buschmaster, select-fire and Captplaid
Or anyone else who wants to do some ciphering. I have a ton of work built up and here is a problem.
Gross Domestic Product (GDP) is a measure of overall activity in the economy. It is defined as the value at the final point of sale of all goods and services produced during a given period by both domestic and foreign-owned enterprises. GDP data for the
1950-2004 period shown in Figure 7.3 offer the basis to test the abilities of simple constant change and constant growth models to describe the trend in GDP over time.
However, regression results generated over the entire 1950-2004 period cannot be used to forecast GDP over any subpart of that period. To do so would be to overstate the forecast capability of the regression model because, by definition, the regression line
minimizes the sum of squared deviations over the estimation period. To test forecast reliability, it is necessary to test the predictive capability of a given regression model over data that was not used to generate that very model. In the absence of GDP data for future periods, say 2005-2010, the reliability of alternative forecast techniques can be illustrated by arbitrarily dividing historical GDP data into two subsamples: a 1950-99 50-year test period, and a 2000-04 5-year forecast period. Regression models estimated over the 1950-99 test period can be used to "forecast" actual GDP over the
2000-04 period. In other words, estimation results over the 1950-99 subperiod provide a forecast model that can be used to evaluate the predictive reliability of the constant growth model over the 2000-04 forecast period.
A. Use the regression model approach to estimate the simple linear relation between the natural logarithm of GDP and time (T) over the 1950-99 subperiod, where ln GDPt = b0 + b1Tt + ut and ln GDPt is the natural logarithm of GDP in year t, and T is a time trend variable (where T1950 = 1, T1951 = 2, T1952 = 3, . . ., and T1999 = 50); and u is a residual term. This is called a constant growth model because it is based on the assumption of a constant percentage growth in economic activity per year. How well does the constant growth model fit actual GDP data over this period?
B. Create a spreadsheet that shows constant growth model GDP forecasts over the 2000-04 period alongside actual figures. Then, subtract forecast values from actual figures to obtain annual estimates of forecast error, and squared forecast error, for each year over the 2000-04 period. Finally, compute the correlation coefficient between actual and forecast values over the 2000-04 period. Also compute the sample average (or root mean
squared) forecast error.
Based upon these findings, how well does the constant
growth model generated over the 1950-99 period forecast actual GDP data over the 2000-04 period?
Gross Domestic Product (GDP) is a measure of overall activity in the economy. It is defined as the value at the final point of sale of all goods and services produced during a given period by both domestic and foreign-owned enterprises. GDP data for the
1950-2004 period shown in Figure 7.3 offer the basis to test the abilities of simple constant change and constant growth models to describe the trend in GDP over time.
However, regression results generated over the entire 1950-2004 period cannot be used to forecast GDP over any subpart of that period. To do so would be to overstate the forecast capability of the regression model because, by definition, the regression line
minimizes the sum of squared deviations over the estimation period. To test forecast reliability, it is necessary to test the predictive capability of a given regression model over data that was not used to generate that very model. In the absence of GDP data for future periods, say 2005-2010, the reliability of alternative forecast techniques can be illustrated by arbitrarily dividing historical GDP data into two subsamples: a 1950-99 50-year test period, and a 2000-04 5-year forecast period. Regression models estimated over the 1950-99 test period can be used to "forecast" actual GDP over the
2000-04 period. In other words, estimation results over the 1950-99 subperiod provide a forecast model that can be used to evaluate the predictive reliability of the constant growth model over the 2000-04 forecast period.
A. Use the regression model approach to estimate the simple linear relation between the natural logarithm of GDP and time (T) over the 1950-99 subperiod, where ln GDPt = b0 + b1Tt + ut and ln GDPt is the natural logarithm of GDP in year t, and T is a time trend variable (where T1950 = 1, T1951 = 2, T1952 = 3, . . ., and T1999 = 50); and u is a residual term. This is called a constant growth model because it is based on the assumption of a constant percentage growth in economic activity per year. How well does the constant growth model fit actual GDP data over this period?
B. Create a spreadsheet that shows constant growth model GDP forecasts over the 2000-04 period alongside actual figures. Then, subtract forecast values from actual figures to obtain annual estimates of forecast error, and squared forecast error, for each year over the 2000-04 period. Finally, compute the correlation coefficient between actual and forecast values over the 2000-04 period. Also compute the sample average (or root mean
squared) forecast error.
Based upon these findings, how well does the constant
growth model generated over the 1950-99 period forecast actual GDP data over the 2000-04 period?
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oh! I didn't see this until today.
basically, what you're saying is that you can make a mathematical model using data from that 50 year period, but the GDP goes up and down too much to actually make a good prediction off of it.
like saying, you can make a graph of average temperatures over the last 50 years, but you can't predict tomorrow's temperature with it, because it's just too unpredictable.
sounds about right.
making a model with a spreadsheet and comparing the 1050-1999 period to the 2000-2004 period, like you asked, would be a peice of cake. that's the right tool anyways.
got some data?0 -
It is long done and turned it.
If I remember right the secret to this one was using G=a+bT to find the demand equation for growth. Then solving the sytem of equations and plugging the ansers into the spreadsheet.0
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