PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
EXECUTIVE SUMMARY:
This project explores different models designed to predict the future inflation rate based on
historical economic information. Since the dependent variable in the data is a continuous
variable, the model will be run for predictive purposes. We will use predictive multiple linear
regression, predictive generalized linear regression, CHAID tree models, and Neural Networks to
determine the most influential factors of our model and allow us to obtain a minimum 70 percent
accuracy rating. The data was collected from various sources of government and other reliable
websites as well as professional articles, which helped us obtain monthly data of the U.S.
expenditures and economic information from 1976 to 2011. Based on our analysis, we see that
the Consumer Price Index variable and Unemployment Rate variable are the most important
predictive factors aligned with changes in the inflation rate. Our findings suggest that companies
should pay attention to changes in the Consumer Price Index and unemployment rates over time
when organizing their business plan.
INTRODUCTION AND PURPOSE:
The value of the U.S. Dollar is ever changing, whether we as consumers know it or not. Since the
United States left the gold standard in 1932, the relative value of the dollar has been both an
indicator of political and economic stability and a key driver of consumer confidence. The
decreasing value of the dollar (and therefore higher prices of consumer goods) is known as
inflation and the rate at which it fluctuates is known as the inflation rate. While the U.S.
government tries to control inflation via the Federal Reserve, its true value is dictated by the
volatility of the market.
The inflation rate has enormous consequences for the economy and investors. Negative effects of
inflation include a decrease in the real value of money and other monetary items over time;
uncertainty over future inflation may discourage investment and savings; and high inflation may
lead to shortages of goods if consumers begin hoarding out of concern that prices will increase in
the future. Positive effects include ensuring central banks can adjust nominal interest rates and
encouraging investment in non-monetary capital projects.
Companies use inflation rates when planning finances, a key step in optimizing one’s
investments, cost management and financial needs. Thus, knowing which factors affect the
inflation rate in order to make valid predictions about what future rates will be useful for
businesses.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
PROCEDURE:
The project will be divided into three steps. First, we will run a predictive model to identify what
might be interpreted as causing inflation. Then, a cluster model will be used to identify other
factors that accompany inflation. Finally, the results of the two types of analyses will be
compared to come up with appropriate answers for the question above.
We hypothesize that the inflation rate is dependent on several key economic factors. We also
hypothesize that fluctuations in the inflation rate will be most strongly correlated with
fluctuations in the CPI and Unemployment Rate, and fluctuations in several other variables will
aid in the predictive ability of a statistical model.
Based on the project plan, selected data was collected to support the model.
With regards to the causal prediction, we see that the inflation rate depends in part upon
exchange rates and the USD’s value relative to foreign currency. We can compare foreign
inflation rates with that of the U.S. to assess stability of economies and possible financial
investment strategies. Additionally, because the inflation rate weighs so heavily on foreign
investment, it can have great implications for foreign investment, import, and export. Since both
the UK’s and Japan’s economies have a strong impact on global financial market, their currency
exchange rates (UK-Pound and Japanese-Yen) should be considered. We also selected the
Unemployment-rate and Housing-rate of the U.S., which reflect the U.S. economic situation, to
accompany in this predictive model.
For the clustering model, we see that there are several key economic factors which affect the
inflation rate. The most common measure of inflation rates is the Consumer Price Index (CPI),
which is based on a survey of tens of thousands of items Americans commonly purchase. Our
project will use other variables calculated by the Bureau of Labor Statistics to predict the
inflation rate. These variables include Gasoline, Commodities, Miscellaneous, and Rubber and
plastic. Additionally, we will use data of average expenditures on Food and beverages, Power
and related products, and Furniture and household durables. The historical inflation rate is also
listed for predictive purposes.
We decided to use the monthly data from 1976 to 2011 for the project, since this period includes
a part of the Cold War (1947-1991), the Asian economic crisis (1997-8) and the great economic
crisis (2007-2010); thus, it can offer valuable insight into how and why the inflation rate
changes.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
Completed Data Mining Steps:
Since the dependent variable in the data is a continuous variable, the model will be run for a
prediction purpose. We will be using predictive linear regression, predictive generalized linear,
CHAID tree models, and Neural Network models. Finally the best model of the three will be
chosen as the predictive model. The choice will be made based on the standard error and
predictive accuracy of each model. We also broke the data into two sections: those data pertinent
to household and marketing purposes (CPI, Gasoline, etc.) and typically recognized as being
indicators of consumer confidence, and those data more pertinent to financial and investment
purposes (unemployment rate, exchange rates, etc.).
