The Construct of the Valuing of Knowledge and Personal Consumption Expenditure in the U.S. National Accounts 1929–1989

Purpose This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy. Design/methodology/approach Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue. Findings All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues. Practical implications Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments. Originality/value It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

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International Comparison of Growth in Demand for a New Durable Consumer Product

Journal of Marketing Research ◽

10.1177/002224378201900309 ◽

1982 ◽

Vol 19 (3) ◽

pp. 364-371 ◽

Cited By ~ 7

Author(s):

Bertil C. Lindberg

Keyword(s):

International Comparison ◽

Consumer Product ◽

Consumption Expenditure ◽

Relative Demand ◽

Personal Consumption

The author introduces relative demand, defined as the proportion of a country's personal consumption expenditure spent on a good, and describes relative demand as a function of the level of saturation of the good. He compares parameters of these functions among countries and discusses their use in predicting demand.

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Forecast densities for economic aggregates from disaggregate ensembles

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2012-0088 ◽

2014 ◽

Vol 18 (4) ◽

Cited By ~ 12

Author(s):

Francesco Ravazzolo ◽

Shaun P. Vahey

Keyword(s):

Moving Average ◽

Forecast Combination ◽

Consumption Expenditure ◽

Post Processing ◽

Continuous Ranked Probability Score ◽

Personal Consumption ◽

Linear Opinion Pool ◽

Processing Step ◽

Density Forecasting ◽

Opinion Pool

AbstractWe extend the “bottom up” approach for forecasting economic aggregates with disaggregates to probability forecasting. Our methodology utilises a linear opinion pool to combine the forecast densities from many disaggregate forecasting specifications, using weights based on the continuous ranked probability score. We also adopt a post-processing step prior to forecast combination. These methods are adapted from the meteorology literature. In our application, we use our approach to forecast US Personal Consumption Expenditure inflation from 1990q1 to 2009q4. Our ensemble combining the evidence from 16 disaggregate PCE series outperforms an integrated moving average specification for aggregate inflation in terms of density forecasting.

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