Brief Meeting Update: 1st of August 2007

Yesterday we held our weekly meeting with Dr.Poon where we discussed our current progress on our individual projects as well as how we are progressing with our data set formation. In this post i will briefly recount the most important agendas covered in the meeting and will elaborate this in more depth in my future posts over the weekend. Two important agendas were covered in this weeks meeting which are: next weeks deliverables regarding the use of Dr.Poon's data set and further clarification was provided on the data set we are currently putting together.

Data Set Construction

Dr.Poon provided further clarification as to the construction of the new data set. Most importantly I was able to attain a greater understanding as to how Dr. Poon constructed the productive capital component of his data set. He informed me that the two ABS items that were used in his data construction were:

• Net Capital Stock
• Productive Capital

The differences between the two capital measures are how they take depreciation into account. Depreciation is defined as the reduction in the balance sheet value of a company asset to reflect its loss of value through age and wear and tear.Net capital stock is more items that are essentially depreciable items such as machinery. A particular form of depreciation is applied such as straight line and this is reported in the firms financial statements. Hence Net capital stock is essentially the book value of the particular capital item. Productive capital stock on the other hand is an item that still gives the same amount of use across its life such as a light bulb. As items such as these don't depreciate they are reported separately form net capital stock. Hence to find the total measure of capital for our data set we will need to combine both measures.

The use of the appropriate deflators is another important issue. Sine we are looking at a panel data set (a set of variables across time and a range of industry) we will need to express our units in constant dollar terms. Dr. Poon informed us that we may need to ring the ABS to find out how the calculated it for particular items if it not available on the website. This will be further investigate by my fellow peers and I in the coming week. Dr. Poon has also asked us to express our data set using the following variables:

• Software Capital
• Hardware Capital
• Other Capital (essentially anything that is not classified as IT capital)
• Labour Hours
• Value Add (output)

Give these new insights I will be meeting again on Monday with my fellow peers to continue our work on this data set.

Deliverables

In addition to the construction of the data set Dr. Poon set each of us a particular task involving the data set he provided. My task involves calculating the AES in the R using the technique i mentioned in my earlier post for the data set he has provided. I plan to go about this in the following way:

1. Splice up the data set into the following variables: Software Capital, Hardware Capital, Other Capital, Labour Hours, Value Add, for each industry across time
2. Stack the panel data set so a pooled regression can be run
   
3. Impose a translog function, transform the data appropriately and run the regression
   
4. Using the estimates of the parameters from the regression calculate the bordered Hessian matrix
5. Calculate the AES for all input variables for all industries
   
6. Determine the Standard error for each AES calculated
7. Apply a T-test to each AES to see if it is statistically significant or not.

I have quite a good idea how to implement this in R. I will need to write my own function to calculate the AES for each industry and I will need to spend a bit of time researching the syntax for writing R functions but this should not take more then a few hours to master this. I will recount in the next few days how I am progressing with this and report and preliminary results ASAP.