Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression...

In order to better capture non-R&D based processes related to Learning by Doing, Using and Interacting (DUI) as a basis for policy advice, this paper empirically identifies DUI mode drivers of SME innovation. For the first time, a large set of conceptually derived indicators is used in a...

El propósito de este documento es presentar el trabajo sobre la sectorización y clasificación de Holdings usando Machine Learning (en español, Aprendizaje Automático) que se ha desarrollado en la Central de Balances en el Banco de España durante el último año. Este trabajo también ha...

We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and...

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies...

Widespread electric vehicle adoption is considered a major policy goal in order to decarbonize the transport sector. However, potential rebound effects both in terms of vehicle ownership and distance traveled might nullify the environmental edge of electric vehicles. Using cross-sectional...

This paper develops a novel indicator of global economic activity, the GEA Tracker, which is based on commodity prices selected recursively through a genetic algorithm. The GEA Tracker allows for daily real-time knowledge of international business conditions using a minimum amount of...

This paper proposes a multi-level dynamic factor model to identify common components in output gap estimates. We pool multiple output gap estimates for 157 countries and decompose them into one global, eight regional, and 157 country-specific cycles. Our approach easily deals with mixed...

Factor models feature prominently in the macroeconomic nowcasting literature, yet no clear consensus has emerged regarding the question of how many and which variables to select in such applications. Examples of both large-scale models, estimated with data sets consisting of over 100 time series...

In this paper, we discuss estimation procedure and various inferential methods for varying coefficient panel data models that include spatially correlated error components. Our estimation procedure is an extension of the quasi-maximum likelihood method for spatial panel data regression to the...