Estimated Variance of Seasonally Adjusted Series

ESTABLISHMENT DATA STATE EMPLOYMENT SEASONALLY ADJUSTED Table D-1. Employees on nonfarm payrolls by state and major industry, seasonally adjusted

TOTAL Alabama. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,909.3 1,911.5 1,916.9 1,918.1 1,918.9 1,922.7 1,925.9 1,927.2 1,937.8 1,940.6 1,942.8 1,945.7 1,944.5 Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337.0 337.1 338.4 338.0 337.1 337.8 337.0 336.3 337.2 338.1 340.6 341.4 340.3 Arizona. . . . . . . . . . . . . . . . . . . . ....

ESTABLISHMENT DATA STATE EMPLOYMENT SEASONALLY ADJUSTED Table D-1. Employees on nonfarm payrolls by state and major industry, seasonally adjusted

TOTAL Alabama. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,916.6 1,911.1 1,912.5 1,912.2 1,913.7 1,913.3 1,912.9 1,918.1 1,927.8 1,940.2 1,940.5 1,947.2 1,948.2 Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338.8 338.0 335.8 336.4 338.8 339.5 333.4 332.9 333.1 335.1 335.8 338.4 340.9 Arizona. . . . . . . . . . . . . . . . . . . . ....

Variance Adjusted Actor Critic Algorithms

We present an actor-critic framework for MDPs where the objective is the variance-adjusted expected return. Our critic uses linear function approximation, and we extend the concept of compatible features to the variance-adjusted setting. We present an episodic actor-critic algorithm and show that it converges almost surely to a locally optimal point of the objective function. Index Terms Reinfo...

Constructing seasonally adjusted data with time-varying con dence intervals

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide con dence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time...

Parameter estimation and naïve bias for a seasonally adjusted aggregate series of different lengths using univariate and multivariate approaches