Results 1 - 10
of
36
Inflation dynamics and the New Keynesian Phillips curve: an identification-robust econometric analysis
, 2005
"... ..."
Bayesian Model Averaging and Endogeneity Under Model Uncertainty: An Application to Development Determinants
, 2009
"... Recent approaches to development accounting reflect substantial model uncertainty at both the instrument and the development determinant level. Bayesian Model Averaging (BMA) has been proven useful in resolving model uncertainty in economics, and we extend BMA to formally account for model uncerta ..."
Abstract
-
Cited by 17 (0 self)
- Add to MetaCart
Recent approaches to development accounting reflect substantial model uncertainty at both the instrument and the development determinant level. Bayesian Model Averaging (BMA) has been proven useful in resolving model uncertainty in economics, and we extend BMA to formally account for model uncertainty in the presence of endogeneity. The new methodology is shown to be highly efficient and to reduce many-instrument bias; in a simulation study we found that IVBMA estimates reduced mean squared error by 60 % over standard IV estimates. We also introduce Bayesian over and under-identification tests that are based on model averaged predictive p-values. This approach is shown to mitigate the reduction in power these tests experience as dimension increases. In a simulation study where the exogeneity of the instrument is compromised we show that the classical Sargan test has a power of 0.2 % while our Bayesian over-identification test has a power of 98 % at detecting the violation of the exogeneity assumption. An application of our method to a prominent development accounting approach leads to new insights regarding the primacy of institutions.
A semiparametric Bayesian approach to the instrumental variable problem
- Journal of Econometrics
, 2008
"... ..."
On Bayesian Structural Inference in a Simultaneous Equation Model
- in Econometrics and the philosophy of economics, ed. by B.P. Stigum
, 2002
"... Econometric issues that are considered fundamental in the development of Bayesian structural inference within a Simultaneous Equation Model are surveyed. ..."
Abstract
-
Cited by 8 (2 self)
- Add to MetaCart
Econometric issues that are considered fundamental in the development of Bayesian structural inference within a Simultaneous Equation Model are surveyed.
A Comparison of Some Recent Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments
, 2000
"... We compare the finite sample performance of a number of Bayesian and classical procedures for limited information simultaneous equations models with weak instruments by a Monte Carlo study. We consider recent Bayesian approaches developed by Chao and Phillips (1998, CP), Geweke (1996), Kleibergen a ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
We compare the finite sample performance of a number of Bayesian and classical procedures for limited information simultaneous equations models with weak instruments by a Monte Carlo study. We consider recent Bayesian approaches developed by Chao and Phillips (1998, CP), Geweke (1996), Kleibergen and van Dijk (1998, KVD), and Zellner (1998). Amongst the sampling theory methods, OLS, 2SLS, LIML, Fuller's modified LIML, and the jackknife instrumental variable estimator (JIVE) due to Angrist, Imbens and Krueger (1999) and Blomquist and Dahlberg (1999) are also considered. Since the posterior densities and their conditionals in CP and KVD are non-standard, we propose a "Gibbs within Metropolis-Hastings" algorithm, which only requires the availability of the conditional densities from the candidate generating density. Our results show that in cases with very weak instruments, there is no single estimator that is superior to others in all cases. When endogeneity is weak, Zellner's MELO does the best. When the
Improvement of bias and coverage in instrumental variable analysis with weak instruments for continuous and binary outcomes.
- Statistics in Medicine
, 2012
"... Abstract Causal estimates can be obtained by instrumental variable analysis using a two-stage method. However, these can be biased when the instruments are weak. We introduce a Bayesian method, which adjusts for the first-stage residuals in the second-stage regression and has much improved bias and ..."
Abstract
-
Cited by 8 (5 self)
- Add to MetaCart
(Show Context)
Abstract Causal estimates can be obtained by instrumental variable analysis using a two-stage method. However, these can be biased when the instruments are weak. We introduce a Bayesian method, which adjusts for the first-stage residuals in the second-stage regression and has much improved bias and coverage properties. In the continuous outcome case, this adjustment reduces median bias from weak instruments to close to zero. In the binary outcome case, bias from weak instruments is reduced and the estimand is changed from a marginal population-based effect to a conditional effect. The lack of distributional assumptions on the posterior distribution of the causal effect gives a better summary of uncertainty and more accurate coverage levels than methods which rely on the asymptotic distribution of the causal estimate. These properties are discussed in the context of Mendelian randomization.
Jeffreys prior analysis of the simultaneous equations model in the case with n + 1 endogenous variables
- J. Econometrics
, 2005
"... COWLES FOUNDATION DISCUSSION PAPER NO. 1198 ..."
Two-stage Bayesian model averaging in endogenous variable models. Econometric Reviews, Forthcoming
, 2011
"... Economic modeling in the presence of endogeneity is subject to model uncertainty at both the instrument and covariate level. We propose a Two-Stage Bayesian Model Averaging (2SBMA) methodology that extends the Two-Stage Least Squares (2SLS) estimator. By constructing a Two-Stage Unit Information Pri ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
Economic modeling in the presence of endogeneity is subject to model uncertainty at both the instrument and covariate level. We propose a Two-Stage Bayesian Model Averaging (2SBMA) methodology that extends the Two-Stage Least Squares (2SLS) estimator. By constructing a Two-Stage Unit Information Prior in the endogenous variable model, we are able to efficiently combine established methods for addressing model uncertainty in regression models with the classic technique of 2SLS. To assess the validity of instruments in the 2SBMA context, we develop Bayesian tests of the identification restriction that are based on model averaged posterior predictive p-values. A simulation study showed that 2SBMA has the ability to recover structure in both the instrument and covariate set, and substantially improves the sharpness of resulting coefficient estimates in comparison to 2SLS using the full specification in an automatic fashion. Due to the increased parsimony of the 2SBMA estimate, the Bayesian Sargan test had a power of 50 percent in detecting a violation of the exogeneity assumption, while the method based on 2SLS using the full specification had negligible power. We apply our approach to the problem of development accounting, and find support not only for institutions, but also for geography and integration as development determi-nants, once both model uncertainty and endogeneity have been jointly addressed.
On Identification of Bayesian DSGE Models
, 2011
"... There has been increasing concern about parameter identification in dynamic stochastic general equilibrium (DSGE) models. Given their structure it may be diffi cult to determine whether a parameter is identified. When using Bayesian methods, a lack of identification may not be evident since the post ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
There has been increasing concern about parameter identification in dynamic stochastic general equilibrium (DSGE) models. Given their structure it may be diffi cult to determine whether a parameter is identified. When using Bayesian methods, a lack of identification may not be evident since the posterior may differ from its prior even if the parameter is unidentified. We suggest two Bayesian identification indicators that do not suffer from this diffi culty and are relatively easy to compute. The first applies where the parameters can be partitioned into those that are known to be identified and the rest where it is not known whether they are identified. In such cases the marginal posterior of an unidentified parameter will equal the posterior expectation of the prior for that parameter conditional on the identified parameters. The second indicator is more generally applicable and considers the rate at which the posterior precision gets updated as the sample size (T) is increased. For identified parameters the posterior precision rises with T, whilst for an unidentified parameter its posterior precision may be updated but its rate of update will be slower than T. These results are illustrated by means of simple DSGE models.
