Do “Technology Shocks” Create the Boom-Bust Cycles?
Most economists hypothesize that in the real world there are relationships between various economic variables. For instance, the relation between personal consumption expenditure and income after tax can be hypothesized as:
Personal consumption = a* Income after tax,
With a being a parameter. Thus, if a is 0.8 then for income after tax of $100 this would imply that personal consumption is $80.
The parameter a is determined with the help of a statistical method. The statistical method also verifies whether the number obtained is a valid estimate of the true parameter in the real world. (Again, in this way of thinking there are parameters in the real world, which by means of statistical methods can be ascertained.)
Using quantitative methods, economists believe that causes behind economic fluctuations, known as business cycles, can also be established. Finn Kydland and Edward C. Prescott (KP), the 2004 Nobel laureates in economics, hypothesized that an important factor behind economic fluctuations is technology shocks.
To verify this theory, KP employed the Solow growth model (Robert Solow, the 1987 Nobel laureate) which in turn is based on the Cobb-Douglas production function:
Y = A K (1-a)Na,
Where Y is real output, A is a technology factor, K is the capital stock, and N is the number of workers employed. The parameter is a.
Instead of employing conventional statistical methods for the estimation of the parameter alpha, KP introduced a method, which they labeled calibration. What is this all about?
The KP framework utilizes various studies, expert opinion, and data analysis to form a view on the numerical magnitude of a parameter. For instance, using the historical data of wages and income KP have concluded that the parameter a in the Cobb-Douglas production function is around 0.64.
By incorporating the information on a with the information on real GDP, the stock of capital and the number of workers employed, KP were able to extract the numerical values for the technology factor A. Once the technology factor A was obtained, it was employed to assess the effect it has on the fluctuations of various key economic data.
In their research KP have concluded that a technology-induced shock can explain 70 perc
Article from Mises Wire