The Kalecki Profit Equation And Forecasting

Having run through the Kalecki profit Equation (link to the first part of a two-part primer), I just want to make some brief remarks about how it ties into the notion of forecastability (description). Should we be able to expect to forecast the business cycle?

(This article is fairly brief, and re-iterates points I have made in other articles. Travelling and major home renovations have cut into my writing time recently.)

Business Sector Is The Main Source Of Modelling Uncertainty

From the perspective of those who work or are interested in finance, it seems obvious that business decisions are a major driver of the business cycle, assuming that policymakers are not doing anything particularly stupid (as in the Euro area in the post-crisis period). The important exception is the housing market, which is dependent upon the willingness of households to borrow insane amounts of money. (However, even this exception is dependent upon the decisions of the financial sector to extend the insane loans.) Conversely, one of the advantages of a mainstream economics education is that common sense is buried, and the view is that the primary driver of the business cycle is households' decisions to optimise consumption choices over time. The result is that the difficulty of forecasting business sector decisions is swept under the carpet.

Why The U.S. Labour Market Befuddles Forecasters

The U.S. labour market has continued its steady torment of forecasters and bond bears. The headline unemployment rate (U-3) dropped to 3.9% in April, yet inflation worries are negligible. If one told bond market participants and economic forecasters five years ago that the unemployment rate would have a 3-handle -- while the 10-year Treasury would have a 2-handle (just!) -- they would have snorted in your face. However, that is exactly what happened. The difficulties that forecasters are having with the unemployment rate/inflation relationship is exactly what one would expect if inflation were not forecastable; unfortunately, society demands that inflation be forecast.

Can We Falsify Models With Time-Varying Parameters?

In a previous article, I argued that having unknown fixed parameters within many economic models does not create much in the way of uncertainty: just extend the range of historical data available, and we can pin down the parameter values. This article covers a related case: what if we allow parameters to vary with time? This possibility will make it impossible to make reliable forecasts with the model. However, such models have another defect: they can be fitted to practically any data set, making the model non-falsifiable. This can be illustrated by thinking about the simplest model of stock index returns. My argument that the apparent success of mainstream macro modelling techniques relies on the use of such non-falsifiable models.

Triviality Of Parameter Uncertainty And Measurement Noise For Forecasting

In earlier articles, I discussed the notion of forecastability (link to previous article): is it possible to forecast the future values of variables in an economic model? This article will begin an extended analysis of the simplest stock-flow consistent (SFC) model: model SIM. Based on what we know about linear system theory, we can that two standard sources of uncertainty (measurement noise and parameter uncertainty) are not forecasting challenges if we assume that we are working with the correct economic model. Other sources of uncertainty present greater problems, and will be discussed in later articles.

Why We Should Be Concerned About The Forecastability Of Economic Models

Although it might be possible to find dissenters, the apparent consensus among financial market practitioners is that mathematical economic models provide terrible forecasts. One response is to keep searching through the set of all possible models, hoping to find something that works. The author's suggested response is to accept that forecasting is an inherently impossible task. However, in order to advance beyond nihilism, we need to quantify why mathematical models are terrible. My argument is straightforward: the models that provide the best fit to observed behaviour cannot themselves be forecast with the type of information that we have available in the real world.

Forecastability And Economic Modelling

When most people think about macroeconomics, what they want is the ability to forecast economic outcomes. However, economists' (of all stripes) reputation as forecasters is not particularly high. My view is that this is not too surprising: what we want forecasters to accomplish is probably impossible. (I am hardly the first person to note this, as variants of this idea go back at least to Keynes; I could not hope to offer a history of this idea.) However, I think if we want to approach macro theory formally, we need to formalise the notion that outcomes cannot be forecast, which means we need to define non-forecastability formally.

This article gives one potential definition of forecastability, and then applies the concept to a simple stock-flow consistent (SFC) model. It should be noted that these are my preliminary thoughts, and I believe that the definition will need to be refined.