The basic ideas underlying the construction of a newly introduced seasonal adjustment procedure by a Bayesian modeling are discussed in detail. Particular emphasis is placed on the use of the concept of the likelihood of a Bayesian model for model selection. The performance of the procedure is illustrated by a numerical example.

Seasonal adjustment by a bayesian modeling

http://darp.lse.ac.uk/PapersDB/Conway-Kimmel_(LabEc_98).pdf

Male labor supply estimates and the decision to moonlight

This paper examines the incidence and reasons for moonlighting with a focus on gender differences. The study specifies and estimates a bivariate probit model of labour supply and the decision to hold more than one job. It is found that the factors leading men and women to moonlight are similar. A wage decomposition analysis reveals that 93 percent of the differential between male and female moonlighters' wages is not explained by differences in characteristics, and little connection is found between an individual's human capital and their moonlighting wage. Finally, it is found that moonlighters are less likely to report their income.

Moonlighting: multiple motives and gender differences

Moonlighting workers represent a significant and growing percentage of the labor force, yet few studies examine the reasons for and economic consequences of this decision. Possible motives for moonlighting include the inability to work sufficient hours on the primary job and the desire for a nonpecuniary aspect of the second job that is unavailable on the primary job. Using data from the SIPP, we present descriptive and econometric evidence on moonlighting behavior in an attempt to reveal who moonlights and why. Our results suggest that both motives are present, with constraints on the primary job being the more common one. In addition, we find that the typical moonlighter remains somewhat poorer than the average worker despite working full time on his primary job and part time on a lower-paying second job.

/pdf/who-moonlights-and-why-evidence-from-the-sipp-5gcpe2xxk6.pdf

Who Moonlights and Why? Evidence from the SIPP

A maximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelve-step-ahead prediction models can be different. The different models exhibit the relative optimality properties for which they were designed. The results are related to open questions on optimal trend estimation and optimal seasonal adjustment of time series.

The Prediction of Time Series With Trends and Seasonalities

The measurement and significance of multiple jobholding in the United States

Seasonal Adjustment of the U.S. Unemployment Rate

Seasonal Adjustment of the U.S. Unemployment Rate: Introductory Remarks

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The measurement and significance of multiple jobholding in the United States

Seasonal Adjustment of the U.S. Unemployment Rate

Seasonal Adjustment of the U.S. Unemployment Rate: Introductory Remarks