The first objective of this chapter is to present a new approach to econometric modeling of producer behavior. Our key contribution is to represent the rate and biases of technical change by unobservable or latent variables. We also divide the rate of technical change between components that are...

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. The authors develop a sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for...

This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the...

In this paper, we propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting...

The paper presents econometric estimates of the capital-labor substitution elasticities in terms of 10 economic activities based on the 2003-2009 data. Elasticities are estimated in the context of computable general equilibrium (CGE) methodology, particularly, based on the constant elasticity of...

We discuss the use of calibration techniques in economic models. Calibration contrasts with estimation in relying on deterministic calculation of model parameter values consistent with data, rather than econometric estimation. The reasons why calibrators use these methods, as well as the main...

In this paper, I show how gradient-based optimization methods can be used to estimate stochastic dynamic models in economics. By extending the state space to include all model parameters, I show that we need to solve the model only once to do structural estimation. Parameters are then estimated...

This paper introduces a novel approach for estimating heterogeneous-agent macroeconomic models adding information from micro data. The methodology covers both panels and repeated cross sections, with applications to a wide class of dynamic structural models used in macroeconomics. The routine...

We consider a class of infinite‐horizon dynamic Markov economic models in which the parameters of utility function, production function, and transition equations change over time. In such models, the optimal value and decision functions are time‐inhomogeneous: they depend not only on state...