The Daily Economic Indicator: tracking economic activity daily during the lockdown

Lourenço, Nuno; Rua, António

doi:10.1016/j.econmod.2021.105500

Cited by 28 publications

(25 citation statements)

References 24 publications

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“…(2020) follow their approach and construct a FEARS index for Germany 4 . Closely related to the macroeconomic context of our paper, Lourenço and Rua (2021) use a latent variable of several daily series within a factor model framework to develop a daily economic indicator for Portugal.…”

Section: Related Researchmentioning

confidence: 99%

Obtaining consistent time series from Google Trends

et al. 2021

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Google Trends data are a popular data source for research, but raw data are frequency‐inconsistent: daily data fail to capture long‐run trends. This issue has gone unnoticed in the literature. In addition, sampling noise can be substantial. We develop a procedure (available in an R‐package), which solves both issues at once. We apply this procedure to construct long‐run, frequency‐consistent daily economic indices for three German‐speaking countries. The resulting indices are significantly correlated with traditional leading economic indicators while being available in real time. We discuss potential applications across disciplines and spanning well beyond business cycle analysis.

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Section: Related Researchmentioning

confidence: 99%

Obtaining consistent time series from Google Trends

et al. 2021

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“…Substantially that urban transformation is researched also as indicators of economic change [66][67][68], therefore, it provides guidance for future research directions to study urban transformation as an indicator of economic changes during Covid-19.…”

Section: Economic Development and Covid-19 Impactmentioning

confidence: 99%

Exploring Sustainable Urban Transformation Concepts for Economic Development

Miķelsone

Atstāja²,

Koval³

et al. 2021

EEA

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Nowadays cities face numerous challenges amplified to build necessary urban municipal and community capacity to ensure sustainability transformation to respond to the local and global challenges of climate change, inequality, and access to resources. This research combines the study fields of sustainability, economic development, governance of sustainability transformation, providing the multidisciplinary approach as a systemic-oriented view encompassing the social, technological and ecological aspects of urban transformation. The purpose of the paper is to explore how the concept of urban transformation could be operationalized for research of economic development under the economic strain assuming the emergency of Covid-19 grand challenge. The research methods used are a systematic literature review and the content analysis. The paper provides a detailed characterization of the urban transformation exploring this concept from the structure and system perspectives for the economic exit from the crisis.

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“…Delle Monache, Emiliozzi, and Nobili, 2020; Eraslan and Götz, 2020;Fenz and Stix, 2021;Wegmüller, Glocker, and Guggia, 2021). Lourenço and Rua (2021) develop a daily variant (DEI) for the Portuguese economy. Woloszko (2020) constructs country-specific economic indicators for 46 countries based on Google Trends data.…”

Section: Motivationmentioning

confidence: 99%

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

Ollech

2021

SSRN Journal

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Research QuestionFrom the start of the COVID-19 pandemic, more timely and granular information on the state of the economy was sought after. To this end, indices based on higher frequency time series have been produced in order to arrive at faster and more precise assessments of economic developments. The series used in the construction of these economic indices often present methodological challenges that differ substantially from those encountered in lower frequency data. We analyse the characteristics of higher frequency time series that cause these methodological challenges in the context of seasonal adjustment. ContributionWe analyse a characteristic set of higher frequency time series that are used as inputs to the weekly activity index (WAI) published by the Deutsche Bundesbank since the beginning of the COVID-19 pandemic. The contribution of this paper is twofold. First, we seasonally adjust daily truck toll data, hourly electricity consumption data and weekly Google Trends data on unemployment. We discuss which differences to lower frequency time series are relevant for seasonal adjustment in general and in light of the COVID-19 pandemic. Second, we develop a taxonomy of the central features of seasonal higher frequency time series. ResultsThe identified central features can be clustered into three groups of characteristics: basic time series characteristics, periodic and calendar effects, and outliers and missing values. This list of features contributes to the evaluation of the seasonal adjustment of higher frequency time series and should inform the development of quality diagnostics. Nichttechnische ZusammenfassungFragestellung Mit Beginn der COVID-19 Pandemie stieg der Bedarf an frühzeitigeren und detaillierteren Informationen zur gesamtwirtschaftlichen Lage. Daher wurden verschiedene Indizes entwickelt, die höherfrequente Zeitreihen nutzen, um die gesamtwirtschaftliche Lage schneller und präziser einschätzen zu können. Die Nutzung der diesen Indizes zugrundeliegenden Zeitreihen ist allerdings mit methodischen Herausforderungen verbunden, die sich substanziell von denen bei niedrigerfrequenten Zeitreihen unterschieden. Wir analysieren die Charakteristika von höherfrequenten Zeitreihen, die die Ursache für solche methodischen Herausforderungen im Rahmen der Saisonbereinigung sind. BeitragWir untersuchen einige typische höherfrequente Zeitreihen, die seit Beginn der COVID-19 Pandemie Eingang in den Wöchentlichen Aktivitätsindex (WAI) der Deutschen Bundesbank finden. Der Beitrag dieser Untersuchung liegt erstens in der Saisonbereinigung täglicher LKW-Maut-Fahrleistungsdaten, stündlicher Energieverbrauchsdaten und wöchentlicher Google Trends Daten zur Arbeitslosigkeit. Wir zeigen dabei, welche Unterschiede zu niedrigerfrequenten Zeitreihen für die Saisonbereinigung allgemein und vor dem Hintergrund der COVID-19 Pandemie relevant sind. Zweitens entwickeln wir eine Taxonomie der zentralen Eigenschaften von saisonalen höherfrequenten Zeitreihen. ErgebnisseDie identifizierten zentralen Eigenschaften lassen ...

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The Daily Economic Indicator: tracking economic activity daily during the lockdown

Cited by 28 publications

References 24 publications

Obtaining consistent time series from Google Trends

Obtaining consistent time series from Google Trends

Exploring Sustainable Urban Transformation Concepts for Economic Development

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

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