The installment below discusses macroeconometrics. It is very brief, because I think that the issue is better discussed in the context of the historical perspective that I envision providing in the main part of the book. We are still on the introduction.
What can we learn about macroeconomics by studying the history of macroeconomic data? Much less than one might hope.
One approach to using historical data is called calibration. This involves writing down a set of generic equations that determine the evolution of macroeconomic variables, and then by a process of trial and error arriving at values for parameters that allow the equations to reproduce historical performance. As a very simple example, one could take all components of spending other than consumption as exogenous, and then write down a simple consumption function as C = a + bY, along with the identity Y = C + I + G + NX. Then, try out various values for a and b, and, using historical values for investment, government spending, and next exports, find the values that allow you to best track the history of C and Y.
This process of trial and error can be assisted by the use of computational techniques borrowed from statistics. The most basic technique is linear regression, which can be modified and enhanced in a variety of ways. However, the statistical properties of these techniques rarely carry over to the practice of macroeconometrics.
In statistics, each observation is supposed to be independent of the other observations. With macroeconomic data, this independence condition is violated. If you pick two periods at random, they are going to be much more similar if you happen to pick adjacent quarters (say, the second and third quarter of 1965) than if you pick two quarters from different decades.
In statistics, parameter estimates are unbiased under the assumption that the investigator makes only one attempt to use the data. In practice, economists tend to engage in what Edward Leamer calls specification searches. They obtain parameter values from statistical computations, then tweak the model, obtain new values, tweak again, and so on. This process vitiates the statistical properties of the resulting estimates.
Regardless of methodological problems, macroeconometrics would be useful if the data spoke with a strong voice. That is, if it were the case that certain model specifications and parameter values were clearly needed in order to explain and predict the evolution of macroeconomic data, then we could use historical evidence to limit the range of theories worthy of consideration. Unfortunately, that is not the case. Often, a simple one-variable random walk with drift can explain and predict a macroeconomic series of interest as well as or better than a sophisticated multivariate model, even at long time horizons. There simply is not enough information in the data to make a convincing case for any particular theory of macroeconomics.