scholarly journals A Composite Likelihood Approach for Dynamic Structural Models

2018 ◽  
Vol 18 (12) ◽  
pp. 1-40 ◽  
Author(s):  
Fabio Canova ◽  
◽  
Christian Matthes ◽  
Keyword(s):  
Structural Models ◽  
Composite Likelihood ◽  
Dynamic Structural Models ◽  
Likelihood Approach

scholarly journals A composite likelihood approach for dynamic structural models

2021 ◽  
Author(s):  
Fabio Canova ◽  
Christian Matthes
Keyword(s):  
General Equilibrium ◽  
Likelihood Function ◽  
Structural Models ◽  
Dynamic Stochastic General Equilibrium ◽  
Composite Likelihood ◽  
Data Sets ◽  
Equilibrium Models ◽  
Dynamic Structural Models ◽  
Dynamic Stochastic ◽  
Likelihood Approach

Abstract We explain how to use the composite likelihood function to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.

MANAGERIAL USE OF DYNAMIC STRUCTURAL MODELS

1977 ◽  
Vol 8 (1) ◽  
pp. 73-94 ◽  
Author(s):  
Elwood S. Buffa ◽  
James S. Dyer
Keyword(s):  
Structural Models ◽  
Dynamic Structural Models

scholarly journals Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

2020 ◽  
Vol 23 (3) ◽  
pp. S1-S24
Author(s):  
Mitsuru Igami
Keyword(s):  
Artificial Intelligence ◽  
Value Function ◽  
Structural Estimation ◽  
Structural Models ◽  
Policy Network ◽  
Value Network ◽  
Deep Blue ◽  
Dynamic Structural Models ◽  
Similarities And Differences ◽  
Cross Fertilization

Summary This article clarifies the connections between certain algorithms to develop artificial intelligence (AI) and the econometrics of dynamic structural models, with concrete examples of three 'game AIs'. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust’s nested fixed-point (NFXP) method. AlphaGo’s 'supervised-learning policy network' is a deep-neural-network implementation of the conditional-choice-probability (CCP) estimation reminiscent of Hotz and Miller's first step; the construction of its 'reinforcement-learning value network' is analogous to their conditional choice simulation (CCS). I then explain the similarities and differences between AI-related methods and structural estimation more generally, and suggest areas of potential cross-fertilization.

scholarly journals A composite likelihood approach for estimating HIV prevalence in the presence of spatial variation

2015 ◽  
Vol 34 (28) ◽  
pp. 3750-3759 ◽  
Author(s):  
Kathleen E. Wirth ◽  
Denis Agniel ◽  
Christopher D. Barr ◽  
Matthew D. Austin ◽  
Victor DeGruttola
Keyword(s):  
Spatial Variation ◽  
Hiv Prevalence ◽  
Composite Likelihood ◽  
Likelihood Approach
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