Effects of Economic Factors on Demand for Luxury Hotel Rooms in the U.S.
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Bibliographic record
Abstract
The purpose of this study is to estimate the effects of economic \nfactors on the demand for luxury hotel rooms in the United \nStates during the 16-year period (1998 - 2013). The average daily \nrate of six types of hotel rooms, gross domestic product and two \nrecessions (2001 and 2007-2009) are considered as independent \nvariables in the sample of the time series data set of 192 points to \npredict luxury room night stays of customers by ex-post data. \nAutoregressive Distributed Lag Model is employed to select the \nbest model of luxury hotel demand on its determinants in the \nshort and long run relationships. Findings indicate that in the \nlong run, (1) the US residents would stay more nights in luxury \nhotels when their income increases; (2) the Canadian and UK \nmight not visit or stay in the luxury hotels in the U.S. when their \nincome or luxury hotel price increases; and (3) the German, \nJapanese, Korean and Chinese visitors would stay in the luxury \nhotels in the U.S. when their incomes increase no matter what the \nluxury hotel price increases. In the short run, the Chinese, \nJapanese, and Korean might not stay in the luxury hotels in the \nU.S. when their income or hotel price increases. The English \nwould stay in the luxury hotels when their income or luxury \nhotel price increases. Finally, the two US economy recessions in \n2001 and 2007-2009 do not affect the demand for luxury hotel \nrooms in the long run.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it