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In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

The paper sets forth and examines the assumptions underlying two global ideas – world class and best practices – and their application to (higher) education and health organizations. Our basic (ex-ante) assumption is that both sectors are influenced by organizational fields that embody these ideas. However, we also assume that these sectors differ, and thus, that one should find between sector variations in the influence of such ideas. The findings suggest that both sectors have been affected by hegemonic ideas, yet in rather different ways, and that these ideas, particularly the metrics being used, pose different challenges in the two sectors.

This chapter focuses on the circumstances under which active clients in universities construct external management consultants as actors. Much research focuses on how consultants legitimize decisions and trends in business organizations, but we know little about how consultants become legitimized as actors in other organizational fields. In the academic field, clients are embedded in a variety of organizational settings embedded in different institutional logics, which determine their sense making. By analyzing how consultants are legitimized, the authors contribute to a better understanding of the organizational preconditions that support the construction of an external expert as an actor. By focusing on IT and strategy consulting in academia, further, the authors discuss the role of competing institutional logics in legitimization processes and the importance of intra-organizational communities.