empirical report

There are essentially three approaches to macroeconomic forecasting: judgemental forecasting,

model forecasting, and statistical forecasting. Judgemental forecasters gather various kinds of

information and data from official sources, and then they forecast future macroeconomic activity

based on their own informal judgement about how the economy works. Both model forecasting and

statistical forecasting are done using sophisticated macroeconomic models and econometric

models. Since this is a 2nd year subject, I believe it is beyond the scope of the class to ask the

students to do either model forecasting or statistical forecasting. So, judgemental forecasting

would be good enough as long as you give a detailed explanation following each number that

explains why you chose that number. For example, you can use leading indicators to predict where

the economy is headed.

To summarize, the report should be an empirical (not econometric) analysis rooted in

macroeconomic theory.

2. How to detrend the data?
You would need to detrend the data for most aggregate variables (such as GDP, consumption,

investment, etc). The best method of detrending the data would be a Hodrick-Prescott filter.

However, since most of you are not familiar with this method, a good alternative would be

expressing these aggregate variables in growth terms. For instance, instead of plotting the time

series of real GDP, you should first remove the trend by calculating the real GDP growth rate and

then plot the time series of real GDP growth rate. To see how to calculate growth rates, please

read the file How to calculate growth rate (growthrates.pdf) that has been posted under learning

resources on Blackboard.

3. How to calculate correlations and cross correlations?
An example of how to use Microsoft Excel to calculate these statistics have been posted on

Blackboard (example.xls). Please note that when calculating correlations (and cross correlations),

you are only required to calculate the correlation (and cross-correlation) of a particular variable

(for example, unemployment rate) with output.

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