Publications
Publications
Lamas Rodríguez, M., Garcia Lorenzo, M. L., Medina Magro, M., & Perez Quiros, G. (2023). Impact of climate risk materialization and ecological deterioration on house prices in Mar Menor, Spain. Scientific Reports, 13(1), 11772.
Abstract: The frequency and severity of extreme events related to climate change have intensified worldwide in the last decades. It is documented that increasing extreme rainfall and flooding cause more nutrient runoff into waterbodies, initiating numerous harmful algal bloom (HAB) events, especially in fragile ecosystems. We analyze the dramatic economic damage of one of these episodes in Mar Menor, the largest salt-water lagoon in Europe. We show that when the public perceived the severity of environmental degradation, the return on housing investment was 43% lower in the surroundings than in similar neighboring zones 6 years after the HAB (2015). This represents a loss in housing wealth of more than 4000 million euros, around ten times the gains of changing from dry-farming to irrigated crops, which makes this ecosystem fragile. Hence, we quantify some of the economic consequences of ecological deterioration linked to episodes of Global Climate Change.
"What can newspaper articles reveal about the euro area economy?". With Lorena Saíz (European Central Bank). ECB Working Paper Series.
Abstract: This study introduces a novel approach to dictionary-based sentiment analysis that extracts valuable insights from economic newspaper articles in the euro area without requiring article translation. We develop sentiment indices that accurately measure economic, labour, and inflation perceptions in Germany, France, Italy, and Spain using native-language texts. The aggregation of these country-specific sentiments provides a reliable indicator for the euro area as a whole, demonstrating the effectiveness of our approach in several nowcasting and forecasting experiments. This translation-free method significantly reduces resource requirements, facilitates easy replication across various languages, and enables daily updates. By eliminating the translation bottleneck, our approach emerges as one of the most timely and cost-effective economic measures available, offering a powerful tool for monitoring and forecasting business cycles in the multilingual context of the euro area.
"Benchmarking Seasonal Filters for High-Frequency GDP Nowcasts." In progress.
Abstract: Weekly economic indicators are gaining relevance in the forecasting literature. However, traditional methods for seasonal adjustment of quarterly and monthly indicators are not directly applicable to weekly data. Hence, we revise the most used methods for seasonal adjustment of weekly data and compare their performance in the context of generating weekly economic trackers to nowcast GDP growth (Lewis et al., 2022). We show that TBATS, MSTL, and GADM models yield similar performance in this task. Additionally, we examine several practical issues, such as how COVID-19 distorts the estimated seasonal factors of the indicators and how revising or not revising their past affects the nowcasting exercises when re-estimating the seasonal factors of the indicators.
"Multilingual sentiment analysis in Economics and translation-induced distortions." With Julien Pinter (University of Alicante). Submitted to Economic Letters.
Abstract: Multilingual sentiment analysis is increasingly used in Economics, with the common approach being to machine-translate texts into English before applying Natural Language Processing (NLP) tools. Such translation can introduce distortions that affect the comparability of sentiment measures across languages. Using a unique dataset of economic articles professionally translated into five languages, we assess the empirical relevance of these distortions across several NLP tools. The severity of the distortions varies by tool and approach. Aggregating sentiment scores across texts reduces distortion and can render it negligible, supporting the use of multilingual sentiment analysis. Our results provide practical guidance for researchers working with cross-language sentiment indicators.