Why Catalonia should use Next Generation funds to boost or modernize this specific sector or project, to do it so you should use empirical data, academic references and the theoretical arguments that we have seen across the lectures.
In order to boost the Economy after covid-19’s the European Union has created the EU’s Next Generation program, which consist of European funds available to EU member states to modernize and boost their economies.
Your assignment is to identify one sector or one strategic project and argument why Catalonia should use Next Generation funds to boost or modernize this specific sector or project, to do it so you should use empirical data, academic references and the theoretical arguments that we have seen across the lectures.
Catalonia is a region with a rich history and culture, and it is currently in the process of seeking independence from Spain. In this blog post, we will discuss one of the key sectors that Catalonia should focus on in order to improve its economy: transportation. We will provide evidence from academic studies and theoretical arguments to support our case that Next Generation funds should be used to modernize transportation in Catalonia.
There are several reasons why transportation is a key sector for Catalonia. First, as a landlocked region, efficient transportation is essential for Catalan businesses to be able to compete in global markets. Second, improved transportation infrastructure would make it easier for tourists to visit Catalonia, which would boost the Catalan economy.
Finally, many of the current transport options in Catalonia are outdated and do not meet the needs of the 21st century.
Next Generation funds could be used to modernize different aspects of Catalan transportation, such as airports, railways, and roads. In particular, we believe that these funds should be used to improve connectivity between different parts of Catalonia.
This would not only make it easier for people and businesses to move around within the region, but it would also make Catalonia more attractive to tourists and investors.
There are a number of academic studies that support our argument that transportation is a key sector for Catalan economic development. For example, a study by the University of Barcelona found that improved transportation infrastructure could lead to an increase in GDP of up to 0.75%.
Another study, this one by the Catalan government, found that every euro invested in transport generates four euros in economic activity. These studies make a strong case for why Next Generation funds should be used to modernize transportation in Catalonia.
In addition to the empirical evidence, there are also several theoretical arguments that support our case. First, improved transportation infrastructure is essential for regional integration and cohesion. This is particularly important for Catalonia, which is seeking independence from Spain. Second, transportation is a key sector for economic development and competitiveness. Catalonia needs to be able to compete in global markets, and efficient transportation is essential for this.
Finally, improved transportation infrastructure would make it easier for tourists to visit Catalonia, which would boost the Catalan economy.
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges.
This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear.
We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Forecasting has come a long way since early humans looked at the sky to see if the weather would be suitable for hunting, and even since hunters could get a forecast such as “a high of 40 with a chance of rain”.
Now a hunter can look at a smartphone to instantly get hour-by-hour forecasts of temperatures and probabilities of rain at multiple locations as well as videos of maps showing forecasted weather patterns over the coming hours.
Tailored forecasts of increasing sophistication can be generated to inform important decisions of many different types by managers, public officials, investors, and other decision makers.
In the 15 years since the excellent review paper by De Gooijer and Hyndman (2006), the field of forecasting has seen amazing growth in both theory and practice. Thus, this review is both timely and broad, ranging from the highly theoretical to the very applied.
Rapid advances in computing have enabled the analysis of larger and more complex data sets and stimulated interest in analytics and data science. As a result, the forecaster’s toolbox of methods has grown in size and sophistication.
Computer science has led the way with methods such as neural networks and other types of machine learning, which are getting a great deal of attention from forecasters and decision makers.
Other methods, including statistical methods such as Bayesian forecasting and complex regression models, have also benefited from advances in computing. And improvements have not been limited to those based on computing advances. For example, the literature on judgmental forecasting has expanded considerably, driven largely by the “wisdom of crowds” notion.