Financial Forecasting - Time Series Models Essay

Words: 2291
Pages: 10

Time Series Models for Forecasting New One-Family Houses Sold in the United States
The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers during the nineties and the early part of the next decade which caused bankers to act predatorily in their lending practices, as they could easily sell and package subprime mortgage loans on leverage. This act caused a bubble that would later
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Often times this can lead to a less accurate forecast as too much emphasis is being applied to the correlation of the independent variables to the dependent variable. In reality large ranges of macroeconomic data such as NHS vary because of numerous variables that may not be taken into account. The Multiple Regression model in Table 1 will have NHS as the dependent variable and use the 30-year conventional mortgage and the Seasonally Adjusted Disposable Person Income as the independent variables. This data comes from the Federal Reserve Bank of St. Louis.
We are going to use two separate periods in our analysis. The first period that we are going to use is our historical data from January 1975 through August 2011. The last six months of model from September 2011 through February 2012 is our hold out model in which we test the forecasted NHS results against the actual NHS during the same span of time to test the accuracy of the models forecast’s.
We will use ForecastX software to run the models in an attempt to determine which model is the most accurate and thus should be used to forecast NHS to obtain the clearest picture of the future direction of the market. The two error measurements we will use to determine accuracy are mean absolute percentage error (MAPE) and root mean square error (RMSE). To obtain MAPE, we first divide the forecast error or error (actual value – forecast value) by the actual