Sectoral wage convergence: a nonparametric distributional analysis

A nonparametric analysis of the similarity between goods and services wage densities, applying kernel density estimates and an overlap statistic to U.S. weekly full-time wages from 1969 to 1993.Manufactures ; Service industries ; Wages

Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study

Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced

Sectoral wage convergence: a nonparametric distributional analysis

An examination of the relative shapes of the wage distribution in the U.S. goods-producing and service-producing sectors that uses a nonparametric measure of density overlap to analyze wage differences between the two sectors over time. ; What implications do 21st century monetary innovations bring for holdings of central bank money and standards of value? Emerging technologies such as cybercash, e-cash, and smart cards can be expected to reduce demand for central bank money, but the theoretical framework for monetary policy has not changed.Manufactures ; Service industries ; Wages

Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient

We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interval. As a prior on the diffusion coefficient, we employ a histogram-type prior with piecewise constant realisations on bins forming a partition of the time interval. Specifically, these constants are realizations of independent inverse Gamma distributed randoma variables. We justify our approach by deriving the rate at which the corresponding posterior distribution asymptotically concentrates around the data-generating diffusion coefficient. This posterior contraction rate turns out to be optimal for estimation of a H\"older-continuous diffusion coefficient with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to implement, as the posterior distributions turn out to be inverse Gamma again, and leads to good practical results in a wide range of simulation examples. Finally, we apply our method on exchange rate data sets

Conditional variance forecasts for long-term stock returns

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon

A Fourier transform method for nonparametric estimation of multivariate volatility

We provide a nonparametric method for the computation of instantaneous multivariate volatility for continuous semi-martingales, which is based on Fourier analysis. The co-volatility is reconstructed as a stochastic function of time by establishing a connection between the Fourier transform of the prices process and the Fourier transform of the co-volatility process. A nonparametric estimator is derived given a discrete unevenly spaced and asynchronously sampled observations of the asset price processes. The asymptotic properties of the random estimator are studied: namely, consistency in probability uniformly in time and convergence in law to a mixture of Gaussian distributions.Comment: Published in at http://dx.doi.org/10.1214/08-AOS633 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Overlap Package

Camera traps - cameras linked to detectors so that they fire when an animal is present - are a major source of information on the abundance and habitat preferences of rare or shy forest animals. Modern cameras record the time of the photo, and the use of this to investigate diel activity patterns was immediately recognised (Gri?ffiths and van Schaik, 1993). Initially this resulted in broad classfication of taxa as diurnal, nocturnal, crepuscular, or cathemeral (van Schaik and Gri?ths, 1996). More recently, researchers have compared activity patterns among species to see how overlapping patterns may relate to competition or predation (Linkie and Ridout, 2011; Carver et al., 2011; Ramesh et al., 2012; Carter et al., 2012; Kamler et al., 2012; Ross et al., 2013). Ridout and Linkie (2009) presented methods to fit kernel density functions to times of observations of animals and to estimate the coe?cient of overlapping, a quantitative measure ranging from 0 (no overlap) to 1 (identical activity patterns). The code they used forms the basis of the overlap package. Although motivated by the analysis of camera trap data, overlap could be applied to data from other sources such as data loggers, provided data collection is carried out around the clock. Nor is it limited to diel cycles: tidal cycles or seasonal cycles, such as plant flowering or fruiting or animal breeding seasons could also be investigated