Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity

This paper investigates identification and inference in a nonparametric structural model with instrumental variables and non-additive errors. We allow for non-additive errors because the unobserved heterogeneity in marginal returns that often motivates concerns about endogeneity of choices requires objective functions that are non-additive in observed and unobserved components. We formulate several independence and monotonicity conditions that are sufficient for identification of a number of objects of interest, including the average conditional response, the average structural function, as well as the full structural response function. For inference we propose a two-step series estimator. The first step consists of estimating the conditional distribution of the endogenous regressor given the instrument. In the second step the estimated conditional distribution function is used as a regressor in a nonlinear control function approach. We establish rates of convergence, asymptotic normality, and give a consistent asymptotic variance estimator.

Efficient Bias Correction for Cross-section and Panel Data

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher-order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split-sample jackknife, result in larger higher-order variances in the i.i.d. setting we focus on. For both a cross-sectional MLE and a panel model with individual fixed effects, we show that the split-sample jackknife has a higher-order variance term that is twice as large as that of the `leave-one-out' jackknife

A New Robust and Most Powerful Test in the Presence of Local Misspeci cation

This paper proposes a new test that is consistent, achieves correct asymptotic size and is locally most powerful under local misspecification, and when any square-root-of-n-estimator of the nuisance parameters is used. The new test can be seen as an extension of the Bera and Yoon (1993) procedure that deals with non-ML estimation, while preserving its optimality properties. Similarly, the proposed test extends Neyman's (1959) C(a) test to handle locally misspecified alternatives. A Monte Carlo study investigates the finite sample performance in terms of size, power and robustness to misspecification

Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73839/1/j.1468-0262.2006.00696.x.pd

Combination Forecasts of Bond and Stock Returns: An Asset Allocation Perspective

We investigate the out-of-sample forecasting ability of the HML, SMB, momentum, short-term and long-term reversal factors along with their size and value decompositions on U.S. bond and stock returns for a variety of horizons ranging from the short run (1 month) to the long run (2 years). Our findings suggest that these factors contain significantly more information for future bond and stock market returns than the typically employed financial variables. Combination of forecasts of the empirical factors turns out to be particularly successful, especially from an an asset allocation perspective. Similar findings pertain to the European and Japanese markets

Collateral Quality and Loan Default Risk: The Case of Vietnam

In the transition economy of Vietnam, financial market is dominated by banking sector but commercial banks heavily rely on collateral-based lending. While the relationship between collateral and implied credit risk is still in debate, this paper provides additional empirical evidence regarding the heterogeneous effects and transmission channels of collateral characteristics on loan delinquency. Applying instrumental variable probit analysis on a unique dataset of 2295 internal loan accounts in Vietnam, we find the significantly negative impact of collateral quality on the probability of default of consumer loans, supporting the dominance of borrower selection and risk-shifting over lender selection effects. The finding implies that high-quality collateral not only signals more credible borrower but also fosters good behavior in using loan, enabling bank to mitigate adverse selection and moral hazard problems