Inference Using Simulated Neural Moments

This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as in Creel (2017). When MSM-MCMC estimation and inference is based on such moments, and using a continuously updating criteria function, confidence intervals have statistically correct coverage in all cases studied. The methods are illustrated by application to several test models, including a small DSGE model, and to a jump-diffusion model for returns of the S&P 500 index.

HETEROGENEOUS EXPECTATIONS AND ASSET PRICE DYNAMICS

Within the seminal asset-pricing model by Brock and Hommes (Journal of Economic Dynamics Control 22, 1235–1274, 1998), heterogeneous boundedly rational agents choose between a fixed number of expectation rules to forecast asset prices. However, agents’ heterogeneity is limited in the sense that they typically switch between a representative technical and a representative fundamental expectation rule. Here, we generalize their framework by considering that all agents follow their own time-varying technical and fundamental expectation rules. Estimating our model using the method of simulated moments reveals that it is able to explain the statistical properties of the daily and monthly behavior of the S&P500 quite well. Moreover, our analysis reveals that heterogeneity is not only a realistic model property but clearly helps to explain the intricate dynamics of financial markets.

The impact of Wal-Mart Supercenters’ entry on incumbent supermarkets’ profit margins

Purpose The purpose of this paper is to quantify the impact of Wal-Mart Supercenters (WMSs) on supermarkets’ profit margins using fluid milk in the Dallas/Fort Worth metropolitan area in the USA as a case study. Design/methodology/approach The authors develop a two-stage dynamic entry game to model market competition in the pre- and post-WMS stages, and test the theoretical model using the method of simulated moments and milk scanner data from Dallas/Fort Worth supermarkets. Findings The empirical findings show that the entry of WMSs accounts for an average of about 16.29-25.69 percent decline in milk profit margins of nearby, or incumbent, supermarkets. Economies of scale and chain synergies are found to be five times more significant for WMSs than for incumbent supermarkets, granting Wal-Mart a significant competitive edge. Originality/value This paper quantifies the impact of WMS’s entry on incumbent supermarkets’ profit margins through a structural model of entry. In addition, this paper assesses the effect economies of scale stemming from the ability to provide shopping convenience to consumers as well as chain economies from operating stores near each other.

Empirical Best Prediction Under Unit-Level Logit Mixed Models

The article applies unit-level logit mixed models to estimating small-area weighted sums of probabilities. The model parameters are estimated by the method of simulated moments (MSM). The empirical best predictor (EBP) of weighted sums of probabilities is calculated and compared with plug-in estimators. An approximation to the mean-squared error (MSE) of the EBP is derived and a bias-corrected MSE estimator is given and compared with parametric bootstrap alternatives. Some simulation experiments are carried out to study the empirical behavior of the model parameter MSM estimators, the EBP and plug-in estimators and the MSE estimators. An application to the estimation of poverty proportions in the counties of the region of Valencia, Spain, is given.

A Structural Analysis of Disappointment Aversion in a Real Effort Competition

We develop a novel computerized real effort task, based on moving sliders across a screen, to test experimentally whether agents are disappointment averse when they compete in a real effort sequential-move tournament. We predict that a disappointment averse agent, who is loss averse around her endogenous choice-acclimating expectations-based reference point, responds negatively to her rival's effort. We find significant evidence for this discouragement effect, and use the Method of Simulated Moments to estimate the strength of disappointment aversion on average and the heterogeneity in disappointment aversion across the population. (JEL C91, D12, D81, D84)