Modeling spatial and temporal variations in tourism-related employment in Michigan

There were two primary focuses of this study. First, trend and seasonal patterns of monthly tourism-related employment in Michigan between January 1974 and December 1984 were identified. Second, alternative short term forecasting models for predicting monthly tourism related employment were developed and compared at both state and regional levels.;Michigan's tourism related employment grew by 25% over the 1974-1979 period, dropped 7% between 1980 and 1982, and subsequently recovered at a 4% annual growth rate in 1983 and 1984. Through the year statewide employment fluctuates plus or minus 6% around its annual average. Statewide seasonal patterns are stable over the eleven year period. Regional differences were found in both growth rate and seasonal fluctuations especially between northern tourism-dependent regions and more populous southern regions.;Structural and time series models were estimated and compared based upon forecast accuracy for each region. They have the same seasonal component but different trend components (i.e. either a structural or a time series trend component). The performance of each model, therefore, depends primarily on its trend component.;All forecasting models fit the data well. Structural models based upon economic variables forecast better for three northern tourism-dependent regions. Time series models based upon time factors forecast somewhat slightly better for the other five regions. The statewide models do not generalize well to northern regions because of differences between regional and statewide patterns. Differences exist in both trend and seasonal components.;Nine study regions were formed, the state of Michigan and eight sub-regions. A general analysis and forecasting procedure was applied to each study region. Multiplicative decomposition was adopted to separate the trend and seasonal components of each series. Trend and seasonal patterns were then identified from these components. Trend models were estimated from trend components using transfer function techniques; seasonal models were estimated from seasonal components using harmonic analysis. Forecasting models were then created by combining trend and seasonal models for each region.