Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence

Dias, Francisco Craveiro; Pinheiro, Maximiano; Rua, António

doi:10.1016/j.econmod.2014.10.034

Cited by 26 publications

(16 citation statements)

References 23 publications

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“…The GDP forecast accuracy improves with progress in transition as well as with the expansion in the information domain (Krkoska and Teksoz 2007). The relative performance of several factor models to forecast GDP growth using a large monthly dataset was analyzed in article (Dias et al 2015). It was found that factor models outperform significantly the univariate autoregressive model for nowcasting and one-quarter ahead forecasting while for longer forecast horizons the gains are substantially reduced.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique

2016

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Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth forecasting based on the different factors. The main goal was to analyze the influence of science and technology factors on the economic growth. Gross domestic product (GDP) was used as economic growth indicator. The method of adaptive neuro fuzzy inference system (ANFIS) was applied to the data in order to select the most influential factors for the GDP growth rate forecasting. Ten inputs are considered: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of GDP growth rate.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique

2016

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“…Concerning the data, we resort to the updated dataset compiled by Dias et al (2015) for the Portuguese economy which comprises 126 series. It includes both hard and soft data covering business and consumers surveys (43 series), retail sales (4 series), industrial production (7 series), turnover in industries and services (20 series), employment, hours worked and wage indices in industries and services (24 series), tourism nights spent in Portugal (3 series), car sales (3 series), cement sales, vacancies and registered unemployment (5 series), energy consumption (3 series), nominal exports and imports of goods (10 series), real effective exchange rate, Portuguese stock market index and ATM/POS series.…”

Section: Preliminariesmentioning

confidence: 99%

“…Herein, we extend the work by Dias et al (2015) and assess the usefulness of the above-mentioned factor models to forecast GDP components.…”

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confidence: 92%

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A bottom-up approach for forecasting GDP in a data-rich environment

Dias

Pinheiro

Rua

2017

Applied Economics Letters

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In an increasingly data-rich environment, the use of factor models for forecasting purposes has gained prominence in the literature and among practitioners. In this article, we extend the work of Dias, Pinheiro and Rua (2015) by assessing the forecasting behaviour of factor models to predict several GDP components and investigate the performance of a bottomup approach to forecast Portuguese GDP growth. We find supporting evidence of the usefulness of factor models and noteworthy forecasting gains when conducting a bottomapproach drawing on the main aggregates of GDP.

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“…When looking at the European economy, it can be acknowledged that the impact of the crisis on some member states (e.g. Portugal, Spain and Greece) was severe, whereas others (including Germany and France) went through the crisis in a relatively mild way (Dias, Pinheiro, & Rua, 2018;Ozturk & Sozdemir, 2015;Trabelsi, 2012;Aller & Grant, 2018;Neal & Garcia-Iglasias, 2013;Gibson, Hall, & Tavlas, 2012;Storm & Naastepad, 2015;Albonico et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Measuring and Assessing the Impact of the Global Economic Crisis on European Real Property Market

Mach¹

2019

Journal of Business Economics and Management

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Economic development of countries, regions or entities operating on the market is possible when favourable economic conditions outweigh adverse conditions. Examining the development of European economy, it is possible to observe this regularity in the majority of periods, i.e. the bull market had a more intense and longer impact on the economy in comparison with the bear market, enabling constant economic development of Europe. Globalisation, a relative ease of financing investment activity, financial over-liquidity, negative interest rates: all of them result in the fact that previously verified and correctly operating mechanisms and market models have become unpredictable. During the latest crisis, the European real property market lost some part of its potential. The loss varied in individual EU member-countries. Some of them coped with the crisis quite successfully, others managed to do so on the average, still others have felt its effects until today. Making use of regressive techniques, the author undertook to measure and assess the impact of the global crisis on the property market. Consequently, the effects and intensity of its impact regarding the number of issued building permits, number of the employed in construction industry, value of construction production and prices of real property were measured.

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Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence

Cited by 26 publications

References 23 publications

RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique

RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique

A bottom-up approach for forecasting GDP in a data-rich environment

Measuring and Assessing the Impact of the Global Economic Crisis on European Real Property Market

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