“The Canadian Labour Congress and the heads of G7 national labour organizations will meet in Ottawa at the Labour 7 (L7) Summit to discuss and provide recommendations on issues such as gender equity, good jobs, inclusive growth, and climate change.” – G7
Like astronomers studying the evolution of stars or biologists studying the evolution of species, macroeconomists cannot conduct controlled experiments in a laboratory. Instead, they must make use of the data that history gives them. Macroeconomists observe that economies differ across countries and that they change over time.
Learn everything you need to know about Advanced Macroeconomics here.
3 Statistics very important Economist use are:
“Society’s capacity to produce will outstrip its capacity to consume.” – Marx
Human judgment permeates forecasting processes. In economic forecasting, judgment may be used in identifying the endogenous and exogenous variables, building structural equations, correcting for omitted variables, specifying expectations for economic indicators, and adjusting the model predictions in light of new information, official announcements, or “street” talk. Forecasters appear to be highly satisfied with judgmental approaches, preferring them over quantitative techniques due to reasons of accuracy and difficulties in obtaining the necessary data for quantitative approaches.
Judgmental biases argued to be especially relevant to forecasting include: illusory correlations, hindsight, selective perception, attribution of success and failure, underestimating uncertainty, optimism, overconfidence, and inconsistency in judgment. These biases could also be related to the organisational incentive system. For instance, forecasters mostly prefer to underforecast, justifying this tendency typically by their motivation to look better if the stated goals are surpassed, or by the choice to be conservative.
Judgmental forecast on the other hand, benefit from human ability to evaluate information that is difficult to quantify, as well as to accommodate changing constraints and dynamic environments. Extensive implications of judgmental forecasting performance necessitates detailed analysis targeted at educating the users and providers of forecasters to recognize those elements of the task which are best delegated to a statistical model and to focus their attention on the elements where their judgment is most valuable.
Uncertainty is on the raise and under our eyes. Everybody can feel it. Even a small dose of it. Perhaps, it is due to a huge list of ongoing activities that we might explain slow through the week:
Restrictions travel to and from the United States
Protests around the world, including in American soil and European soil
Inflation and Deflation at the same time
Rise of Corruption involving private companies – The oher side of the capitalism
Lobbying – Isn’t the same as corruption?
Increase cost of Health Care – We will all pay for it
Lack of Transparency
Baby Boomers retaining their jobs
House price crisis
All these things together, and even isolated, are increasing our level of uncertanty on a daily basis. It is changing the way we are thinking and trusting in companies, enterprises, schooling and in our financial. A new approach is in need to start soon as the old fashion Chicago school does not work anymore to us. Good Luck to all of us. And, always remember that the world was not made only for you maintaining one’s faith above the torment of doubts.