Search results

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).