Managerial economicsFor organizations, the key to success in the digital age Federal agencies involved in budgeting, tax administration and other activities that call for economic modeling can get a boost from artificial intelligence AI technologies that have matured over the years to become powerful tools. For example, multidimensional economic modeling enabled by technological advances allows researchers to rapidly predict and consider the potential effects of a large number of variables on the economy across multiple dimensions. Traditional modeling has just two dimensions available. In addition to government activities, this advanced modeling approach has applications in policy analysis, academic settings and other organizations. AI is expanding the capabilities of modeling in ways that could make research significantly more effective. In particular, work is underway on detecting patterns in vast volumes of data and interpreting their meaning.
Economic Forecasting: Introduction Video
Improving economic forecasting with AI
It seems that you're in Germany. We have a dedicated site for Germany. Authors: Carnot , N. Economic Forecasting provides a comprehensive overview of macroeconomic forecasting. The focus is first on a wide range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting. In addition, the book addresses the main issues surrounding the use of forecasts accuracy, communication challenges and their policy implications. A tour of the economic data and forecasting institutions is also provided.
Many of our ebooks are available through library electronic resources including these platforms:. Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance.
The CEFC meets twice a year to update its economic forecast, which is used in establishing the revenue forecast for the State. The Commission forecasts three key indicators: wage and salary employment, personal income by component , and the Consumer Price Index. These materials summarize the current economic environment and any recent developments since the Commission's past meeting. Special attention is paid to indicators of general economic activity as well as indicators for employment, personal income, and inflation the Consumer Price Index , which are forecast by the Commission. Additional background may include updates on home prices and sales, heating and crude oil, and other topics of interest.