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Presence and Strength of Seasonality in CPI (IW)
Background/Objectives: In official statistics, seasonal adjustment technique is usually employed to explore the seasonality characteristic of a time series. This paper attempts to analyse the seasonality characteristics of Consumer Price Index Numbers for Industrial Workers (CPI-IW base 2001=100). This is commonly used for calculation of inflation and dearness allowance fixation of government employees as well as industrial workers all around the country.
Method/Statistical Analysis: United States’ Bureau of the Census developed a model called X-12 ARIMA intended to explore seasonality of a time series. This econometric model helps the economist to determine the presence and strength of seasonality of a time series.
Findings: The results of X-12 ARIMA indicate that the Consumer Price Index Numbers for Industrial Workers (CPI-IW base 2001=100) contains identifiable seasonality, whereas the series have no indication of substantial moving seasonality.
Improvement/ Applications: To study the underlying trends of the time series, one can make use of the model’s output such as seasonally adjusted series, trend cycle, etc. to explore the underlying developments in the economy in a different perspective. Another scope of this study is to explore the monthly growth rate of original series as well as component series. Hence, enable one to study and understand short span of series, detect change in trend, etc.
Keywords
- Seasonal adjustment of economic time series, Singapore Department of Statistics. https://www.singstat.gov.sg/-/media/files/publications/reference/ip-e32.pdf. Date accessed: 09/11/2006.
- Manual on financial and banking statistics. https://rbi.org.in/scripts/AnnualPublications.aspx?head=Manual%20on%20Financial%20and%20Banking%20Statistics%20-%20March%202007. Date accessed: 01/08/2007.
- C.C.H. Hood, K.M. McDonald-Johnson. Getting started with X-12-ARIMA diagnostics. Catherine Hood Consulting. 2009; 1-15.
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- A. Bloem, R. Dippelsman, N. Maehle. Seasonal adjustment and estimation of trend-cycles. International Monetary Fund, Quarterly National Accounts Manual—Concepts, Data Sources, and Compilation. 2001; 125-146.
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