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Exploring a researchable topic and narrowing it down sufficiently to make it workable is a first task in any scientific research. This is particularly difficult when the researcher is a novice, because s(he) is unlikely to be properly aware of what the essential issues and the research question(s) in the field are. This article addresses the question of how to navigate a research topic for an academic project. The article is potentially of interest to novice researchers and researchers new to a field. Illustrating its argument by means of an example in the area of knowledge management, the article proposes a set of guidelines for narrowing down a research topic to workable size. A number of recommendations are made; by utilizing these recommendations to construct a navigation map, it is hoped that a researcher can use fully formulate research question(s). It can be argued that drawing such a navigation map is an art in which prospective researchers need to be trained.

Alvin Hansen and John Williams’ Fiscal Policy Seminar at Harvard University is widely regarded as a key mechanism for the spread of Keynesianism in the United States. An original and regular participant, Richard A. Musgrave was invited to prepare remarks for the fiftieth anniversary of the seminar in 1988. These were never published, though a copy was filed with Musgrave’s papers at Princeton University. Their reproduction here is important for several reasons. First, it is one of the last reminiscences of the original participants. Second, the remarks make an important contribution to our understanding of the Harvard School of macro-fiscal policy. Third, the remarks provide interesting insights into Musgrave’s views on national economic policymaking as well as the intersection between theory and practice. The reminiscence demonstrates the importance of the seminar in shifting Musgrave’s research focus and moving him to a more pragmatic approach to public finance.

This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case, the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.

These moments of the asymptotic distribution of the least-squares estimator of the local-to-unity autoregressive model are computed using computationally simple integration. These calculations show that conventional simulation estimation of moments can be substantially inaccurate unless the simulation sample size is very large. We also explore the minimax efficiency of autoregressive coefficient estimation, and numerically show that a simple Stein shrinkage estimator has minimax risk which is uniformly better than least squares, even though the estimation dimension is just one.

We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the Stein-rule estimator of Hill and Judge (1987) that shrinks the unrestricted unbiased ordinary least squares (OLS) estimator toward a restricted biased principal component (PC) estimator. Since the Stein-rule estimator combines the OLS and PC estimators, it is a model-averaging estimator and produces a combined forecast. The conditions under which the improvement can be achieved depend on several unknown parameters that determine the degree of the Stein-rule shrinkage. We conduct Monte Carlo simulations to examine these parameter regions. The overall picture that emerges is that the Stein-rule shrinkage estimator can dominate both OLS and principal components estimators within an intermediate range of the signal-to-noise ratio. If the signal-to-noise ratio is low, the PC estimator is superior. If the signal-to-noise ratio is high, the OLS estimator is superior. In out-of-sample forecasting with AR(1) predictors, the Stein-rule shrinkage estimator can dominate both OLS and PC estimators when the predictors exhibit low persistence.

This chapter discusses the evolution of German views on public debt 1850–1920, referring to three strands of secondary literature: (1) German retrospectives on public finance, (2) the historical literature with a public choice perspective, and (3) contributions to public/constitutional law, mainly referring to Lorenz von Stein. The skeptic view of public debt endorsed by authors of the second half of the period is shown to be related to politico-economic issues of state agency combined with new state functions, rather than to the rejection of Dietzel’s Proto-Keynesian macroeconomic reasoning.

This chapter is a contribution to the intellectual history of the anxiety that full employment in the modern United States depended somehow on military spending. This discourse (conveniently abbreviated as “military Keynesianism”) is vaguely familiar, but its contours and transit still await a full study. The chapter shows the origins of the idea in the left-Keynesian milieu centered around Harvard’s Alvin Hansen in the late 1930s, with a particular focus on the diverse group that cowrote the 1938 stagnationist manifesto An Economic Program for American Democracy. After a discussion of how these young economists participated in the World War II mobilization, the chapter considers how questions of stagnation and military stimulus were marginalized during the years of the high Cold War, only to be revived by younger radicals. At the same time, it demonstrates the existence of a community of discourse that directly links the Old Left of the 1930s and 1940s with the New Left of the 1960s and 1970s, and cuts across the division between left-wing social critique and liberal statecraft.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.