Skip to main content

Bayesian Econometrics

Bayesian Econometrics

BIBLIOGRAPHY

Bayesian econometrics employs Bayesian methods for inference about economic questions using economic data. In the following, we briefly review these methods and their applications.

Suppose a data vector X = (X1, , X ) follows a distribution with a density function p (x ǀθ) which is fully characterized by some parameter vector θ = (θ 1, , θ d )'. Suppose that the prior belief about θ is characterized by a density p (θ ) defined over a parameter space Θ, a subset of a Euclidian space Rd. Using Bayes s rule to incorporate the information provided by the data, we can form posterior beliefs about the parameter θ, characterized by the posterior density

The posterior density pn (θ ǀX), or simply p (θ), describes how likely it is that a parameter value θ has generated the observed data X. We can use the posterior density to form optimal point estimates and optimal hypotheses tests. The notion of optimality is minimizing mean posterior loss, using various loss functions. For example, the posterior mean

is the point estimate that minimizes posterior mean squared loss. The posterior mode θ* is defined as the maximizer of the posterior density, and it is the decision that minimizes the posterior mean Dirac loss. When the prior density is flat, the posterior mode turns out to be the maximum likelihood estimator. The posterior quantiles characterize the posterior uncertainty about the parameter, and they can be used to form confidence regions for the parameters of interest (Bayesian credible regions). The posterior α-quantile θ j (α ) for θ j (the j- the component of the parameter vector) is the number c such that

Properties of Bayesian procedures in both large and small samples are as good as the properties of the procedures based on maximum likelihood. These properties have been developed by Pierre-Simon Laplace (1818), Peter Bickel and J. A. Yahav (1969), and Ildar Ibragimov and R. Z. Hasminskii (1981), among others. With mild regularity conditions (which hold in many econometric applications), the properties include:

(1) consistency and asymptotic normality of the point estimates, including asymptotic equivalence and efficiency of the posterior mean, mode, and median;

(2) asymptotic normality of the posterior density;

(3) asymptotically correct coverage of Bayesian confidence intervals; and

(4) average risk optimality of Bayesian estimates in small and hence large samples.

The regularity conditions for properties (1) and (2) require that the true parameter θ 0 is well identified and that the datas density Pn (x ǀθ) is sufficiently smooth in the parameters. Mathematically, property (1) means that

where J equals the information matrix limn , indicates agreement up to a stochastic term that approaches zero in large samples, and d N(0, J-1 ) means approximately distributed as a normal random vector with mean 0 and variance matrix J -1. These estimators are asymptotically efficient in the sense of having smallest variance J -1 in the class of asymptotically unbiased estimators. Property (2) is that pn(θ ) is approximately equal to a normal density with mean θ and variance J -1/n. Property (3) means that in large samples

In nonregular cases, such as in structural auction and search models, consistency and correct coverage properties also continue to hold (Chernozhukov and Hong 2004). Property (4) is implied by the defining property of the Bayes estimators that they minimize the posterior mean risk (Lehmann and Casella 1998). The property continues to hold in nonregular cases, which proved especially useful in nonregular econometric models (Hirano and Porter 2003; Chernozhukov and Hong 2004).

The explicit dependency of Bayesian estimates on the prior is both a virtue and a drawback. Priors allow us to incorporate information available from previous studies and various economic restrictions. When no prior information is available, diffuse priors can be used. Priors can have a large impact on inferential results in small samples, and in any other cases where the identifiability of parameters crucially relies on restrictions brought by the prior. In such cases, selection of priors requires a substantial care: See Gary Chamberlain and Guido Imbens (2003, 2004) for an example concerning simultaneous equations, and Harald Uhlig (2005) for an example dealing with sign restrictions in structural vector autoregressions. Conversely, priors should have little impact on the inferential results when the identifiability of parameters does not crucially rely on the prior and when sample sizes are large.

