Statistics Project

Multiple Regression Analysis: An estimate for a Producer Price Index

1. Statement of the Problem

The purpose of this analysis is to construct a meaningful multiple regression model to estimate the U.S. producer price index (PCI) for finished goods. This is a macroeconomics variable that is recorded monthly by the U.S. Bureau of Labor Statistics, as a part of their inflation and price statistics. The objective of this project is to use other macroeconomics variables to construct a regression model that explains a sizeable amount of the variation exhibited by the PCI variable. There are a myriad of possible selections of variables that could contribute to explain the variation exhibited by PCI, but based on the macroeconomics laws, the following variables could be considered as reasonable candidates to be included in a regression model to predict PCI:

• Unemployment Rate
• Consumer Price Index (CPI)
• Export Index for all commodities (BEA END USE EXPORT INDEXES)

Also, it is not unusual that this kind of time series models will require the use of some kind of lagged variables in case that a problem with autocorrelation exists.

2. Hypotheses

The main objective of the analysis is to find a model of the form

The purpose of the rest of the paper is to validate the above model and make improvements to arrive to the best possible multiple regression model, by removing variable that don’t contribute significantly, or eventually adding a lagged variable in case that the model exhibits any autocorrelation problems.

Price: $31.32

Solution: The downloadable solution consists of 19 pages, 1232 words and 23 charts.
Deliverable: Word Document