Like any science, economics is concerned with the explanation of observed phenomena. Theories are developed to explain these observed phenomena in terms of a set of basic rules and assumptions. With the application of statistical and econometric techniques, theories can be used to construct models from which quantitative predictions can be made.
Economic forecasting can be defined as the process of attempting to predict the future condition of the economy by employing a combination of important and widely followed indicators. Economic forecasting typically attempts to come up with future GDP growth rate, inflation rate, unemployment rate and interest rate, among others.
Accurate economic forecasts are important because they can foster confidence and stability in an economy. For example, the national treasury is responsible for managing government finances and allocating funds to relevant departments. When people listen to budget speeches and forecasts, they form expectations, which determine their behavior.
There have been lively discussions surrounding economic forecasts (inflation in particular), and once again, it is that time of the year when forecasts are made and shared. It would be good to discuss why forecasting is a tricky yet indispensable business.
Economic forecasting is tricky because economies, like weather systems, are complex. Instead of having a foundation based on more predictable physical laws, economies are built on human behavior, which naturally involves uncertainty. Demand behavior, for instance, is shaped largely by individual tastes and preferences, which are difficult to generalize because of their volatility and idiosyncrasy. However, this does not mean that prediction becomes absolutely impossible. What must be clarified is that economists are merely describing tendencies at best.
Naturally, looking into the future involves uncertainty and risk, and the fact that forecasts are based on rough models creates a serious dilemma for policy-makers. As economic journalist Robert Samuelson notes, models exclude almost everything interesting and disruptive in life: politics, nationalism, technological change, the weather, greed, fear, ambition, ignorance and stupidity. To explain these lapses, economists often blame shocks. A shock is a catchall label that covers almost anything that an economic model could miss.
According to Samuelson, forecasting is merely telling people what they already know, or might know, by examining available information. It creates an illusion of understanding. The trouble is that there are times when radical and dramatic changes do happen, and at these moments, economists are almost as clueless as everyone else. Their crystal balls are cracked. Their economic forecasts seem to be least reliable when they are most needed.
Nevertheless, despite having what might be perceived as a poor track record, economic forecasting is indispensable because it plays a crucial role in establishing even a general sense of the future, as investment author Cullen Roche argues. Roche prefers to describe forecasting as intelligent guesswork rather than crystal ball gazing. He argues that instead of making highly specific or detailed numerical forecasts, economists can provide a general indication of the likely environment in which forward-looking policy needs to be formulated.
To further clarify his point, Roche compares forecasting with preparing to set sail across an ocean. One would never claim to accurately predict the weather or conditions he might face, but he can have a general understanding of the way the vessel operates, the path, and the potential weather patterns, in order to increase the likelihood that he will reach his destination in one piece.
Since politics could be highly partisan, such as when government agencies produce unrealistic scenarios in an attempt to justify legislation, most rational people might regard government economic forecasts with healthy doses of skepticism. Thus, people can benefit from forecasts offered by private sector economists and academics, for these provide alternative views.
Although forecasts can influence people’s expectations, is it sensible to call out particular forecasters for making inaccurate predictions? Perhaps, there is a need to differentiate between accuracy and precision. In science, measurements that are close to a standard or known value are said to be accurate, whereas measurements that are close to each other are said to be precise.
The best case is that all forecasts are accurate (and thus also precise), but this world is obviously not perfect. In a more natural setting, there is variation across forecasts (measurements not necessarily precise), but at least there are accurate predictions. The worst case is that all forecasts are inaccurate (although likely to be precise, as will be explained shortly).
The suggestion to call out wrong forecasters runs the risk of groupthink, which occurs when a group of well-intentioned forecasters makes irrational decisions that are spurred by the urge to conform or the discouragement of dissent. As Samuelson puts it, to stray too far from one’s peers is to risk looking foolish alone. There is less embarrassment in being wrong with everyone else.
Thus, groupthink is likely to result in the worst case.
Ser Percival K. Peña-Reyes, PhD, teaches economics at the Ateneo de Manila University.