The appealing theoretical properties of Bayesian methods have been known for many years, but computational difficulties prevented their wide use. Closed-form solutions for estimators such as (2) have been derived only for very special cases. The recent emergence of Markov Chain Monte Carlo (MCMC) algorithms has diminished the computational challenge and made these methods attractive in a variety of practical applications; see for example, Christian Robert and George Casella (2004) and Jun Liu (2001). The idea of MCMC is to simulate a possibly dependent random sequence, (θ (1), , θ (B)), called a chain, such that stationary density of the chain is the posterior density pn (θ ). Then we approximate integrals such as (2) by the averages of the chain, that is, For computation of posterior quantiles, we simply take empirical quantiles of the chain. The leading MCMC method is the Metropolis-Hastings (MH) algorithm, which includes, for example, the random walk algorithm with Gaussian increments generating the candidate points for the chain. Such random walk is characterized by an initial point u 0 and a one-step move that consists of drawing a point η according to a Gaussian distribution centered on the current point u with covariance matrix σ2I, then moving to η with probability p = min{pn (η)/p n (u ), 1} and staying at u with probability 1 p. The MH algorithm is often combined with the Gibbs sampler, where the latter updates components of θ individually or in blocks. The Gibbs sampler can also speed up computation when the posterior for some components of θ is available in a closed form. MCMC algorithms have been shown to be computationally efficient in a variety of cases.

The classical econometric applications of Bayesian methods mainly dealt with the classical linear regression model and the classical simultaneous equation model, which admitted closed-form solutions (Zellner 1996; Poirier 1995). The emergence of MCMC has enabled researchers to attack a variety of complex nonlinear problems. The recent examples of important problems that have been solved using Bayesian methods include:

1. discrete choice models (Albert and Chib 1993; Lancaster 2004);

2. models with limited-dependent variables (Geweke 2005);

3. nonlinear panel data models with individual heterogeneity (McCulloch and Rossi 1994; Lancaster 2004);

4. structural vector autoregressions in macroeconomics, including models with sign restrictions (Uhlig 2005);

5. dynamic discrete decision processes (Geweke, Keane, and Runkle 1997; Geweke 2005);

6. dynamic stochastic equilibrium models (Smets and Wouters 2003; Del Negro and Schorfheide 2004);

7. time series models in finance (Fiorentini, Sentana, and Shephard 2004; Johannes and Polson 2003); and

8. unit root models (Sims and Uhlig 1991).

Econometric applications of the methods are rapidly expanding.

There are also recent developments that break away from the traditional parametric Bayesian paradigm. Jayanta Ghosh and R. V. Ramamoorthi (2003) developed and reviewed several nonparametric Bayesian methods. Chamberlain and Imbens (2003) developed Bayesian methods based on the multinomial framework of Thomas Ferguson (1973, 1974). In models with moment restrictions and no parametric likelihood available, Victor Chernozhukov and Han Hong (2003) proposed using an empirical likelihood function or a generalized method-of-moment criterion function in place of the unknown likelihood p n (X ǀθ ) in equation (1). This permits the application of MCMC methods to a variety of moment condition models. As a result, there are a growing number of applications of the latter approach to nonlinear simultaneous equations, empirical game-theoretic models, risk forecasting, and asset-pricing models. The literature on both theoretical and practical aspects of various nonpara-metric Bayesian methods is rapidly expanding.

SEE ALSO Econometrics; Least Squares, Ordinary; Regression; Regression Analysis; Simultaneous Equation Bias

BIBLIOGRAPHY

Albert, James H., and Siddhartha Chib. 1993. Bayesian Analysis of Binary and Polychotomous Response Data. Journal of the American Statistical Association 88 (422): 669679.

Bickel, Peter J., and J. A. Yahav. 1969. Some Contributions to the Asymptotic Theory of Bayes Solutions. Zeitschrift fur Wahrscheinlichkeitstheorie und Verwandte Gebiete 11: 257276.

Chamberlain, Gary, and Guido Imbens. 2003. Nonparametric Applications of Bayesian Inference. Journal of Business and Economic Statistics 21 (1): 1218.

Chamberlain, Gary, and Guido Imbens. 2004. Random Effects Estimators with Many Instrumental Variables. Econometrica 72 (1): 295306.

Chernozhukov, Victor, and Han Hong. 2003. An MCMC Approach to Classical Estimation. Journal of Econometrics 115 (2): 293346.

Chernozhukov, Victor, and Han Hong. 2004. Likelihood Estimation and Inference in a Class of Nonregular Econometric Models. Econometrica 72 (5): 14451480.

Del Negro, Marco, and Frank Schorfheide. 2004. Priors from General Equilibrium Models for VARs. International Economic Review 45: 643673.

Ferguson, Thomas S. 1973. A Bayesian Analysis of Some Nonparametric Problems. Annals of Statististics 1: 209230.

Ferguson, Thomas S. 1974. Prior Distributions on Spaces of Probability Measures. Annals of Statistics 2: 615629.

