Haiyue. "Application of Bayesian Econometrics in Policy Evaluation." Journal of Applied Mathematics and Physics 12, no. 11 (2024): 3765–73. http://dx.doi.org/10.4236/jamp.2024.1211226.

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26

Griffin, Jim, Maria Kalli, and Mark Steel. "Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics." Statistical Methods & Applications 27, no. 2 (July 10, 2017): 207–18. http://dx.doi.org/10.1007/s10260-017-0384-0.

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27

Poirier, Dale J. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics." Journal of Economic Perspectives 2, no. 1 (February 1, 1988): 121–44. http://dx.doi.org/10.1257/jep.2.1.121.

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I plan to discuss, in as simple and nontechnical a fashion as possible, the subjectivist-Bayesian attitude toward model building in econometrics and to contrast it with the standard frequentist attitude. To convey what I believe is the principle distinguishing attitude between Bayesians and non-Bayesians, I refer to their respective positions as “subjectivist” and “frequentist.” The basic differences between these positions arise from different interpretations of “probability.” Frequentists interpret probability as a property of the external world, i.e., the limiting relative frequency of the occurrence of an event as the number of suitably defined trials goes to infinity. For a subjectivist, probability is interpreted as a degree of belief fundamentally internal to the individual as opposed to some characteristic of the external world. Subjective probability measures a relationship between the observer and events (not necessarily “repetitive”) of the outside world, expressing the observer's personal uncertainty about those events. The subjectivist paradigm is designed to produce “coherent” revisions in beliefs about future observables in light of observed data. Most of the issues I raise are familiar to statisticians but not to economists. Rather than give the suspicious reader a menu of Bayesian techniques, I hope to create an interest in acquiring a taste for the Bayesian cuisine by recommending five pragmatic principles.

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28

Lopes, Hedibert F., and Ruey S. Tsay. "Particle filters and Bayesian inference in financial econometrics." Journal of Forecasting 30, no. 1 (July 29, 2010): 168–209. http://dx.doi.org/10.1002/for.1195.

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29

Karlsson, Sune. "Corrigendum to “Bayesian reduced rank regression in econometrics” [J. Econometrics 75 (1996) 121–146]." Journal of Econometrics 201, no. 1 (November 2017): 170–71. http://dx.doi.org/10.1016/j.jeconom.2012.10.005.

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30

Strachan, Rodney. "Guest Editorial: Workshop on Bayesian Econometric Methods." Review of Economic Analysis 2, no. 2 (August 4, 2010): 135–36. http://dx.doi.org/10.15353/rea.v2i2.1467.

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31

Martin, Gael. "A Review of The Oxford Handbook of Bayesian Econometrics." Econometrics Journal 15, no. 3 (October 1, 2012): B11—B15. http://dx.doi.org/10.1111/j.1368-423x.2012.00377.x.

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32

Choi, Taeryon, Yasuhiro Omori, Michael Smith, and Stephen G. Walker. "Special issue on Bayesian methods in statistics and econometrics." Econometrics and Statistics 3 (July 2017): 1–2. http://dx.doi.org/10.1016/j.ecosta.2017.05.003.

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33

Jackson, Mathew O. "Bayesian Implementation." Econometrica 59, no. 2 (March 1991): 461. http://dx.doi.org/10.2307/2938265.

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34

Kamenica, Emir, and Matthew Gentzkow. "Bayesian Persuasion." American Economic Review 101, no. 6 (October 1, 2011): 2590–615. http://dx.doi.org/10.1257/aer.101.6.2590.

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When is it possible for one person to persuade another to change her action? We consider a symmetric information model where a sender chooses a signal to reveal to a receiver, who then takes a noncontractible action that affects the welfare of both players. We derive necessary and sufficient conditions for the existence of a signal that strictly benefits the sender. We characterize sender-optimal signals. We examine comparative statics with respect to the alignment of the sender's and the receiver's preferences. Finally, we apply our results to persuasion by litigators, lobbyists, and salespeople. (JEL D72, D82, D83, K40, M31)

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35

Serletis, Apostolos. "A BAYESIAN CLASSIFICATION APPROACH TO MONETARY AGGREGATION." Macroeconomic Dynamics 13, no. 2 (April 2009): 200–219. http://dx.doi.org/10.1017/s1365100508080024.

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In this article we use Bayesian classification and finite mixture models to extract information from the MSI database (maintained by the Federal Reserve Bank of St. Louis) and construct a new set of non-nested monetary aggregates (under the Divisia aggregation procedure) based on statistical similarities and multidimensional structures. We also use recent advances in the fields of applied econometrics, dynamical systems theory, and statistical physics to investigate the relationship between the new money measures and economic activity. The empirical results offer practical evidence in favor of this approach to monetary aggregation.

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36

Florens, Jean-Pierre, and Anna Simoni. "REGULARIZING PRIORS FOR LINEAR INVERSE PROBLEMS." Econometric Theory 32, no. 1 (November 6, 2014): 71–121. http://dx.doi.org/10.1017/s0266466614000796.

