Forecasting GDP at the Regional Level with Many Predictors

Lehmann, Robert; Wohlrabe, Klaus

doi:10.1111/geer.12042

Cited by 32 publications

(15 citation statements)

References 41 publications

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“…Various econometric methods are applied to produce early estimates of economic growth using BCS indicators. Lehmann and Wohlrabe (2013) develop an autoregressive distributed lag (ADL) model with hard and soft statistics for forecasting GDP in German regions. D'Amato et al (2015) exercise the nowcasting of Argentinian GDP growth by using bridge equations and the dynamic factor model (DFM) with consumer surveys data.…”

Section: Literature Reviewmentioning

confidence: 99%

The relationship of economic sentiment and GDP growth in Russia in light of the Covid-19 crisis

Kitrar¹,

Lipkind²

2021

EBER

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The objective of the article is to prove the empirical and predictive value of the aggregate opinions of businesses and households for expanding cyclical macroeconomic data in Russia, especially during the coronavirus shocks. Research Design & Methods: We use qualitative information from surveys that cover about 24 000 organisations and 5100 households in all Russian regions. The total economic sentiment indicator (TESI) combines information on 18 survey-based indicators. Cross-correlation analysis, Hodrick-Prescott filtering, and a vector autoregressive (VAR) model with dummy variables are used as the research methods. Findings: The study confirms an almost synchronous cyclic conformity of the gross domestic product (GDP) growth and TESI dynamics for the period of 1998-2020. Probable GDP growth until the end of 2021 is estimated based on the expected impulses in the TESI dynamics, including those due to the sudden impact of the coronavirus. Implications & Recommendations: Assessments of business and household activity are reliable and available much earlier than quantitative statistics on GDP growth. Therefore, we advise to use them as an early warning system about economic growth and take them into account in policymaking. Contribution & Value Added: We are the first to confirm the effectiveness and reliability of TESI as a leading indicator of GDP growth in Russia, using data from large-scale business surveys and with a focus on crisis shocks. Article type:research article

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Section: Literature Reviewmentioning

confidence: 99%

The relationship of economic sentiment and GDP growth in Russia in light of the Covid-19 crisis

Kitrar¹,

Lipkind²

2021

EBER

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“…Most of the studies compare their methodology with benchmark models such as the univariate autoregressive process by using standard accuracy measures (e.g., root mean squared forecast errors). However, only a handful of articles offer statements about the statistical significance of forecast error differences (e.g., Lehmann and Wohlrabe, 2015or Kopoin et al, 2013. 8.…”

Section: State-of-the-art In Regional Economic Forecastingmentioning

confidence: 99%

Regional economic forecasting: state-of-the-art methodology and future challenges

Lehmann

Wohlrabe

2014

Econom. bus. letters

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Over the last decade, the topic of regional economic forecasting has become increasingly prevalent in academic literature. The most striking problem in this context is data availability at a regional level. However, considerable methodological improvements have been made to address this problem. This paper summarises a multitude of articles from academic journals and describes state-of-the-art techniques in regional economic forecasting. After identifying current practices, the article closes with a roadmap for possible future research activities.

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“…Regional models could be centred on one or few regions and called uni-regional models, or they could be based on all regions of a specific national level and called multi-regional models. Among the extensive literature, we can refer to the recent works of Mayor et al (2007), Lehmann and Wohlrabe (2015) and Kopoin et al (2013) for the uni-regional models and to the work of Baltagi et al (2014) for the multiregional models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the sectoral GVA of a small Spanish region

Lampis

2016

Econom. bus. letters

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Our main goal in this paper is to evaluate the point forecasting accuracy of several time series econometric models when applied to a small Spanish region. The variable of interest is the sectoral GVA of the Basque Country. The results support the use of causal models, which outperform univariate models, such as ARMA and SETAR, in forecasting accuracy. The use of a causal model, such as a simple Dynamic Regression model, offers a systematic advantage in the case of a small regional economy for which abundant regional statistical information is available.

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Forecasting GDP at the Regional Level with Many Predictors

Cited by 32 publications

References 41 publications

The relationship of economic sentiment and GDP growth in Russia in light of the Covid-19 crisis

The relationship of economic sentiment and GDP growth in Russia in light of the Covid-19 crisis

Regional economic forecasting: state-of-the-art methodology and future challenges

Forecasting the sectoral GVA of a small Spanish region

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