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
MODELS
First we compiled and cleaned the data, removing outliers and accounting for missing data.via
analysis using data audit and transform nodes.
We then separated the data based on whether it was deemed as “associative” or “causal,” using a
filter node, and ran both through several models. We also partitioned the data in order to make
sure our model will be predictive of data beyond that used to build it.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
We also included a time series graph node, in order to visually see the correlation of variables
over time, and to be able to associate rises and falls of the inflation rate, prices, and financial
indicators with our knowledge of U.S. and international economic history.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
We examined each model nugget, and using a user input node could predict inflation rates based
on acquired or predicted knowledge of our other variables. Below are results of predictor
importance for our Neural Network using causal data.
This was our most accurate model. We also included an accuracy summary of this model,
showing it was 90 percent accurate, well above our goal of 70 percent accuracy. Several other
models had accuracy ratings of above 70 percent, but no model truly agrees on one single
variable as the most important predictor.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
However, using an analysis node and comparing all the models together, we see that most of the
models agree fairly well on their actual predictions.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
DEPLOYMENT
The associative model (using ‘Gasoline,’ ‘Commodities,’ ‘CPI,’ etc.) lends itself to use in the
field of marketing. It represents the relationship between fluctuation in the inflation rate and
price fluctuation of common household necessities. The causal model (using exchange rates, Tbill, and Moody’s Aaa rates, etc.) will be more useful in the financial sector. This model uses
other historical financial data in order to predict the inflation rate, which has major implications
for stock and investment trading, as well as global market trade. Both of these models require
some considerable statistics acumen, and are mainly accessible only to those with a background
in forecasting, econometrics, or intermediate statistics. Both neural net models are surprisingly
accurate and could be used in market research to assess market prices and consumer confidence
or in financial analysis assessing potential financial trading and/or global trading.
LIMITATIONS
We are very pleased with the accuracy of our models. It is important to realize that no single
variable is in itself a good predictor of the inflation rate, however using several key variables
together and examining their relationships with each other can create an accurate predictive
model. A major contributor to the accuracy of our model is most likely the frequency of our data
(monthly). Additionally, while our model is very accurate it is not over fitted and has the ability
to still be accurately predictive when used with new data. While our model can give accurate
predictions for the inflation rate, it still does not provide insight as to how companies should act
considering the interest rate.
The nature of our most accurate model, the Neural Network, is that while it can accurately
predict an inflation rate given series of data, it is unclear how exactly the Neural Net comes to its
conclusion. Dissimilar to linear regression or even CHAID models, we have no general rule and
have to trust the hidden neurons of the Neural Net. Also, we should take into account the high
correlation between our predictor data (i.e. exchange rates, unemployments rates, and other
“causal” data) and the inflation rate itself. Especially in our linear regression model, we have to
be aware of multicolinearity.
Further analysis must also take into account intricacies of specific business employing our
model. While our model accounts for previous historical spikes in the inflation rate, consumer
prices, exchange rates, etc. it cannot predict extraordinary phenomena such as stagflation and
economic depressions.
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PREDICTING THE U.S. FUTURE INFLATION RATE
By: Ayman Hassan Abualajin - Tim Christian - Tina Nguyen
Resources:
Chang, Kuang-Liang, and Chi-Wei He. "Does the Magnitude of the Effect of Inflation
Uncertainty on Output Growth Depend on the Level of Inflation?." Manchester School 78.2
(2010): 126-148. EconLit with Full Text. EBSCO. Web. 11 Apr. 2011.
Berentsen, Aleksander, Guido Menzio, and Randall Wright. "Inflation and Unemployment in the
Long Run." American Economic Review 101.1 (2011): 371-398. EconLit with Full Text. EBSCO.
Web. 11 Apr. 2011.
http://www.federalreserve.gov/econresdata/releases/statisticsdata.htm
http://www.bls.gov/bls/inflation.htm
http://en.wikipedia.org/wiki/Inflation
http://awberry2.blogspot.com/2011/03/how-to-use-inflation-rates.html
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