Showing papers in "Journal of Econometrics in 2009"

TL;DR: Durlauf et al. as discussed by the authors considered an extended version of the linear-in-means model where interactions are structured through a social network and provided easy-to-check necessary and sufficient conditions for identification of peer effects.

TL;DR: In this article, the authors proposed a test procedure that allows a break under both the null and alternative hypotheses and, when a break is present, the limit distribution of the test is the same as in the case of a known break date, thereby allowing increased power while maintaining the correct size.

TL;DR: The Wishart Autoregressive (WAR) process as discussed by the authors is a dynamic model for time series of multivariate stochastic volatility, which naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form nonlinear forecasts.

TL;DR: In this article, two iterative procedures are proposed to jointly estimate the slope parameters and the stochastic trends, and the resulting estimators are referred to respectively as CupBC (continuously updated and bias-corrected) and CupFM (continuous-updated and fully modified) estimators.

TL;DR: In this article, a new concept of Granger causality in risk is introduced and a class of kernel-based tests are proposed to detect extreme downside risk spillover between financial markets, where risk is measured by the left tail of the distribution or equivalently by the Value at Risk (VaR).

TL;DR: In this article, a stochastic cointegration model for quantile regression with cointegrated time series is proposed, where the value of cointegrating co-efficients may be aected by the shocks and thus may vary over the innovation quantile.

TL;DR: In this paper, the authors proposed a new testing procedure for detecting error cross section dependence after estimating a linear dynamic panel data model with regressors using the generalised method of moments (GMM).

TL;DR: In this paper, a data-driven Box-Pierce test for serial correlation is proposed, which automatically chooses the number of autocorrelationships to be tested, and its asymptotic null distribution is chi-square with one degree of freedom.

TL;DR: In this article, a general density function based on the maximum entropy (ME) approach was proposed to model the asymmetry in financial data, taking account of asymmetry, excess kurtosis, and high peakedness.

TL;DR: The authors developed an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data.

TL;DR: In this article, the leading term of a large-T expansion of the bias of the MLE and estimators of average marginal effects in parametric fixed effects panel binary choice models is characterized.

TL;DR: The paper establishes a CLT, ULLN, and LLN for spatial processes or random fields that should be applicable to a broad range of data processes.

TL;DR: In this article, a local linear fitting scheme is developed to estimate the coefficient functions and the asymptotic distributions of the estimators are obtained, showing different convergence rates for the stationary and non-stationary covariates.

TL;DR: In this article, the authors apply the minimax regret criterion to choice between two treatments conditional on observation of a finite sample, and find that the treatment rules are well approximated by empirical success rules in many cases, but differ from them significantly in terms of how the rules look and in the maximal regret incurred.

TL;DR: In this paper, a copula-based multivariate generalized autoregressive conditional heteroscedasticity model with uncorrelated dependent errors is proposed for non-elliptically distributed financial returns, which are generated through a linear combination of dependent random variables.

TL;DR: In this article, the authors proposed the Expectile-Based Value at Risk (EVaR) measure, which is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR), and derived an encompassing test for non-nested expectile models.

TL;DR: In this paper, a parametric bootstrap procedure is proposed to correct bias for general diffusion processes with a theoretical justification, which can effectively reduce both the bias and the mean square error of parameter estimates, for both univariate and multivariate processes.

TL;DR: In this paper, the authors considered semiparametric efficient estimation of conditional moment models with nonsmooth residuals in unknown parametric components and unknown functions of endogenous variables, and showed that the penalized sieve minimum distance (PSMD) estimator can simultaneously achieve root-n asymptotic normality of and nonparametric optimal convergence rate.

TL;DR: The authors proposed a test for the slope of a trend function when it is a priori unknown whether the series is trend-stationary or contains an autoregressive unit root, based on a Feasible Quasi Generalized Least Squares method.

TL;DR: In this article, an approximation for the estimator of the Gini index is derived by expressing it as a sum of IID random variables, and a simple butective bias correction is also derived.

TL;DR: In this article, the authors extend the unit root tests based on quantile regression to allow stationary covariates and a linear time trend, and apply it to the real exchange rates of four different countries.

TL;DR: In this paper, two types of stochastic correlation structures for multivariate Stochastic Volatility (MSV) models, namely the constant correlation (CC) MSV and dynamic correlation (DC)MSV models, are proposed.

TL;DR: In this article, the authors consider the problem of testing for equality of two density or two conditional density functions defined over mixed discrete and continuous variables, with the smoothing parameters chosen via least-squares cross-validation.

TL;DR: In this paper, the authors show that statistical inference on the risk premia in linear factor models that is based on the Fama-MacBeth (FM) and generalized least squares (GLS) two-pass risk preia estimators is misleading when the β ’s are small and/or the number of assets is large.

TL;DR: In this article, the authors extended the transformed data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan (1994) to account for the fact that observed equity prices may have been contaminated by trading noises.

TL;DR: The Asymmetric Exponential Power Distribution (AEPD) as discussed by the authors generalizes the class of SEPD in a way that in addition to skewness introduces different decay rates of density in the left and right tails.

TL;DR: This article proposed a quantile minimum distance estimator which converges at the parametric rate to the parameter vector of interest, and has an asymptotically normal distribution, which obviates the need for nonparametric estimation.

TL;DR: In this paper, a method of inference for general stochastic volatility models containing price jumps is proposed, which is based on treating realized multipower variation statistics calculated from high-frequency data as their unobservable (fill-in) asymptotic limits.

TL;DR: Heckman and Vytlacil as mentioned in this paper extended the method of local instrumental variables to the estimation of not only means, but also distributions of potential outcomes using data from the National Longitudinal Survey of Youth of 1979.

TL;DR: In this article, a time-varying quantile can be fitted by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm.