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Found 8 papers, 1 papers with code
Solving the Forecast Combination Puzzle
In addition, we demonstrate that the low power of such predictive accuracy tests in the forecast combination setting can be completely avoided if more efficient estimation strategies are used in the production of the combinations, when feasible.
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods.
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting.
Weak Identification in Discrete Choice Models
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models.
Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects
This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models.
Approximate Maximum Likelihood for Complex Structural Models
This new approach is based on using a constrained approximation to the structural model, which ensures identification and can deliver estimators that are nearly efficient.
Efficient Bayesian synthetic likelihood with whitening transformations
Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution -- typically Gaussian -- and then performs statistical inference using standard likelihood-based techniques.
Robust Approximate Bayesian Inference with Synthetic Likelihood
Similar to other approximate Bayesian methods, such as the method of approximate Bayesian computation, implicit in the application of BSL is the maintained assumption that the data generating process (DGP) can generate simulated summary statistics that capture the behaviour of the observed summary statistics.
Methodology Applications Computation
