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

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

doi:10.1080/13504851.2017.1361000

Cited by 5 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bottom-up approach is used in micro-level studies where IO and CGE models cannot be easily applied, for instance in the case of assessing economic and employment impacts at the local level (Cooper et al, 2016). Bottom-up approach has been used in a wide array of disciplines to estimate economic impacts, including the transport sector in Austria (Pfurtscheller and Genovese, 2018), the energy sector in Germany (J€ ager et al, 2016), macroeconomics (Dias et al, 2018) and in the infrastructure sector (Ojha et al, 2020). Bottom-up models are more suitable for assessing socio-economic interactions in regional areas, where aggregated national-level economic data as a fundamental requirement in top-down models may not be relevant (Gajanayake et al, 2020) and have been used where economy-wide sectoral information is not available (Wiesenthal et al, 2012).…”

Section: Economic Impacts Of Maintenance Projectsmentioning

confidence: 99%

Application of a bottom-up approach to estimate economic impacts of building maintenance projects: cladding rectification program in Australia

Teo

Gajanayake

Jayasuriya

et al. 2021

ECAM

View full text Add to dashboard Cite

PurposeThis paper critically reviews economic impact assessment methods adopted in construction-related projects, to develop and present a novel bottom-up approach suitable to estimate regional economic impacts of building maintenance projects.Design/methodology/approachA thorough literature review of economic impact assessment in construction projects is carried out to identify the most relevant approach to estimate wider economic impacts of building maintenance projects. Based on these findings, a model based on the bottom-up approach to estimate wider economic impacts is developed. The applicability and face validity of the developed model is demonstrated through a case of cladding replacement program in Australia.FindingsThe literature review revealed that bottom-up models are better suited for estimating regional economic impacts of maintenance projects, given the challenges of obtaining micro-level economic data in the maintenance sector. In relation to the total economic impacts (direct and indirect), the results show that for every $1 of government spending on similar projects the Gross State Product would increase by $1.34. In terms of employment impact, over 70% of the direct economic value addition is driven by the increase in labour, where close to 3 FTE jobs will be required for each $1 million of spending on cladding replacement projects.Originality/valueThis paper presents a model to estimate the wider economic impacts of building maintenance projects, which is typically overlooked in the construction management field. The proposed model is developed to incorporate the variability of different building maintenance projects so that the economic impact resulting from these projects could be estimated more accurately. This model can be used by local government decision-makers to justify and prioritise maintenance projects in a similar manner to new construction projects.

show abstract

Section: Economic Impacts Of Maintenance Projectsmentioning

confidence: 99%

Application of a bottom-up approach to estimate economic impacts of building maintenance projects: cladding rectification program in Australia

Teo

Gajanayake

Jayasuriya

et al. 2021

ECAM

View full text Add to dashboard Cite

show abstract

“…The most commonly used measure of the economy is GDP. Forecasting GDP is subject of concern for [Dias 2017]. The estimation for forecasting manner of the factor models with aim to prognosticate some GDP integrants is pursued as well as the efficiency of a bottom-up approach to appraise the GDP growth.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation Modeling of the Czech Republic Economy With a Development Forecasting

Grešová¹,

Svetlík²

2021

MM SJ

View full text Add to dashboard Cite

The current uncertain and rapidly changing time requires a thorough analysis of the actual situation and subsequent appropriate decision-making in many areas. This applies to the individual national economies and their particular sectors, as well. Therefore, the topic of development forecasting is becoming even more important nowadays. The presented paper deals with forecasting associated to the further development of the selected national economy through modeling via simulation technique. The country of selection is the Czech Republic. The gross domestic product (GDP) is chosen as the indicator that the most reliably reflects the national economy condition. Therefore, a prediction of the potential GDP development is realized. The obtained findings are applied to the industry segment.

show abstract

“…Chan & Song (2018) indicated significant changes in the expectations, uncertainty about U.S. inflation during the Great Recession period. Dias, Pinheiro, & Rua (2018) found that factor models employing a bottom-approach for GDP is useful. Chang (2018) showed that Greenbooks (Board of Governors of the Federal Reserve System staff's forecasts) are more likely to under-predict real GDP growth with one quarter ahead forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

Kurihara¹,

Fukushima²

2019

AEF

View full text Add to dashboard Cite

This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries. The empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.

show abstract

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

Cited by 5 publications

References 35 publications

Application of a bottom-up approach to estimate economic impacts of building maintenance projects: cladding rectification program in Australia

Application of a bottom-up approach to estimate economic impacts of building maintenance projects: cladding rectification program in Australia

Simulation Modeling of the Czech Republic Economy With a Development Forecasting

AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

Contact Info

Product

Resources

About