Computationally Intensive Methods for Integration in Econometrics

By John Geweke, Michael P. Keane. 2001

160 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Panel Data Discrete Choice Models of Consumer Demand (2013, background): “As the focus here is on panel data issues and not problems that already arise in cross-section discrete choice models, I‘ll refer the reader to Geweke and Keane (2001) for further details.”
Discussion Papers in Economics and Econometrics A Computationally Practical Simulation Estimation Algorithm for Dynamic panel Data Models with Unobserved Endogenous State Variables (2005, methodology): “This pattern emerges because π11t is an increasing linear function of the proportion choosing dit = 1, and π00t is a decreasing linear function of the same proportion, as shown in (8).”
Labor Supply and Taxes: A Survey (2011, methodology): “102 Alternative solution methods are discussed in detail in a number of references, including Rust (1987), Geweke and Keane (2001) and Aguirregebaria and Mira (2010) The next step of the backsolving process is to move back to period T-1.”
Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation (2007, methodology): “Often, parametric structural models are computationally cumbersome [see Geweke and Keane (2001)] and identification in dynamic recursive models is often difficult to establish [see Rust (1994), Magnac and Thesmar (2002)], although progress has been made [see Taber (2001), Aguirregabiria (2004),…”
Invited Paper - Learning Models: An Assessment of Progress, Challenges, and New Developments (2011, unknown): “But advances in simulation methods, such as the GHK algorithm and Gibbs sampling (see Geweke and Keane (2001), McCulloch, Polson and Rossi (2000)) have greatly increased the popularity of probit, particularly among Bayesians.”
COMPARING ALTERNATIVE MODELS OF HETEROGENEITY IN CONSUMER CHOICE BEHAVIOR (2012, methodology): “It has been extended to probit by Geweke and Keane (1999, 2001) and to MIXL by Rossi et al (2005) and Burda et al (2008).10 The appeal of this approach is that the discrete mixture-of-normals can approximate any heterogeneity distribution arbitrarily well.”
Estimating Dynamic Models of Imperfect Competition (2004, unknown): “Manski and Tamer (2002) and Haile and Tamer (2003) use simulated annealing to sample the objective function and then construct one-dimensional bounds on the parameters.”
Smoothly mixing regressions (2007, methodology): “This is an advantage of Bayesian MCMC approaches generally over methods like simulated maximum likelihood and simulated method of moments that require the computation of state probabilities (see Geweke et al. (1994) and Geweke and Keane (2001)).”
The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity (2010, unknown): “The hierarchical Bayes (HB) approach has also become popular recently, in part because advances in simulation methods (Markov chain Monte Carlo) made it computationally practical (see Allenby and Rossi 1998, Geweke and Keane 2001).”