The total dataset consists of 551 monthly observations covering the period from January 1959 to December 2005, available from the Federal Reserve's economic and financial database (FRED) described in [15].
The choice of input variables was made on the basis of some previous research suggesstions ([11],[13]). Fifteen input variables describing US capital market and macroeconomic situation are used to predict the value of the the US federal funds target rate in the next month (FFR). They can be grouped into several categories as follows: (1) capital market indicators: S&P500 index (S&P500), Dow Jones Industrial Average (DJI); (2) inflation indicators: consumer price index (CPI), inflation rate (INFL), US unemployment rate (UNEMPLOY), producer price index (PPI); (3) Economic real sector indicators: industrial production index (IPI), personal consumption expenditures (PCE), housing starts (HOUSING); (4) financial market indicators: discount rate (DISC_RATE), 10 year treasury rate (10Y_TREASURY), 3 month treasury bill rate (3M_TREASURY); (5) monetary aggregates: money supply (M1); and (6) commodities market indicators: gold price (GOLD), oil price (OIL).
In order to eliminate the trend influence and amplitude influence, data were transformed into percentage change and normalized before entering neural network models. Descriptive statistics
Wednesday, September 26, 2007
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2 comments:
No updates since January? What sort of results did you see?
-Will Dwinnell
Data Mining in MATLAB
I'm currently updating the model with some new data and applying it on new markets...
Results I've shown in the latest post. I'll check your blog of data mining in MATLAB. Thanks for visiting.
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