A composite likelihood approach for dynamic structural models

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.

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

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.

Twenty Years of Time Series Econometrics in Ten Pictures

This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.

DESIGN OF DYNAMIC STRUCTURAL MODELS OF INFORMATION MANAGEMENT SYSTEM OF MOVING OBJECTS

In the following work the authors attempt to find the best way to design a dynamic structural model of information management system of moving objects. This structural model allows organizing various management systems of moving objects, considering the spatial and time dependencies between the key components or parameters of the said management system. An example of such system may be a group of UAVs.