Graphical diagnostics of endogeneity

We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. An interesting case arises with a continuous or ordered (e.g., years of schooling) endogenous variable. Then, a graphical test for misspecification due to endogeneity (e.g., self selection) can be obtained without instrumental variables. Moreover, the sign of the bias implied by this endogeneity becomes deducible through such graphs.CUSUM plot; Recursive residuals; Return to schooling; Self selection

Smoothing-inspired lack-of-fit tests based on ranks

A rank-based test of the null hypothesis that a regressor has no effect on a response variable is proposed and analyzed. This test is identical in structure to the order selection test but with the raw data replaced by ranks. The test is nonparametric in that it is consistent against virtually any smooth alternative, and is completely distribution free for all sample sizes. The asymptotic distribution of the rank-based order selection statistic is obtained and seen to be the same as that of its raw data counterpart. Exact small sample critical values of the test statistic are provided as well. It is shown that the Pitman-Noether efficiency of the proposed rank test compares very favorably with that of the order selection test. In fact, their asymptotic relative efficiency is identical to that of the Wilcoxon signed rank and $t$-tests. An example involving microarray data illustrates the usefulness of the rank test in practice.Comment: Published in at http://dx.doi.org/10.1214/193940307000000103 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Technology persistence and monetary policy

In this paper, by using several statistical tools, we provide evidence of increased persistence of the U.S. total factor productivity. In a forward-looking model, agentsí optimal behavior depends on the autocorrelation structure of the exogenous shocks. Since many monetary models are driven by exogenous technology shocks, we study the implications of a change in technology persistence on monetary policy using a New Keynesian framework. First, we analytically derive the interaction between the TFP persistence, monetary policy parameters, and output gap and ináation. Second, we show that change in the TFP persistence a§ects the optimal behavior of monetary policy

TFP persistence and monetary policy

In this paper, by using several statistical tools, we provide evidence of an increase in the persistence of the U.S. total factor productivity. In a forward looking model, agents' optimal behavior depends on the autocorrelation structure of the exogenous shocks. Since many monetary models are driven by exogenous TFP shocks, we study the interaction between monetary policy and TFP persistence. Considering a New-Keynesian model, first, we analytically derive the interaction between the TFP persistence, monetary policy parameters, and output gap and inflation. Second, we show that TFP persistence affects the optimal behavior of the monetary policy

Copula-based nonlinear quantile autoregression

Parametric copulas are shown to be attractive devices for specifying quantile autoregressive models for nonlinear time-series. Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches. Consistency and asymptotic normality of the proposed quantile estimators are established under mild conditions, allowing for global misspecification of parametric copulas and marginals, and without assuming any mixing rate condition. These results lead to a general framework for inference and model specification testing of extreme conditional value-at-risk for financial time series data.

Nonparametric estimation of concave production technologies by entropic methods

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, bases on the priciple of maximum likelihood, uses entropic distance and concvex programming techniques to estimate production functions.convex programming, production functions, entropy

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 6, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory, edited by Gregory Long and Alok Gupta