C51 Model Construction and Estimation
The new keynesian approach to dynamic general equilibrium modeling: models, methods, and macroeconomic policy evaluation
- This chapter aims to provide a hands-on approach to New Keynesian models and their
uses for macroeconomic policy analysis. It starts by reviewing the origins of the New Keynesian
approach, the key model ingredients and representative models. Building blocks of
current-generation dynamic stochastic general equilibrium (DSGE) models are discussed in
detail. These models address the famous Lucas critique by deriving behavioral equations
systematically from the optimizing and forward-looking decision-making of households and
firms subject to well-defined constraints. State-of-the-art methods for solving and estimating
such models are reviewed and presented in examples. The chapter goes beyond the mere
presentation of the most popular benchmark model by providing a framework for model
comparison along with a database that includes a wide variety of macroeconomic models.
Thus, it offers a convenient approach for comparing new models to available benchmarks
and for investigating whether particular policy recommendations are robust to model uncertainty.
Such robustness analysis is illustrated by evaluating the performance of simple
monetary policy rules across a range of recently-estimated models including some with financial
market imperfections and by reviewing recent comparative findings regarding the
magnitude of government spending multipliers. The chapter concludes with a discussion of
important objectives for on-going and future research using the New Keynesian framework.
Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
- We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.