CHOOSING THE VARIABLES TO ESTIMATE SINGULAR DSGE MODELS

We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second selects the vector that minimizes the informational discrepancy between the singular and non‐singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for applied researchers are provided. Copyright © 2014 John Wiley & Sons, Ltd.     

The formation and evolution of childhood skill acquisition: Evidence from India

Building on recent advances in the literature and using a rich data set for two cohorts of children aged between one and twelve for Andhra Pradesh, India, we investigate the determinants of children's cognitive as well as non-cognitive skills. We find evidence of self-productivity for cognitive skills and cross-productivity effects from cognitive on non-cognitive skills. Moreover, we demonstrate that parental investment has contemporaneously positive effects on skill levels for all age groups. Investigating other determinants of these skills, we find child health at age one to influence cognitive abilities at age five, whilst child health at age one is influenced by parental care already during pregnancy and earliest childhood. Understanding the determinants which account explicitly for the effect of a large number of child, caregiver and household characteristics provides insights with regard to possible policy interventions to improve the chances of children in poor environments of developing cognitive and non-cognitive skills crucial for success in many spheres of life.     

Local versus Global Assessment of Mobility*

Mobility indices are popular tools designed to quantify the extent of income changes by aggregating “local” distributional change into a “global” scalar according to some rule. For some mobility measures, this aggregation rule is only implicit in their standard definition. We derive an insightful approximation to the (statistical) aggregation rule for the important class of mobility indices introduced by Shorrocks (Journal of Economic Theory 19 (1978), 376–93) and further generalized by Maasoumi and Zandvakili (Economic Letters 22 (1986), 97–102), which enables us to characterize their normative properties. We also develop methods for estimation and inference. A substantive empirical contribution emerges from the comparison of mobility between the United States and Germany. Our methods reveal why income mobility is higher in Germany than in the United States: Higher German mobility in the bottom of the distribution is combined with an implicitly higher weighting by the mobility index at the bottom.     

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.     

A Conversation with James O. Ramsay

Jim Ramsay was born on September 5, 1942, in Prince George, British Columbia. He pursued undergraduate studies at the University of Alberta, where he completed a BEd in 1964 with a major in English and a minor in mathematics. He then specialized in statistics and psychometry, earning a PhD in psychology from Princeton University in 1966. After holding a temporary lectureship in the Department of Psychology at University College London for one year, he joined the Department of Psychology at McGill University, where he rose through the academic ranks. He was chair of his department from 1986 to 1989 and spent sabbatical leaves in Cambridge, Grenoble, and Toulouse. He was named professor emeritus upon his retirement in 2007. Jim is the author of four influential books and over 100 peer‐reviewed articles in statistical and psychometric journals. He developed much of the statistical theory behind multidimensional scaling and is widely recognized as the founder of functional data analysis. Three of his papers were read to the Royal Statistical Society, and another won The Canadian Journal of Statistics 2000 Best Paper Award. The Statistical Society of Canada (SSC) awarded him a Gold Medal for research in 1998 and an honorary membership in 2012. Jim was president of the Psychometric Society in 1981–82 and president of the SSC in 2002–03. The following conversation took place at Jim's home in Ottawa, Ontario, on March 14 and April 4, 2012.     

Adverse Selection and Moral Hazard in Equity Partnerships: Evidence from Hollywood's Slate Financing Agreements

We use a movie industry project‐by‐project data set to analyze the principal–agent problem in slate financing arrangements. Under this specific film financing regime, which has become a significant mode of raising capital in Hollywood over the past decade, an external investor concludes a long‐term contract with a film producer and commits to cofinance a larger number of future film projects of that particular partner. In line with our theoretical conjectures, slate cofinanced movies receive poorer quality ratings and yield considerably lower return rates. Our data suggests that a substantial part of these performance differences may be attributed to adverse project selection and producer moral hazard.     

Two-stage non Gaussian QML estimation of GARCH models and testing the efficiency of the Gaussian QMLE

In generalized autoregressive conditional heteroskedastic (GARCH) models, the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption. We show that, for estimating the original specification based on the standard identifiability assumption, efficiency gains can be expected from using a quasi-maximum likelihood (QML) estimator based on a non Gaussian density and a reparameterization based on an alternative identifiability assumption. A test allowing to determine whether a reparameterization is needed, that is, whether the more efficient QMLE is obtained with a non Gaussian density, is proposed.