Once the economical model reduces to a statistical one, the entire machinery of the latter is at the disposal of the former. Alternatively, a stochastic processes can be the solution to an economical model, so there's another machinery that can handle uncertainty.

For instance, among the econometric (statistical) models, one may consider: Linear regression, Generalized linear model, Probit, Logit, ARIMA, Vector Autoregression, Cointegration, Hazard, etc.

As for stochastic processes, one may consider: Optimal control of dynamic stochastic systems: this leads to problems of stochastic control where the optimization objective has to be reformulated suitably; Stochastic stability of dynamic models, where the parameters or their estimates may be stochastic: this leads to self-tuning regulators, where optimal estimation and optimal control may be suitably combined; and Models of optimal search and learning in a random environment, where a suitable probabilistic criterion, e.g., an entropy criterion has to be optimized: this leads to problems of improving the model specification which are also called problems of optimal design in control system engineering.

You can consider the pandemic, disaster, clash, country legal policy. Foremost bank management ignorance on undeveloped countries exchange policy.

Petroleum crah.

Once the economical model reduces to a statistical one, the entire machinery of the latter is at the disposal of the former. Alternatively, a stochastic processes can be the solution to an economical model, so there's another machinery that can handle uncertainty.

For instance, among the econometric (statistical) models, one may consider: Linear regression, Generalized linear model, Probit, Logit, ARIMA, Vector Autoregression, Cointegration, Hazard, etc.

As for stochastic processes, one may consider: Optimal control of dynamic stochastic systems: this leads to problems of stochastic control where the optimization objective has to be reformulated suitably; Stochastic stability of dynamic models, where the parameters or their estimates may be stochastic: this leads to self-tuning regulators, where optimal estimation and optimal control may be suitably combined; and Models of optimal search and learning in a random environment, where a suitable probabilistic criterion, e.g., an entropy criterion has to be optimized: this leads to problems of improving the model specification which are also called problems of optimal design in control system engineering.

Thank you George for your clarification, very valuable indeed

Economic uncertainty implies the future outlook for the economy is unpredictable. When people talk of economic uncertainty, they usually imply there is a high likelihood of negative economic events. Economic uncertainty could involve. Predictions of a higher and more volatile inflation rate. ( inflation uncertainty). https://www.economicshelp.org/blog/4941/economics/economic-uncertainty/

Uncertainty and its role in decision-making is an important phenomenon that has received considerable research attention within international business (IB) studies over the last five decades. Uncertainty, defined as the lack of knowledge about the probabilities of the future state of events. https://www.tandfonline.com/doi/full/10.1080/23311975.2019.1650692?af=R&

Economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667305/

see

Article Probability and Uncertainty in Economic Modeling

Economic uncertainty implies the future outlook for the economy is unpredictable. When people talk of economic uncertainty, they usually imply there is a high likelihood of negative economic events. Economic uncertainty could involve. Predictions of a higher and more volatile inflation rate. ( inflation uncertainty)

When the possible outcomes of a decision model are not controlled by the decision maker, but modeled by some stochastic event, there is uncertainty or risk, the word more used to refer it. If the probabilities for the states of nature are not known, you can have help on the Minimax, Savage, Laplace, Hurwick methods to pick the best decision or alternative. If probabilities are know and discrete, you can use decision trees and influence diagrams to pick the best decisions. If probabilities for the states of nature are continuous, Monte Carlo simulation is the best approach .

Let us take an example. An agent-based model of the economy, society, stock market, etc. You define the model, implement it in an AB evaluation environment, and run the simulations.

Many people end here. What can be done more? We change parameters and run simulations again for all changed parameters separately. In this way, we create statistics of possible evolutions of the model. From here, statistical evaluation is easy.

Agreed Chinaza Godswill Awuchi