Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts

Jansen, W. Jos; Jin, Xiaowen; Winter, Jasper M. de

doi:10.1016/j.ijforecast.2015.05.008

Cited by 55 publications

(23 citation statements)

References 42 publications

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“…Specialized forecast centres, which are equipped with modern methodological bases and access to databases of branch monitoring, are still periodically forced to solve the problem of choosing an effective forecast model and consolidating forecasts [20].…”

Section: Analysis Of Previous Studies and Statement Of The Problemmentioning

confidence: 99%

Formation of prognostic software support for strategic decision-making in an organization

Romanenkov

Vartanian

2016

EEJET

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FORMATION OF PROGNOSTIC SOFTWARE SUPPORT FOR STRATEGIC DECISION-MAKING IN AN ORGANIZATION Y u . R o m a n e n k o vPhD, Associate Professor* E-mail: KhAI.management@ukr.net V . V a r t a n i a n Doctor of Technical Sciences, Professor* E-mail: vartanyan_vm@ukr.net *Department of management M. E. Zhukovsky National Aerospace University «Kharkiv Aviation Institute» Chkalova str., 17, Kharkiv, Ukraine, 61070Проаналізовано фактори невизначеності у проце-сі прийняття стратегічних рішень і проведено порів-няльний аналіз традиційних статистичних моде-лей і методів прогнозування. Сформульовано основні завдання прогностичного забезпечення та обґрунтова-но необхідність розробки моделі прогностичного забез-печення підтримки прийняття стратегічних рішень для потреб організації. Запропоновано чотирирівневу модель системи із принципами її методичного насичен-ня, а також інструменти її налаштування Ключові слова: прогностичне забезпечення, під-тримка прийняття управлінських рішень, прогнозу-вання, комплексування прогнозних оцінок Проанализированы факторы неопределённости в процессе принятия стратегических решений и про-ведён сравнительный анализ традиционных ста-тистических моделей и методов прогнозирования. Сформулированы основные задачи прогностического обеспечения и обоснована необходимость разработки модели прогностического обеспечения поддержки при-нятия стратегических решений для нужд организа-ции. Предложена четырёхуровневая модель системы с принципами её методического насыщения, а также инструменты её настройки Ключевые слова: прогностическое обеспечение, поддержка принятия управленческих решений, про-гнозирование, комплексирование прогнозных оценок UDC 658.5:004.94

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Section: Analysis Of Previous Studies and Statement Of The Problemmentioning

confidence: 99%

Formation of prognostic software support for strategic decision-making in an organization

Romanenkov

Vartanian

2016

EEJET

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“…We refrain from choosing a particular combination of r and p to avoid potential misspecification and instability problems; see also Kuzin, Marcellino and Schumacher () and Jansen et al . ().…”

Section: Data Benchmark Model and Forecast Designmentioning

confidence: 97%

“…This holds for statistical models as well as professional forecasters; see for instance Stark () and Jansen et al . (). Estimation of the DFM and the subsequent calculation of forecasts happens recursively on the basis of the most recent 15 years of data on monthly indicators and quarterly real GDP.…”

Section: Data Benchmark Model and Forecast Designmentioning

confidence: 97%

“…Jansen et al . () found in their comparative multi‐country study that the DFM was the best statistical procedure overall, in particular for nowcasting and backcasting. Dynamic factor models have been applied to many countries, generally delivering relatively accurate macroeconomic forecasts…”

Section: Data Benchmark Model and Forecast Designmentioning

confidence: 99%

“…The evidence in Aiolfi, Capistrán and Timmermann (), Jansen et al . () and Liebermann () suggests that subjective forecasts by private sector analysts often embody valuable information that sophisticated mechanical forecasting procedures fail to pick up. Publicly available subjective forecasts thus seem to offer the potential of enhancing real‐time model‐based GDP forecasts and thus a better assessment of the current state of the economy.…”

Section: Introductionmentioning

confidence: 99%

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Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

Jansen

Winter

2018

Oxf Bull Econ Stat

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We investigate the effects of combining model‐based near‐term GDP forecasts and judgmental (quarterly) forecasts by professional analysts (Consensus forecasts) in a real‐time setting for the G7 countries over the years 1999–2013. Model‐based forecasts are produced by a dynamic factor model (DFM). We consider as combination schemes the weighted average and the linear combination. Combining with subjective information delivers sizable gains in forecasting ability of statistical models for all countries except Japan, even when subjective forecasts are somewhat dated. Accuracy gains are much more pronounced in the volatile period after 2008 due to a marked improvement in predictive power of Consensus forecasts relative to the DFM. A possible explanation is that mechanical models may be more vulnerable to extreme observations in estimation samples. Consensus forecasts are superior at the moment of publication for most countries since 2008. For some countries forecast combinations can improve upon Consensus forecasts in between the quarterly release dates of the Consensus survey.

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Step by Step—A Quarterly Evaluation of EU Commission's GDP Forecasts

Heinisch

2024

Journal of Forecasting

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The European Commission's growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multiperiod ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first‐release and current‐release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short‐time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real‐time data or pseudo–real‐time data and these differences do not significantly impact the overall assessment of the forecasts' quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.

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Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts

Cited by 55 publications

References 42 publications

Formation of prognostic software support for strategic decision-making in an organization

Formation of prognostic software support for strategic decision-making in an organization

Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

Step by Step—A Quarterly Evaluation of EU Commission's GDP Forecasts

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