Large deviation asymptotics for statistical treatment rules

This note applies large deviation-based optimality theory to evaluate treatment rules for treatment assignment problems. We find nearly optimal treatment rules whose asymptotic maximum large deviation risks can be arbitrary close to the corresponding minimax bounds.     

Conditional empirical likelihood estimation and inference for quantile regression models

This paper considers two empirical likelihood-based estimation, inference, and specification testing methods for quantile regression models. First, we apply the method of conditional empirical likelihood (CEL) by Kitamura et al. [2004. Empirical likelihood-based inference in conditional moment restriction models. Econometrica 72, 1667–1714] and Zhang and Gijbels [2003. Sieve empirical likelihood and extensions of the generalized least squares. Scandinavian Journal of Statistics 30, 1–24] to quantile regression models. Second, to avoid practical problems of the CEL method induced by the discontinuity in parameters of CEL, we propose a smoothed counterpart of CEL, called smoothed conditional empirical likelihood (SCEL). We derive asymptotic properties of the CEL and SCEL estimators, parameter hypothesis tests, and model specification tests. Important features are (i) the CEL and SCEL estimators are asymptotically efficient and do not require preliminary weight estimation; (ii) by inverting the CEL and SCEL ratio parameter hypothesis tests, asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators; and (iii) in contrast to CEL, the SCEL method can be implemented by some standard Newton-type optimization. Simulation results demonstrate that the SCEL method in particular compares favorably with existing alternatives.     

Local GMM estimation of time series models with conditional moment restrictions

This paper investigates statistical properties of the local generalized method of moments (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown forms and establish the consistency, asymptotic normality, and semi-parametric efficiency of the LGMM estimator. Second, we undertake a higher-order asymptotic expansion and demonstrate that the LGMM estimator possesses some appealing bias reduction properties for positively autocorrelated processes. Our analysis of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to shed light on the nature of the bias correction performed by the LGMM estimator. The practical importance of these findings is evaluated in terms of a bond and option pricing exercise based on a diffusion model for spot interest rate.     

Enhancing market discipline in banking: The role of subordinated debt in financial regulatory reform

The increasing complexity of large financial firms has led to consideration of alternative regulatory structures. This has intensified recently because of the worldwide turmoil in financial markets. One important consideration has been to increase reliance on market discipline—most notably, increased reliance on subordinated debt (sub-debt) in the bank capital structure to discipline banks’ risk taking. This proposal, however, has been subject to criticism related to the quality of the signal generated in current sub-debt markets. We argue that previous studies evaluating the potential usefulness of sub-debt proposals have evaluated sub-debt spreads in a very different environment from that characterized by a fully implemented sub-debt program, where the market will become deeper, issuance will be more frequent, debt will be viewed as a more viable means to raise capital, bond dealers will be less reluctant to publicly disclose more details on debt transactions, and generally, the market will be more closely followed. As a test to see how the quality of the signal may change, we evaluate the risk-spread relationship—accounting for the enhanced liquidity and market transparency surrounding new debt issues. Our empirical results indicate a superior risk-spread relationship surrounding the period of new debt issuance due, we posit, to greater liquidity and transparency. Our results overall suggest that the degree of market discipline would be significantly enhanced by a mandatory sub-debt program, thus suggesting a potential role for sub-debt in the banking regulatory reform.     

NATURAL DISASTERS,DAMAGE TO BANKS,AND FIRM INVESTMENT

This article investigates the effect of banks’ lending capacity on firms’ investment. To identify exogenous shocks to loan supply, we utilize the natural experiment provided by Japan's Great Hanshin‐Awaji earthquake in 1995. Using a unique data set that allows us to identify firms and banks in the earthquake‐affected areas, we find that the investment ratio of firms located outside the earthquake‐affected areas but having a main bank inside the areas was significantly smaller than that of firms located outside the areas and having a main bank outside the areas. Our findings suggest that loan supply shocks affect firm investment.     

Testing for non-nested conditional moment restrictions using unconditional empirical likelihood

We propose non-nested hypothesis tests for conditional moment restriction models based on the method of generalized empirical likelihood (GEL). By utilizing the implied GEL probabilities from a sequence of unconditional moment restrictions that contains equivalent information of the conditional moment restrictions, we construct Kolmogorov–Smirnov and Cramér–von Mises type moment encompassing tests. Advantages of our tests over Otsu and Whang’s (2011) tests are: (i) they are free from smoothing parameters, (ii) they can be applied to weakly dependent data, and (iii) they allow non-smooth moment functions. We derive the null distributions, validity of a bootstrap procedure, and local and global power properties of our tests. The simulation results show that our tests have reasonable size and power performance in finite samples.     

Organizational dynamics and aggregate fluctuations: The role of financial relationships

This paper constructs an estimated dynamic stochastic general equilibrium model to study the role of labor reallocation between production and organizational tasks within a firm in movements in measured TFP in Japan. Allocating more labor to organizational tasks strengthens the financial relationship with financial intermediaries and helps firms to mitigate a widening in credit spread during financial difficulties. However, doing so reduces the labor allocated to production tasks and hence the measured TFP. Our quantitative analysis indicates that labor reallocation contributes to the observed procyclicality in measured TFP. We also find that this mechanism amplifies and propagates the effects of exogenous shocks on aggregate activity.     

Optimal comparison of misspecified moment restriction models under a chosen measure of fit

Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman–Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties. Local power properties, one-sided test, and comparison under the generalized empirical likelihood-based measure of fit are also investigated. A simulation study illustrates that our approximate test is more powerful than the Rivers–Vuong test.     

On Bahadur efficiency of empirical likelihood

This paper studies the Bahadur efficiency of empirical likelihood for testing moment condition models. It is shown that under mild regularity conditions, the empirical likelihood overidentifying restriction test is Bahadur efficient, i.e., its p$p$-value attains the fastest convergence rate under each fixed alternative hypothesis. Analogous results are derived for parameter hypothesis testing and set inference problems.     

Hodges–Lehmann optimality for testing moment conditions

This paper studies the Hodges and Lehmann (1956) optimality of tests in a general setup. The tests are compared by the exponential rates of growth to one of the power functions evaluated at a fixed alternative while keeping the asymptotic sizes bounded by some constant. We present two sets of sufficient conditions for a test to be Hodges–Lehmann optimal. These new conditions extend the scope of the Hodges–Lehmann optimality analysis to setups that cannot be covered by other conditions in the literature. The general result is illustrated by our applications of interest: testing for moment conditions and overidentifying restrictions. In particular, we show that (i) the empirical likelihood test does not necessarily satisfy existing conditions for optimality but does satisfy our new conditions; and (ii) the generalized method of moments (GMM) test and the generalized empirical likelihood (GEL) tests are Hodges–Lehmann optimal under mild primitive conditions. These results support the belief that the Hodges–Lehmann optimality is a weak asymptotic requirement.