BOOK REVIEW

Emerging Financial Markets, by David O. Beim and Charles W. Calomiris. McGraw‐Hill/Irwin, 2001, 364 pages, price $75.00.     

Are We Better Off if Our Politicians Have More Information?

The model of public policy studied in this paper has heterogeneous citizens/voters and two public goods: one (roads) chosen directly by an elected policy‐maker, and the other (pollution) stochastically dependent on the amount of roads. Both a one‐country and a two‐country version of the model are analyzed; the latter displays externalities across the countries which create incentives for free riding and strategic delegation. The welfare effects of providing the policy‐maker with information about the relationship between roads and pollution are investigated, and it is shown that more information hurts some—sometimes even all—citizens. In particular, the opportunity not to create an institution for information gathering can serve as a commitment device for a country, although with the unfortunate effect of making the overall outcome even worse. Implications for the welfare effects of “informational lobbying” are also discussed.     

From T-Bills to Stocks: Seasonal Anomalies Revisited

This paper employed eleven data series which consist of stocks, bonds, bills, equity premiums, term premiums, and various default premiums to investigate whether January seasonality reported in existing literature is robust across different states of the economy as this has important trading implications. For the periods 1926–1990, small stocks, small stock premiums, low grade bonds, and default premiums (spread between high grade, low grade and government bonds) reveal January seasonality and that the seasonality is robust across different states of the economy except for low grade bond returns and default premiums. January seasonality for low grade bond returns and low grade bond default premiums are primarily driven by results found during periods of economic expansion. Overall, January seasonality is more evident during the economic expansion periods although the magnitude of default premiums is larger during periods of economic contraction. Furthermore, prior findings of strong summer equity returns are primarily driven by the results found during the periods of economic contraction. It is also found that equity returns are generally higher during periods of economic expansion.     

A reappraisal of the forecasting performance of corn and soybean new crop futures

Forecasting performance of December corn and November soybean futures contracts during the previous spring was evaluated using the commonly specified price-level and percent-change models. These models invoke different assumptions regarding stationarity. Using Stein's analytical framework, results for the price-level model suggest avoidable social loss existed in the soybean market since 1973, because November futures provided biased forecasts. Regression R²s for both corn and soybeans declined substantially between 1952–1972 and 1973–1997, suggesting total social loss increased. By contrast, results from the percent-change model suggest only unavoidable social loss existed in the corn and soybean markets, because the futures provided unbiased forecasts. R² increased for corn but declined for soybeans, suggesting unavoidable social loss declined for corn, but increased for soybeans. The important, conflicting nature of the results from the two models underscores the importance of examining alternative model specifications when evaluating price forecasting performance. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 604–618, 1999     

Technological Threat and Opportunity Assessment

One of the major challenges in the management of innovation is a practical and useful implementation of technology forecasting. This article proposes the concept of aniticpating the technological future, and that a structured approach to this concept could be an invaluable aid to technical decision-making. The notion of technological threat and opportunity assessment is presented as a useful framework for anticipating technological change. This notion is based on a dual approach.Firstly, a rapidly changing global technological landscape necessitates keeping track of technological developments. However, since we are dealing with innovation (rather than mere invention), the market implications are as important as the technological ones and have to be accounted for as such. Secondly, any organisation could be considered to be technology-based to some or other degree, implying that technologies have the ability to affect the bottom line of the organisation in some way. It is thus required to assess the business impact of such technologies, typically through a technology or innovation audit.Having assessed specific technological threats and opportunities facing the organisation, an innovation strategy needs to be developed in response to the identified threats and opportunities. Various possible offensive and/or defensive responses should be considered, culminating in the selection and implementation of an optimal strategy.     

Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling

In cross-national longitudinal studies it is often impossible to administer the same measurement instruments at the same occasions to all sample units in all participating countries. This quickly results in large quantities of missing data, due to (a) missing measurement instruments in some countries, (b) missing assessment waves within or across countries, (c) missing data for individual sample units. As compared to cross-sectional studies, the problem of missing values is further aggravated by the fact that missing values are always associated with different time intervals between repeated observations. In the past, this has often been dealt with by the use of phantom-variables, but this approach is limited to simple designs with few missing value patters. In the present paper we propose a new way to think of, and deal with, missing values in longitudinal studies. Instead of conceiving of a longitudinal study as a study with \(T\) discrete time points of which some are missing, we propose to conceive of a longitudinal study as a way to measure an underlying process that develops continuously over time, but is only observed at some selected discrete time points. This transforms the problem of missing values into a problem of unequal time intervals. After a quick introduction to the basic idea of continuous time modeling, we demonstrate how this approach provides a straightforward solution to missing measurement instruments in some countries, missing assessment waves within or across countries, and missing data for individual sample units.