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This paper proposes a new Bayesian approach for estimating, nonparametrically, functional parameters in econometric models that are characterized as the solution of a linear inverse problem. By using a Gaussian process prior we propose the posterior mean as an estimator and prove frequentist consistency of the posterior distribution. The latter provides the frequentist validation of our Bayesian procedure. We show that the minimax rate of contraction of the posterior distribution can be obtained provided that either the regularity of the prior matches the regularity of the true parameter or the prior is scaled at an appropriate rate. The scaling parameter of the prior distribution plays the role of a regularization parameter. We propose a new data-driven method for optimally selecting in practice this regularization parameter. We also provide sufficient conditions such that the posterior mean, in a conjugate-Gaussian setting, is equal to a Tikhonov-type estimator in a frequentist setting. Under these conditions our data-driven method is valid for selecting the regularization parameter of the Tikhonov estimator as well. Finally, we apply our general methodology to two leading examples in econometrics: instrumental regression and functional regression estimation.

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37

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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38

Ahelegbey, Daniel Felix. "The econometrics of Bayesian graphical models: a review with financial application." Journal of Network Theory in Finance 2, no. 2 (June 2016): 1–33. http://dx.doi.org/10.21314/jntf.2016.016.

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39

MTW and Arnold Zellner. "Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers." Journal of the American Statistical Association 93, no. 443 (September 1998): 1251. http://dx.doi.org/10.2307/2669901.

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40

Lafuente, Juan Ángel, Amparo Marco, Mercedes Monfort, and Javier Ordóñez. "Does Perceived Corruption Converge? International Evidence." Economics 16, no. 1 (January 1, 2022): 43–56. http://dx.doi.org/10.1515/econ-2022-0018.

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Abstract This article analyses the evolution over time of perceived corruption for a large set of countries worldwide. To proxy corruption, we use the recently proposed Bayesian Corruption Index (Standaert, S. (2015). Divining the level of corruption: A bayesian state space approach, Journal of Comparative Economics, 43(3), 782–803). We employ the test developed by (Phillips, P., & Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75, 1771–1855) that enables the endogenous determination of convergence clubs for countries over time. Having divided countries into convergence clubs, we explore whether each club differs from the others in terms of their competitiveness ranking. In particular, drawing on the 2019 Global Competitiveness Report, we focus not only on the global competitiveness score, but also on the first and the fifth pillars of competitiveness: institutions and health, respectively. Mean and median scores for clubs confirm the general rule that low perceived corruption levels tend to be associated with high-income countries with established democracies, high-quality healthcare systems, and relatively low-income inequality. However, countries such as Spain and Italy, which are innovation-driven economies with excellent scores in the health pillar, are in the worst club for perceived corruption, suggesting there are additional idiosyncratic aspects that could drive perceived corruption levels.

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41

Dietrich, Franz. "Fully Bayesian aggregation." Journal of Economic Theory 194 (June 2021): 105255. http://dx.doi.org/10.1016/j.jet.2021.105255.

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42

Woźniak, Tomasz. "Bayesian Vector Autoregressions." Australian Economic Review 49, no. 3 (September 2016): 365–80. http://dx.doi.org/10.1111/1467-8462.12179.

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43

Brusco, Sandro. "Perfect Bayesian implementation." Economic Theory 5, no. 3 (October 1995): 419–44. http://dx.doi.org/10.1007/bf01212327.

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44

Duggan, John. "Virtual Bayesian Implementation." Econometrica 65, no. 5 (September 1997): 1175. http://dx.doi.org/10.2307/2171883.

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45

Leamer, Edward E. "Bayesian Elicitation Diagnostics." Econometrica 60, no. 4 (July 1992): 919. http://dx.doi.org/10.2307/2951572.

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46

d'Aspremont, Claude, Jacques Crémer, and Louis-André Gérard-Varet. "Balanced Bayesian mechanisms." Journal of Economic Theory 115, no. 2 (April 2004): 385–96. http://dx.doi.org/10.1016/j.jet.2003.07.001.

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47

Luo, Xiao, and Chih-Chun Yang. "Bayesian coalitional rationalizability." Journal of Economic Theory 144, no. 1 (January 2009): 248–63. http://dx.doi.org/10.1016/j.jet.2008.03.004.

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48

Stewart, Colin. "Nonmanipulable Bayesian testing." Journal of Economic Theory 146, no. 5 (September 2011): 2029–41. http://dx.doi.org/10.1016/j.jet.2011.06.015.

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49

Arieli, Itai, and Yakov Babichenko. "Private Bayesian persuasion." Journal of Economic Theory 182 (July 2019): 185–217. http://dx.doi.org/10.1016/j.jet.2019.04.008.

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50

Mongin, Philippe. "Consistent Bayesian Aggregation." Journal of Economic Theory 66, no. 2 (August 1995): 313–51. http://dx.doi.org/10.1006/jeth.1995.1044.

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