Quasi Empirical Likelihood Estimation of Moment Condition Models

In this paper, I develop a quasi empirical likelihood estimator that has good finite-sample properties when there are many moment conditions. I show that the quasi empirical likelihood estimator, which uses semiparametric efficient estimation, is an approximation to the empirical likelihood estimator, which has been shown to have good statistical properties. The quasi empirical likelihood estimator is a consistent estimator and has a normal asymptotic distribution. As with the full-blown empirical likelihood estimator, the quasi empirical likelihood estimator reduces finite-sample bias, but is much simpler to compute than the empirical likelihood estimator. Monte Carlo experiments and a quick validation exercise confirm my theoretical resultsGMM, empirical likelihood, finite-sample bias, instrumental variables

EXOGENOUS PRODUCTION SHOCKS AND TECHNICAL EFFICIENCY AMONG TRADITIONAL IVORIEN RICE FARMERS

This paper uses a unique panel data set and data envelopment analysis (DEA) to obtain estimates of technical efficiency for 492 traditional rice plots in Côte d'Ivoire. The objective of this paper is to explore the importance of explicitly controlling for exogenous shocks to production in technical efficiency estimation. We show how omission of such variables in highly stochastic production environments can lead to serious inferential errors, with potentially significant policy implications. Conventional DEA estimation of a production frontier, followed by second-stage Tobit estimation of the correlates of plot- level technical efficiency, suggest widespread and substantial inefficiency related to crop fragmentation and seed varieties. However, when one controls for unobserved groupwise cross-sectional and intertemporal heterogeneity and introduces measurable exogenous shocks into the second-stage estimation, managerial characteristics become jointly insignificant and state-conditional technical efficiency becomes nearly universal. The implication is that conventional technical efficiency estimates that refute the classic Schultzian "poor but efficient" hypothesis may be incorrect because they ignore farmers' vulnerability to adverse states of nature against which they cannot insure.Africa (Sub-Saharan), Ivory Coast, production frontiers, agricultural productivity, rice., Crop Production/Industries, Productivity Analysis, O12, Q12, D2,

MACROECONOMIC SHOCKS, HUMAN CAPITAL AND PRODUCTIVE EFFICIENCY: EVIDENCE FROM WEST AFRICAN FARMERS

Little empirical work has quantified the transitory effects of macroeconomic shocks on farm-level production behavior. We develop a simple analytical model to explain how macroeconomic shocks might temporarily divert managerial attention, thereby affecting farm-level productivity, but perhaps to different degrees and for different durations across production units. We then successfully test hypotheses from that model using panel data bracketing massive currency devaluation in the west African nation of Cote d'Ivoire. We find a transitory increase in mean plot-level technical inefficiency among Ivorien rice producers and considerable variation in the magnitude and persistence of this effect, attributable largely to ex ante complexity of operations, and the educational attainment and off-farm employment status of the plot manager.Labor and Human Capital, O1, Q12, Q18,

Making Sense of the Subprime Crisis

macroeconomics, subprime crisis, foreclosures, market, risk factors

Making sense of the subprime crisis

This paper explores the question of whether market participants could have or should have anticipated the large increase in foreclosures that occurred in 2007 and 2008. Most of these foreclosures stemmed from loans originated in 2005 and 2006, leading many to suspect that lenders originated a large volume of extremely risky loans during this period. However, the authors show that while loans originated in this period did carry extra risk factors, particularly increased leverage, underwriting standards alone cannot explain the dramatic rise in foreclosures. Focusing on the role of house prices, the authors ask whether market participants underestimated the likelihood of a fall in house prices or the sensitivity of foreclosures to house prices. The authors show that, given available data, market participants should have been able to understand that a significant fall in prices would cause a large increase in foreclosures although loan-level (as opposed to ownership-level) models would have predicted a smaller rise than actually occurred. Examining analyst reports and other contemporary discussions of the mortgage market to see what market participants thought would happen, the authors find that analysts, on the whole, understood that a fall in prices would have disastrous consequences for the market but assigned a low probability to such an outcome.Subprime mortgage

FHA, Fannie Mae, Freddie Mac, and the Great Recession

Part of the Finance and Economics Discussion Series

The jumbo-conforming spread: a semiparametric approach

This paper estimates the jumbo-conforming spread using data from the Federal Housing Finance Board’s Monthly Interest Rate Survey from January 1993 to June 2007. Importantly, this paper augments the typical parametric approach by adding state-level foreclosure laws and ZIP-level demographic variables to the model, estimating the effects of loan size and loan-to-value ratio on mortgage rates nonparametrically, and including geographic location as a control for some potentially unobserved borrower and market characteristics that might vary over geography, such as credit scores, debt-to-income ratios, and house price volatility. A partial locallinear regression approach is used to estimate the jumbo-conforming spread, on the premise that loans similar to each other in terms of loan size, loan-to-value ratio, or geographic location might also be similar in other, unobservable borrower and market characteristics. I find estimates of the jumbo-conforming spread of 13 to 24 basis points—50 to 24 percent smaller since about 1996, when credit scores became widely used in mortgage underwriting, than estimates from a commonly used parametric model. I therefore attribute the difference in estimates to credit quality and other unobserved characteristics, among other potential explanations, making these controls an important issue in estimating the jumbo-conforming spread.Mortgages