With the confidence and language economists and analysts use when making predictions, it is easy to forget that they have near to no knowledge of what is about to transpire in the economic world with the certainty they portray. An easy explanation to this is the world is complicated and predicting complicated systems is difficult and sometimes near impossible. The lengthy explanation is a bit nuanced so hop in while I explain.
It is important to mention two things, first is that forecasters use prior knowledge of treads to help predict the future and second is a key assumption that underlines economics is that individuals are rational, they employ logical thinking. This assumption gives birth to predictions that rely on people to react logically given a particular stimulus or a multitude of stimuli. The problem arises because a large majority of people do not employ logical thinking and thus the prediction breaks down. This is only a surface level explanation but highlights the complexity of economic prediction.
Since we are now aware that people do not always react the way we expect them to, economists and analysts try and factor this into their predictions. Now since everyone is different and will react differently, forecasters will attempt to group similar reactions together and give it a weightage (a percentage) on how much that specific reaction group will affect the overall economic environment. Keep in mind that these reactions themselves are not certain, they are an expected reaction that they get by employing past information and treads of how people reacted in the past and used that to predict the future.
Forecasters also portray an availability bias. This means that recently occurred events such as a rise in economic activity last year will weigh heavier in the next year’s prediction compared to an economic downturn that happened seven years ago. The outcome of this is that surprise events such as a worldwide pandemic, natural disaster or unstable political environment would not be factored in the prediction because they are not recurring events; even though these surprise events are the ones that cause the most lasting and damaging effects on the economy.
To add on to the list of complexities, the final prediction on itself would alter the effects on the economy creating a sort of feedback loop. If the forecasters predict an inflation rate of 3%, the people would demand that their wages be increased by 3% to offset the loss of their purchasing power. This will result in the actual inflation rate to be higher than previously predicted. The existence of the prediction changes the environment they are trying to predict. And finally with the icing on the cake, forecasters only use collected data to make predictions. This data could be no longer relevant, unrepresentative of the actual population or even altered meaning that their predictions would not match up with reality. All of what has been said can be backed up with this simple statistic, economists have failed to predict 148 out of the 150 past recessions, and with all the complexity involved, did they even ever have a chance? This forecasting issue is one of chaos theory, where small changes in initial conditions will drastically change the outcome of the system. It is not that it is impossible to forecast accurately and consistently, but there are numerous variables to consider that we are aware of and unaware of to implement into a prediction model.
Blodget, Henry. “REMINDER: Economists And Analysts Have No Idea What Is Going To Happen Next Year.” Business Insider, 19 Dec. 2011, www.businessinsider.com/economist-forecasts-wrong-2011-12?IR=T. Accessed 7 Oct. 2020.
FARMER, LIZ. “Why Economists’ Predictions Are Usually Wrong.” Www.Governing.Com, Nov. 2015, www.governing.com/topics/finance/gov-economic-forecasting-accuracy.html. Accessed 7 Oct. 2020.
Shaw, Adam. “Why Economic Forecasting Has Always Been a Flawed Science.” The Guardian, 2 Sept. 2017, www.theguardian.com/money/2017/sep/02/economic-forecasting-flawed-science-data.