Economics ETDs

Publication Date

6-28-1972

Abstract

The research work described in this thesis constitutes a methodology for deriving short-term economic time series indicators at the state level. A brief discussion of the need for such indicators can be found in Chapter I. Following this chapter, alternative representative series of the level of economic activity are considered. Reported wages and salaries, the dependent variable used in this research work, is discussed in relation to other potential representative series such as Personal Income, Per Capita Income, and Gross State Product.

Criteria for choosing economic time series is listed in Chapter III along with titles and comments about the 27 independent time series used in this thesis. Time series component descriptions and individual component effects are briefly discussed in order to provide the reader some insight as to why superficial examination of time series can be misleading. Seasonal and trend component removal is accomplished on the times series used in the thesis, but only after these components are tested for their respective effects. Leading, coincident,and lagged time series are determined from the original group of 27 independent time series by the method of choosing the highest correlation coefficient attained in a given time period, lead or lag. The chosen leading, coincident, and lagged series are then tested by a regression analysis for their ability to explain variation in the dependent variable. The end result of the thesis is a method by which short-term forecasts (one or two quarters in advance), coincident estimating, and lagged analysis of the level of economic activity can be accomplished through a time series representing economic activity.

Degree Name

Economics

Level of Degree

Masters

Department Name

Department of Economics

First Committee Member (Chair)

Shaul Ben-David

Second Committee Member

Gerald Joseph Boyle

Third Committee Member

Lee B Zink

Document Type

Thesis

Included in

Economics Commons

Share

COinS