Browsing by Subject "DSGE"

This dissertation consists of my essays in macroeconomics. In the first essay, I develop a new general equilibrium model to explain several facts about aggregate inventory that are challenging to existing inventory models. This work highlights the importance of taking into account consumers' search behavior in understanding aggregate inventory dynamics. In the second essay, I adapt a Markov-switching vector autoregression (MS-VAR) to uncover detailed asymmetries embedded in inventory dynamics. This work further demonstrates the shortcomings of existing inventory models and points to new directions in improving inventory models.

This dissertation presents two essays on Markov-Switching dynamic stochastic general equilibrium models.

The first essay is "Perturbation Methods for Markov-Switching Models," which is co-authored with Juan Rubio-Ramirez, Dan Waggoner, and Tao Zha. This essay develops an perturbation-based approach to solving dynamic stochastic general equilibrium models with Markov-Switching, which implies that parameters governing policies or the environment evolve over time in a discrete manner. Our approach has the advantages that it introduces regime switching from first principles, allows for higher-order approximations, shows non-certainty equivalence of first-order approximations, and allows checking the solution for determinacy. We explain the model setup, introduce an iterative procedure to solve the model, and illustrate it using a real business cycle example.

The second essay considers a model with financial frictions and studies the role of expectations and unconventional monetary policy during financial crises. During a financial crisis, the financial sector has

reduced ability to provide credit to productive firms, and the central bank may help lessen the magnitude of the downturn by using unconventional monetary policy to inject liquidity into credit markets. The model allows agents in the economy to expect policy changes by allowing parameters to change according to a Markov process, so agents have expectations about the probability of the central bank intervening during a crisis, and also have expectations about the central bank's exit strategy post-crisis.

Using this Markov Regime Switching specification, the paper addresses three issues. First, it considers the effects of different exit strategies, and shows that, after a crisis, if the central bank sells off its accumulated assets too quickly, the economy can experience a double-dip recession. Second, it analyzes the effects of expectations of intervention policy on pre-crisis behavior. In particular, if the central bank commits to always intervening during crises, there is a loss of output in pre-crisis times relative to if the central bank commits to never intervening. Finally, it considers the welfare implications of committing to intervening during crises, and shows that committing can raise or lower welfare depending upon the exit strategy used, and that committing before a crisis can be welfare decreasing but then welfare increasing once a crisis occurs.

This dissertation computes the optimal monetary and fiscal policy for small open and emerging economies in an estimated medium-scale model. The model departs from the conventional approach as it encompasses all the major nominal and real rigidities normally found in the literature in a single framework. After estimating the model using Bayesian techniques for one small open economy and one emerging economy, the Ramsey solution for the optimal monetary and fiscal policy is computed. Results show that foreign shocks have a strong influence in the dynamics of emerging economies, when compared to the designed optimal policy for a developed small open economy. For both economies, inflation is low, but very volatile, while taxes follow the traditional results in the literature with high taxes over labor income and low taxes for capital income.

This dissertation aims to introduce a new sequential Monte Carlo (SMC) based estimation framework for structural models used in macroeconomics and industrial organization. Current Markov chain Monte Carlo (MCMC) estimation methods for structural models suffer from slow Markov chain convergence, which means parameter and state spaces of interest might not be properly explored unless huge numbers of samples are simulated. This could lead to insurmountable computational burdens for the estimation of those structural models that are expensive to solve. In contrast, SMC methods rely on the principle of sequential importance sampling to jointly evolve simulated particles, thus bypassing the dependence on Markov chain convergence altogether. This dissertation will explore the feasibility and the potential benefits to estimating structural models using SMC based methods.

Chapter 1 casts the structural estimation problem in the form of inference of hidden Markov models and demonstrates with a simple growth model.

Chapter 2 presents the key ingredients, both conceptual and theoretical, to successful SMC parameter estimation strategies in the context of structural economic models.

Chapter 3, based on Chen, Petralia and Lopes (2010), develops SMC estimation methods for dynamic stochastic general equilibrium (DSGE) models. SMC algorithms allow a simultaneous filtering of time-varying state vectors and estimation of fixed parameters. We first establish empirical feasibility of the full SMC approach by comparing estimation results from both MCMC batch estimation and SMC on-line estimation on a simple neoclassical growth model. We then estimate a large scale DSGE model for the Euro area developed in Smets and Wouters (2003) with a full SMC approach, and revisit the on-going debate between the merits of reduced form and structural models in the macroeconomics context by performing sequential model assessment between the DSGE model and various VAR/BVAR models.

Chapter 4 proposes an SMC estimation procedure and show that it readily applies to the estimation of dynamic discrete games with serially correlated endogenous state variables. I apply this estimation procedure to a dynamic oligopolistic game of entry using data from the generic pharmaceutical industry and demonstrate that the proposed SMC method can potentially better explore the parameter posterior space while being more computationally efficient than MCMC estimation. In addition, I show how the unobserved endogenous cost paths could be recovered using particle smoothing, both with and without parameter uncertainty. Parameter estimates obtained using this SMC based method largely concur with earlier findings that spillover effect from market entry is significant and plays an important role in the generic drug industry, but that it might not be as high as previously thought when full model uncertainty is taken into account during estimation.