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|Data Vintages and Measuring
Forecast Model Performance
|Valuation Models for
|John C. Robertson
Ellis W. Tallman
The data on economic variables are usually estimates, and these estimates may be revised many times after their initial publication. Most historical forecast evaluation exercises use the ?latest available? or most recently revised vintage of historical data when constructing the forecasts?that is, they use estimates that may well have been unavailable to a forecaster in real time. Evaluations using such data could thus give a misleading picture of the forecast performance that can be expected in real-time situations. This fact is particularly relevant if a forecasting model?s performance is to be compared with that of published real-time forecasts. One practical question is whether actually using the data set available to a forecaster in real time would lead to inferences that are substantially different from those made using the latest available vintage of data. A related question is whether it matters which vintage of data the forecasts are evaluated against.
The authors argue that the choice of data vintage can have both a quantitative and a qualitative influence on forecast and model comparisons, at least over short horizons. This influence is illustrated by examining the performance of the composite index of leading indicators as a forecaster of alternative measures of real output. However, more research is required in order to determine whether the results generalize to forecasts of other series that are subject to revision, such as the various money aggregate measures.
Valuing financial securities often assumes that the contractual obligations of the security are going to be honored. However, frequently a party to a contract will default on its obligations. Because the contractual features of defaultable securities are usually complex and it is difficult to find comparable securities for which to observe prices, valuation requires formal models that take into account the security?s complexities and the uncertainties surrounding future cash flows. Many financial institutions hold large amounts of these securities in their portfolios, and it is important that these institutions have a reliable estimate of the resulting credit exposure. Understanding the strengths and drawbacks of various modeling approaches is also important for implementing prudent risk-management policies to manage credit exposures.
The author of this article reviews developments in valuation models for defaultable securities dating back to Merton (1974), concluding that although researchers have improved considerably on the basic Merton framework, problems remain. For example, many of the institutional features of bankruptcy and defaults, such as rescheduling of debts, cannot be readily incorporated in the models discussed without making the models intractable. He points out the need for the next generation of valuation models to incorporate at least some institutional features and be able to use the historical probabilities of defaults and credit rating changes without making unnecessarily strong assumptions.
|Venture Capital Investment:
Emerging Force in the
|Index for 1998|
Phillip Todd Parker
Venture capital investment throughout the United States and in the Southeast in particular has grown dramatically in recent years. Pensions funds, bank holding companies, insurance companies, investment banks, and nonfinancial institutions all invest venture capital in pursuit of high returns and as a means of diversifying investment risks. However, returns from such investment have been mixed over the industry?s relatively short history. As more and more large institutional investors pour increasing amounts of assets into venture capital and as state and local governments seek to attract this capital and the industries it fosters, the potential benefits will grow, but not without raising public policy issues.
This article examines the history, structure, and evolution of the national venture capital industry and then focuses on current developments in the Southeast, including state policies promoting such investment. The authors observe that venture capital, starting from a small base just a few years ago, has become an integral part of new business formation in the Southeast. They note that clear evidence on the impact of state venture capital support is lacking and the role of public support of funds and projects may still be questioned. Nonetheless, they conclude, new technological advances, business opportunities, and entrepreneurial needs should continue to spur development of the venture capital industry in the region.