Bayesian Econometrics
Bayesian Econometrics
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 R^{d}. 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 p_{n} (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 αquantile 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 PierreSimon Laplace (1818), Peter Bickel and J. A. Yahav (1969), and Il’dar Ibragimov and R. Z. Has’minskii (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 data’s density P_{n} (x ǀθ) is sufficiently smooth in the parameters. Mathematically, property (1) means that
where J equals the information matrix lim_{n} , ≈ 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 p_{n}(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. Closedform 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 p_{n} (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 MetropolisHastings (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 onestep move that consists of drawing a point η according to a Gaussian distribution centered on the current point u with covariance matrix σ^{2}I, then moving to η with probability p = min{p_{n} (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 closedform 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 limiteddependent 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 methodofmoment 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 gametheoretic models, risk forecasting, and assetpricing models. The literature on both theoretical and practical aspects of various nonparametric Bayesian methods is rapidly expanding.
SEE ALSO Econometrics; Least Squares, Ordinary; Regression; Regression Analysis; Simultaneous Equation Bias
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Victor Chernozhukov
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