Quantile Inferences for Inflation and Its Variability: Does a Threshold Inflation Rate Exist?

Using quantile regressions and cross-sectional data from 152 countries, we examine the relationship between inflation and its variability. We consider two measures of inflation - the mean and median - and three different measures of inflation variability - the standard deviation, coefficient of variation, and median deviation. Using the mean and standard deviation or the median and the median deviation, the results support both the hypothesis that higher inflation creates more inflation variability and that inflation variability raises inflation across quantiles. Moreover, higher quantiles in both cases lead to larger marginal effects of inflation (inflation variability) on inflation variability (inflation). Using the mean and the coefficient of variation, however, the findings largely support no correlation between inflation and its variability. Finally, we also consider whether thresholds for inflation rate or inflation variability exist before finding such positive correlations. We find evidence of thresholds for inflation rates below 3 percent, but mixed results for thresholds for inflation variability.

Efficiency Bounds for Estimating Linear Functionals of Nonparametric Regression Models with Endogenous Regressors

Consider a nonparametric regression model Y=mu*(X) + e, where the explanatory variables X are endogenous and e satisfies the conditional moment restriction E[e|W]=0 w.p.1 for instrumental variables W. It is well known that in these models the structural parameter mu* is 'ill-posed' in the sense that the function mapping the data to mu* is not continuous. In this paper, we derive the efficiency bounds for estimating linear functionals E[p(X)mu*(X)] and int_{supp(X)}p(x)mu*(x)dx, where p is a known weight function and supp(X) the support of X, without assuming mu* to be well-posed or even identified.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.