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Activity Number: 507 - Economic Forecasts and Macro Modeling
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #323744 View Presentation
Title: A Composite Economic Index for the United States' Economy: What Does This Index Tell Us?
Author(s): Brian Sloboda* and Chandrasekhar Putcha
Companies: University of Phoenix, Center for Management and Entrepreneurship (CME) and California State University at Fullerton
Keywords: Composite Index ; Macroeconomic data ; time series

This paper develops a method for calculating composite economic index (CEI) for the United States economy. The key variables for the estimation of the CEI are as follows: Gross National Product per year (GNPPY), Housing Prices Per Year (HPPY), Unemployment Rate per year (URPY), Average Income per year (AIPY). These are all dependent variables while Year is independent variable. However, first 5 years data is studied to see the behavior of these variables. Looking at the data, first it is determined what kind of a function would fit the data (linear, non-linear, piece-wise linear, equivalent linear, linear spline, quadratic spline etc.). Then, the actual regression analysis is performed to get the actual mathematical function to fit the data. These are then checked for adequacy by the two standard indicators: the correlation coefficients and the standard error. The behavior will be similar while the actual coefficients (depending on what kind of function is fitted like linear vs. polynomial) will be different. Using the slope(s) from the estimated equations, the composite economic index (CEI) is developed. These results should be useful to researchers.

Authors who are presenting talks have a * after their name.

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