Fiorentini, Gabriele, Enrique Sentana, and Neil Shephard. 2004. Likelihood-Based Estimation of Latent Generalized ARCH Structures. Econometrica 72 (5): 14811517.

Geweke, John. 2005. Contemporary Bayesian Econometrics and Statistics. Hoboken, NJ: Wiley Interscience.

Geweke, John F., Michael P. Keane, and David E. Runkle. 1997. Statistical Inference in the Multinomial Multiperiod Probit Model. Journal of Econometrics 80 (1): 125165.

Ghosh, Jayanta K., and R. V. Ramamoorthi. 2003. Bayesian Nonparametrics. New York: Springer-Verlag.

Hirano, Keisuke, and Jack R. Porter. 2003. Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support. Econometrica 71 (5): 13071338.

Ibragimov, Ildar A., and R. Z. Hasminskii. 1981. Statistical Estimation: Asymptotic Theory. Trans. Samuel Kotz. New York: Springer-Verlag.

Johannes, Michael, and Nicholas Polson. 2006. MCMC Methods for Continuous-Time Financial Econometrics. In Handbook of Financial Econometrics, ed. Yacine Ait-Sahalia and Lars Peter Hansen. Amsterdam: North-Holland.

Lancaster, Tony. 2004. An Introduction to Modern Bayesian Econometrics. Malden, MA: Blackwell.

Laplace, Pierre-Simon. [1818] 1995. Théorie analytique des probabilités. Paris: Editions Jacques Gabay.

Lehmann, E. L., and George Casella. 1998. Theory of Point Estimation. New York: Springer.

Liu, Jun S. 2001. Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.

McCulloch, Robert, and Peter E. Rossi. 1994. An Exact Likelihood Analysis of the Multinomial Probit Model. Journal of Econometrics 64 (12): 207240.

Poirier, Dale J. 1995. Intermediate Statistics and Econometrics: A Comparative Approach. Cambridge: Massachusetts Institute of Technology Press.

Robert, Christian P., and George Casella. 2004. Monte Carlo Statistical Methods. 2nd ed. New York: Springer-Verlag.

Sims, Christopher A., and Harald Uhlig. 1991. Understanding Unit Rooters: A Helicopter Tour. Econometrica 59 (6): 15911599.

Smets, Frank, and Raf Wouters. 2003. An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of the European Economic Association 1: 527549.

Uhlig, Harald. 2005. What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure. Journal of Monetary Economics 52: 381419.

Zellner, Arnold. [1971] 1996. An Introduction to Bayesian Inference in Econometrics. New York: Wiley Interscience.

Victor Chernozhukov

Cite this article
Pick a style below, and copy the text for your bibliography.

  • MLA
  • Chicago
  • APA

"Bayesian Econometrics." International Encyclopedia of the Social Sciences. . Encyclopedia.com. 17 Sep. 2019 <https://www.encyclopedia.com>.

"Bayesian Econometrics." International Encyclopedia of the Social Sciences. . Encyclopedia.com. (September 17, 2019). https://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/bayesian-econometrics

"Bayesian Econometrics." International Encyclopedia of the Social Sciences. . Retrieved September 17, 2019 from Encyclopedia.com: https://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/bayesian-econometrics

Learn more about citation styles

Citation styles

Encyclopedia.com gives you the ability to cite reference entries and articles according to common styles from the Modern Language Association (MLA), The Chicago Manual of Style, and the American Psychological Association (APA).

Within the “Cite this article” tool, pick a style to see how all available information looks when formatted according to that style. Then, copy and paste the text into your bibliography or works cited list.

Because each style has its own formatting nuances that evolve over time and not all information is available for every reference entry or article, Encyclopedia.com cannot guarantee each citation it generates. Therefore, it’s best to use Encyclopedia.com citations as a starting point before checking the style against your school or publication’s requirements and the most-recent information available at these sites:

Modern Language Association

http://www.mla.org/style

The Chicago Manual of Style

http://www.chicagomanualofstyle.org/tools_citationguide.html

American Psychological Association

http://apastyle.apa.org/

Notes:
  • Most online reference entries and articles do not have page numbers. Therefore, that information is unavailable for most Encyclopedia.com content. However, the date of retrieval is often important. Refer to each style’s convention regarding the best way to format page numbers and retrieval dates.
  • In addition to the MLA, Chicago, and APA styles, your school, university, publication, or institution may have its own requirements for citations. Therefore, be sure to refer to those guidelines when editing your bibliography or works cited list.