Event Title

Seasonal Trends in Lake County Sales Tax

Graduation Year

2014

Location

Room E102, Center for Natural Sciences, Illinois Wesleyan University

Start Date

20-4-2013 10:00 AM

End Date

20-4-2013 11:00 AM

Description

This paper studies Lake County, Illinois’s sales tax receipts that were collected by the Illinois Department of Revenue. The data sample is collected monthly from July 2004 to December 2012, with 102 observations. This study fits an ARMA (p,q) model to the data in order to conduct short term forecasting for the series. Preliminary analysis shows the presence of 3 trends, the first one being positive from the beginning of the data set to February 2007, the second inflection point occurs until May 2005, and the final trend is positive again. In addition, the data also demonstrates the presence of seasonal patterns that occur annually in March. I used the ADF and KPSS test to identify the order of integration of the data set. The number of times a series needs to be differentiated is used to find the order of integration, used to determine an effective OLS regression. After calculating this, I have determined that the series has an order of integration of I(1).

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Apr 20th, 10:00 AM Apr 20th, 11:00 AM

Seasonal Trends in Lake County Sales Tax

Room E102, Center for Natural Sciences, Illinois Wesleyan University

This paper studies Lake County, Illinois’s sales tax receipts that were collected by the Illinois Department of Revenue. The data sample is collected monthly from July 2004 to December 2012, with 102 observations. This study fits an ARMA (p,q) model to the data in order to conduct short term forecasting for the series. Preliminary analysis shows the presence of 3 trends, the first one being positive from the beginning of the data set to February 2007, the second inflection point occurs until May 2005, and the final trend is positive again. In addition, the data also demonstrates the presence of seasonal patterns that occur annually in March. I used the ADF and KPSS test to identify the order of integration of the data set. The number of times a series needs to be differentiated is used to find the order of integration, used to determine an effective OLS regression. After calculating this, I have determined that the series has an order of integration of I(1).