Forecasting (aggregate) demand for US commercial air travel

Carson, Richard T.; Cenesizoglu, Tolga; Parker, Roger

doi:10.1016/j.ijforecast.2010.02.010

Cited by 64 publications

(23 citation statements)

References 21 publications

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“…Jørgensen (2004) found that when seven teams of experts forecast project completion times, the errors of bottom-up forecasts were 49 percent smaller than the errors of direct forecasts. Carson, Cenesizoglu, and Parker (2011) forecast total monthly U.S. commercial air travel passengers for 2003 and 2004. They estimated an econometric model using data from 1990 to 2002 in order to directly forecast aggregate passenger numbers.…”

Section: Decompose the Problem To Best Use Knowledge Information Anmentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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This paper proposes a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The Golden Rule is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek all knowledge relevant to the problem, and use methods that have been validated for the situation. A checklist of 28 guidelines is provided to implement the Golden Rule. This article's review of research found 150 experimental comparisons; all supported the guidelines. The average error reduction from following a single guideline (compared to common practice) was 28 percent. The Golden Rule Checklist helps forecasters to forecast more accurately, especially when the situation is uncertain and complex, and when bias is likely. Nonexperts who know the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data have led forecasters to violate the Golden Rule. As a result, despite major advances in forecasting methods, evidence that forecasting practice has improved over the past half-century is lacking.

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Section: Decompose the Problem To Best Use Knowledge Information Anmentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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“…For instance, in a VAR our suggestion implies to impose zero restrictions on the coefficients of the disagggregates in the aggregate equation. In contrast to the empirical approach implemented in Carson, Cenesizoglu & Parker (2007) and Zellner & Tobias (2000), who impose the parameters of all or most of the disaggregates to be identical across the individual empirical models, we suggest imposing either zero restrictions on disaggregate parameters in the aggregate model or imposing a factor structure, where the weights of the disaggregates in the factor maximize their explained variance. Granger (1980Granger ( , 1987 considers correlations among the disaggregates due to a common factor.…”

Section: Introductionmentioning

confidence: 99%

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

Hendry

Hubrich

2011

Journal of Business & Economic Statistics

136

109

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in WO R K I N G PA PE R S E R I E S N O 1155 / F E B R UA RY 2010This paper can be downloaded without charge from http://www.ecb.europa.eu or from the Social Science Research Network electronic library at http://ssrn.com/abstract_id=1551223In 2010 all ECB publications feature a motif taken from the €500 banknote. Abstract 4 Non-technical summary 5 COMBINING DISAGGREGATE FORECASTS OR COMBINING

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“…Dengan meningkatnya laju pertumbuhan penumpang udara akan memerlukan peningkatan kapasitas bandar udara. (Carson et al, 2010) berpendapat bahwa prediksi terhadap potensi perjalanan lalu lintas udara merupakan input yang sangat penting dalam pengambilan keputusan baik dari prespektif ekonomi, penelitian dan pengembangan terhadap desain pesawat, perencanaan proses produksi maupun dalam perencanaan penyediaan infrastruktur bandara.…”

Section: Tinjauan Pustakaunclassified

Modal Share Dalam Demand Forecasting di Bandara Internasional Jawa Barat (BIJB) Kertajati Majalengka

Yuliawati¹

2017

war. ardhia

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Modal Share Dalam Demand Forecasting di Bandara Internasional Jawa Barat (BIJB) Kertajati Majalengka, (Eny Yuliawati) 173The province of West Java, with 3,709,528.44 hectares area and 35.72 Provinsi Jawa Barat, dengan wilayah daratan seluas 3.709.528,44 Ha dan jumlah penduduk sebanyak 35,72 juta jiwa, memiliki potensi sumber daya alam maupun sumber daya manusia yang sangat maju. Potensi tersebut tentu membutuhkan dukungan infrastruktur transportasi, salah satunya adalah dengan keberadaan bandara yang memadai. Saat ini keberadaan Bandara Husein Sastranegara semakin padat, sehingga mendorong Pemerintah Daerah untuk menginisiasi pembangunan bandara baru di Kertajati Majalengka. Penelitian ini bermaksud mengembangkan model demand forecasting berdasarkan modal share dalam rangka mendukung akselerasi pembangunan Bandara di Kertajati Jawa Barat. Analisa prediksi permintaan penumpang ini didasarkan pada modal share data Origin-Destination di 9 wilayah kabupaten/kota yang diasumsikan berada dalam cakupan pelayanan Bandara Kertajati dengan mempertimbangkan pengaruh variabel ekternal berupa variabel sosioekonomi pada masing-masing wilayah prediksi. Selain itu pendekatan teknik stated preference juga digunakan untuk mengetahui preferensi penumpang angkutan udara di Bandara Husein Sastra Negara dengan adanya Bandara Baru di Kertajati. Teknik stated preference tersebut mempertimbangkan atribut-atribut seperti travel time, travel cost dan aksesibilitas. Hasil penelitian model demand forecasting penumpang angkutan udara berdasarkan pendekatan model share dan teknik stated preference diharapkan dapat digunakan untuk menganalisa prediksi potensi penumpang angkutan udara di bandara baru sehingga dapat mendukung akselerasi pembangunan BIJB di Kertajati Majalengka Jawa Barat.

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Forecasting (aggregate) demand for US commercial air travel

Cited by 64 publications

References 21 publications

Golden Rule of Forecasting: Be Conservative

Golden Rule of Forecasting: Be Conservative

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

Modal Share Dalam Demand Forecasting di Bandara Internasional Jawa Barat (BIJB) Kertajati Majalengka

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