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Found 7 papers, 0 papers with code
Inference on LATEs with covariates
In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification without making parametric assumptions on how available covariates enter the model.
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data.
Instrument-based estimation of full treatment effects with movers
The effect of the full treatment is a primary parameter of interest in policy evaluation, while often only the effect of a subset of treatment is estimated.
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting.
Efficient variational approximations for state space models
Variational Bayes methods are a potential scalable estimation approach for state space models.
Fast variational Bayes methods for multinomial probit models
Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities.
Scalable Bayesian estimation in the multinomial probit model
Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives.
