Unaliasing of aliased line component frequencies

This paper is concerned with undoing aliasing effects, which arise from discretely sampling a continuous‐time stochastic process. Such effects are manifested in the frequency‐domain relationships between the sampled and original processes. The authors describe a general technique to undo aliasing effects, given two processes, one being a time‐delayed version of the other. The technique is based on the observations that certain phase information between the two processes is unaffected by sampling, is completely determined by the (known) time delay, and contains sufficient information to undo aliasing effects. The authors illustrate their technique with a simulation example. The theoretical model is motivated by the helioseismological problem of determining modes of solar pressure waves. The authors apply their technique to solar radio data, and conclude that certain low‐frequency modes known in the helioseismology literature are likely the result of aliasing effects. The Canadian Journal of Statistics 38: 116–135; 2010 © 2010 Statistical Society of Canada     

Estimating the Continuous-Time Consumption-Based Asset-Pricing Model

The consumption-based asset-pricing model predicts that excess yields are determined by the market's degree of relative risk aversion and by the covariances of per capita consumption growth with asset returns. Estimation and testing are complicated by the fact that the model's predictions relate to the instantaneous flow of consumption and point-in-time asset values, but only data on the integral or time average of the consumption flow are available. This article shows how to take account of the effects of time averaging on the covariances. We estimate the model's parameters and test the overidentifying restrictions using six different data sets.     

On the total sojourn time of particles in semi-markov compartmental system with branching particles

We consider a semi-Markov compartroental system with branching particles, Me study the total sojourn time of all the particles and obtain the asymptotic behaviour of its mean. We also discuss some special systems.     

Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors

Motivated by the need to assess the significance of the trend in some macroeconomic series, this article considers inference of a parameter in parametric trend functions when the errors exhibit certain degrees of nonstationarity with changing unconditional variances. We adopt the recently developed self-normalized approach to avoid the difficulty involved in the estimation of the asymptotic variance of the ordinary least-square estimator. The limiting distribution of the self-normalized quantity is nonpivotal but can be consistently approximated by using the wild bootstrap, which is not consistent in general without studentization. Numerical simulation demonstrates favorable coverage properties of the proposed method in comparison with alternative ones. The U.S. nominal wages series is analyzed to illustrate the finite sample performance. Some technical details are included in the online supplemental material.     

Parallel tempering for dynamic generalized linear models

ABSTRACT

Markov chain Monte Carlo (MCMC) methods can be used for statistical inference. The methods are time-consuming due to time-vary. To resolve these problems, parallel tempering (PT), as a parallel MCMC method, is tried, for dynamic generalized linear models (DGLMs), as well as the several optimal properties of our proposed method. In PT, two or more samples are drawn at the same time, and samples can exchange information with each other. We also present some simulations of the DGLMs in the case and provide two applications of Poisson-type DGLMs in financial research.     

Estimating systems of trending variables

A survey is given of some results on inference in cointegrated systems. We discuss some regression methods, and contrast them with the analysis of the vector autoregressive model. We discuss determination of cointegrating rank and estimation of parameters, as well as asymptotic inference. The problems are treated for 1(1) and for 1(2) variables.     

Unobserved-Components Models for Seasonal Adjustment Filters

Time series models are presented, for which the seasonal-component estimates delivered by linear least squares signal extraction closely approximate those of the standard option of the widely-used Census X-11 program. Earlier work is extended by consideration of a broader class of models and by examination of asymmetric filters, in addition to the symmetric filter implicit in the adjustment of historical data. Various criteria that guide the specification of unobserved- components models are discussed, and a new preferred model is presented. Some nonstandard options in X-11 are considered in the Appendix.     

Discount-neutral utility models for denumerable time streams

This paper formulates and axiomatizes utility models for denumerable time streams that make no commitment in regard to discounting future outcomes. The models address decision under certainty and decision under risk. Independence assumptions in both contexts lead to additive or multiplicative utilities over time periods that allow unambiguous comparisons of the relative importance of different periods. The models accommodate all patterns of future valuation. This discount-neutral feature is attained by restricting preference comparisons to outcome streams or probability distributions on outcome streams that differ in at most a finite number of periods.     

Children's time in structured and unstructured leisure activities

Using the 1981 Time Use Longitudinal Panel Study data, this study examines factors which influence children's time use in leisure activities. More time is spent in unstructured than structured leisure activities. Analysis of variance is performed on 32 categories of leisure time, including total leisure, to determine the influence of characteristics of the child and the mother and other socioeconomic characteristics. Males spend significantly more time than females in active sports, playing games, and passive leisure especially watching television on weekends. Females spend significantly more time than males socializing on weekends. The amount of time spent socializing increases with age but decreases with employment. Time spent playing games decreases with age, increases with number of children, and decreases as family income increases. Given the large number of children who do not participate in a given set of leisure activities, future research should examine the probability of participation.The data used in this article were made available by the Inter-university Consortium for Political and Social Research. The data for Time Use Longitudinal Panel Study, 1975–1981 were originally collected by F. Thomas Juster, Martha S. Hill, Frank P. Stafford, and Jacquelynne Eccles Parsons of the Survey Research Center, Institute for Social Research, The University of Michigan. Neither the collector of the original data nor the Consortium bear any responsibility for the analysis or interpretation presented here.Carol B. Meeks is a Professor and Teresa Mauldin is an Assistant Professor in the Department of Housing and Consumer Economics, The University of Georgia, Athens, GA 30602. Dr. Meeks' research interests include time use, family structure, and economic aspects of housing. She received her Ph.D. from Ohio State University. Dr. Mauldin, who also received her Ph.D. from Ohio State University, is interested in time use, economic well being of individuals and families, and family structure.