QuERI is expert at working with limited datasets and filling missing data or developing estimates for countries lacking data. Pooled cross sectional time series models integrate data from multiple sources into a single equation.

QuERI relies upon a standardized method for filling in missing data and estimating data points for countries that report limited or no data. Generic models based on reported information from other countries is often used to develop estimation equations for filling in the missing data. One good example of how well this technique works is that QuERI was able to collect limited informaton from multiple sources on the market for office space and high-end hotels and using these techniques fill in a model and data base covering 80 countries showing vacancy rates, average rents, and other vital information. A similar technique was used to fill in missing data in data on tourism.

Pooled data sets have a key advantage over time series data in that they allow for changing structural elements to develop. Countries at a lower level of development can emerge quickly once certain minimum development stages are achieved. Time series models with out of date data for a single country cannot allow for this kind of rapid development. Thus pooled models are basically working off of a stage of development model.

When pooled models are based on a single companies data across multiple countries they identify differences in performance between countries after taking into account economic conditions and estimates of market size. This allows managers to determine the best strategies to use and to fairly rate performance across divisions.

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Modeling Methods -- Working with Limited Informaton