Consumer Price Index (CPI) to measure inflation

We use the Consumer Price Index or CPI to measure inflation. It is a fixed-weight price index, which takes the quantities in some base year as being the typical goods bought by the average consumer during that base year, and then uses those quantities (same basket) as weights to calculate the index in each year.

CPI helps calculate the cost of living and the value of money. We can arrive at the inflation rate by calculating the annual percentage change in price levels.

Limitations of CPI

The CPI is not an altogether accurate measure of inflation rates as it tends to inflate it. It has inherent biases, some of which are:

New Goods Bias: If we decide to compare the inflation rates today with those say 20 years back we need to compare similar goods. For instance, if we compare phones, the new touch phones would be more expensive than the old rotary dial phones. This would get factored into the CPI making the current CPI more expensive.

Quality Change Bias: Newer products come in with more technological innovation and are hence more expensive. However, the increase in prices is due to innovation rather than inflation. CPI sees it as inflation and states it such.

Commodity Substitution Bias: When consumers switch from one product to a similar product due to increase in prices, CPI records inflation rates as high in that product. If consumers derive the same amount of satisfaction or utility by the switch this is not factored by CPI. Only the increased usage of the substitute product is documented.

Outlet Substitution Bias: In times of increased prices people shopping from supermarkets may choose to go to discount stores. CPI does not factor in outlet substitutions.

Related Downloads

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.