On Conditional Density Estimation

With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so-called re-weighted Nadaraya-Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well-known Nadaraya-Watson smoother. With a detailed asymptotic analysis, we demonstrate that the RNW smoother preserves the superior large-sample bias property of the local linear smoother of the conditional density recently proposed in the literature. As a matter of independent statistical interest, the limit distribution of the RNW estimator is also derived.     

Policies to promote cereal intensification in Ethiopia: The search for appropriate public and private roles

Over the past decade, policymakers have been searching for an appropriate blend of public and private sector roles to accelerate the intensification of food staple production, smallholder commercialization, and sustainable market development in sub-Saharan Africa. In Ethiopia, steps taken to liberalize markets in the 1990s and promote fertilizer and seed packages have yet to generate payoffs in terms of higher cereal yields, lower food prices, or reduced dependency on food aid. This raises concern about the performance of the agricultural sector, specifically in terms of the systems for providing improved seed, fertilizer, credit, and extension services. This paper examines the evolving roles of the public and private sectors in intensifying cereal production in Ethiopia. Findings suggest that while Ethiopia has an admirable record of supporting agriculture, its state-led policies has now outlived their usefulness. These findings for Ethiopia offer lessons that are potentially applicable to other sub-Saharan African countries facing similar challenges.     

Asymmetric Quantile Persistence and Predictability: the Case of US Inflation

This article investigates the evidence of time‐variation and asymmetry in the persistence of US inflation. We compare the out‐of‐sample performance of different forecasting models and find that quantile forecasts from an Auto‐Regressive (AR) model with level‐dependent volatility are at least as accurate as the forecasts of the Quantile Auto‐Regressive model, in particular for the core inflation measures. Our results indicate that the persistence of core inflation has been relatively constant and high, but it declined for the headline inflation measures. We also find that the asymmetric persistence of inflation shocks can be mostly attributed to the positive relation between inflation level and its volatility.     

A bootstrap-based non-parametric forecast density

Interest in density forecasts (as opposed to solely modeling the conditional mean) arises from the possibility of dynamics in higher moments of a time series, as well as in forecasting the probability of future events in some applications. By combining the idea of Markov bootstrapping with that of kernel density estimation, this paper presents a simple non-parametric method for estimating out-of-sample multi-step density forecasts. The paper also considers a host of evaluation tests for examining the dynamic misspecification of estimated density forecasts by targeting autocorrelation, heteroskedasticity and neglected non-linearity. These tests are useful, as a rejection of the tests gives insight into ways to improve a particular forecasting model. In an extensive Monte Carlo analysis involving a range of commonly used linear and non-linear time series processes, the non-parametric method is shown to work reasonably well across the simulated models for a suitable choice of the bandwidth (smoothing parameter). Furthermore, an application of the method to the U.S. Industrial Production series provides multi-step density forecasts that show no sign of dynamic misspecification.     

Food versus fuel: examining tradeoffs in the allocation of biomass energy sources to domestic and productive uses in Ethiopia

Rural households in Ethiopia have limited options to meet their domestic energy needs because they lack access to modern fuels and technologies. Domestic use of certain fuel sources, such as cow dung, can hinder agricultural outcomes and productivity. This article explores the tradeoffs between domestic and productive uses of biomass energy sources in the Nile Basin of Ethiopia using a nonseparable farm household model where labor allocation to energy collection and farming are analyzed simultaneously. We estimate a system of five structural equations using three‐stage least squares and find that the use of dung as a domestic fuel source has negative implications for the value of harvested crops, while use of on‐farm fuelwood is associated with increased value of agricultural output. On‐farm production of fuelwood appears to increase the value of crop output and provide labor savings, by making fuelwood collection more convenient for households. Policy interventions to support the expansion of agroforestry and increase access to new energy‐efficient technologies are needed to ensure that agricultural productivity can be both increased and sustained.