"I therefore eliminated the effect of variations in industry size by
“deflating” each sector’s aggregate price and aggregate value by its
aggregate cost. The cost-weighted variables can be understood as measures
of the “percentage” markup over costs, in price and value terms,
respectively."
"Running 21 log-linear cross-sectional regressions of the
cost-weighted prices on the cost-weighted values (one for each
year), I found no support for the labor theory of relative prices."
Questions:
a) Where are those "prices" and "values" come from? How do you get them?
How are measured? Are you talking about "market prices"? Did you take out
rent effects (oil, e.g.)?
b) Could you please give us an example of the spurious correlation you are
pointing out which doesn't involve "values" and "prices"? As far as I
understand the issue, the matter is that the absolute size of a given
sector does provoke a "correlation" in itself. For example, imagine two
variables, "sales" and "debt" which are not "correlated" in the sense that
*they are not equal* (i.e. sales/debt is not equal to 1), as it seems the
theory your are testing does regarding prices and values. But as "big
sectors" have "big sales" and "big debts", and "small sectors" have "small
sales" and "small debts", we can get a fine correlation if we compare the
absolute magnitudes. Is this the argument?
c) So, to get ride of that "absolute effect" you are trying with something
like this:
W/K and P/K
where K is cost-price and, then, correlating them. Right?
Can you perform some test on something like
P/W
directly?
d) Could you please explain why did you run "log-linear" regressions?
e) Does each regression correspond to one year?
f) Can't you specify some kind of "TSS model" in which there are time lags?
Values of one year "determining" prices of the next?
Many thanks in advance!
Alejandro Ramos