Dress-up contest: a dark side of fiscal decentralization

A `dress-up contest' is a competition for the best public image, and fiscal decentralisation can lead to such contests between local governments. In this paper we model the dress-up contest and investigate how it a effects social welfare. We show that yardstick competition (due to fiscal decentralisation) forces local governments to allocate more resources to more visible public goods (such as cash assistance) than less visible goods (such as vendor payments) and thus starts dress-up contests. The resulting distortion of resource allocation causes a structural bias in public expenditure and further hurts social welfare. To empirically verify our theoretical model, we employ U.S. state-level data from 1992 to 2008, and we estimate the panel data model using various econometric approaches. The empirical results provide strong evidence that fiscal decentralisation can lead to distortion in public expenditure arising from dress-up contests. We also find that this distortion increases the regional poverty rate

Asymptotic Properties of the Synthetic Control Method

This paper provides new insights into the asymptotic properties of the synthetic control method (SCM). We show that the synthetic control (SC) weight converges to a limiting weight that minimizes the mean squared prediction risk of the treatment-effect estimator when the number of pretreatment periods goes to infinity, and we also quantify the rate of convergence. Observing the link between the SCM and model averaging, we further establish the asymptotic optimality of the SC estimator under imperfect pretreatment fit, in the sense that it achieves the lowest possible squared prediction error among all possible treatment effect estimators that are based on an average of control units, such as matching, inverse probability weighting and difference-in-differences. The asymptotic optimality holds regardless of whether the number of control units is fixed or divergent. Thus, our results provide justifications for the SCM in a wide range of applications. The theoretical results are verified via simulations

Optimal model averaging estimation for partially linear models

This article studies optimal model averaging for partially linear models with heteroscedasticity. A Mallows-type criterion is proposed to choose the weight. The resulting model averaging estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments suggest that the proposed model averaging method is superior to other commonly used model selection and averaging methods. The proposed procedure is further applied to study Japan’s sovereign credit default swap spreads

Optimal model averaging for single-index models with divergent dimensions

This paper offers a new approach to address the model uncertainty in (potentially) divergent-dimensional single-index models (SIMs). We propose a model-averaging estimator based on cross-validation, which allows the dimension of covariates and the number of candidate models to increase with the sample size. We show that when all candidate models are misspecified, our model-averaging estimator is asymptotically optimal in the sense that its squared loss is asymptotically identical to that of the infeasible best possible averaging estimator. In a different situation where correct models are available in the model set, the proposed weighting scheme assigns all weights to the correct models in the asymptotic sense. We also extend our method to average regularized estimators and propose pre-screening methods to deal with cases with high-dimensional covariates. We illustrate the merits of our method via simulations and two empirical applications.<br/

Testing for Stock Market Contagion: A Quantile Regression Approach

__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test allows us to investigate the stock market contagion at various quantiles, not only at the mean. We show that the quantile contagion test can detect a contagion effect that is possibly ignored by correlation-based tests. A wide range of simulation studies show that the proposed test is superior to the correlation-based tests in terms of size and power. We compare our test with correlation-based tests using three real data sets: the 1994 Tequila crisis, the 1997 Asia crisis, and the 2001 Argentina crisis. Empirical results show substantial differences between two types of tests