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This chapter provides a discussion of Roger Slee and Julie Allan’s 2001 article “Excluding the included: A reconsideration of inclusive education” published in International Studies in Sociology of Education. “Excluding the included” is a salient example of the influential work of these two scholars, threads of which can be found throughout their prior and following work, and in the work of other scholars in the area. The importance of the work and its ongoing impact on the field of inclusive education is discussed.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

This paper is an initial attempt to discuss the American institutionalist movement as it changed and developed after 1945. Institutionalism in the inter-war period was a relatively coherent movement held together by a set of general methodological, theoretical, and ideological commitments (Rutherford, 2011). Although institutionalism always had its critics, it came under increased attack in the 1940s, and faced challenges from Keynesian economics, a revived neoclassicism, econometrics, and from new methodological approaches derived from various versions of positivism. The institutionalist response to these criticisms, and particularly the criticism that institutionalism “lacked theory,” is to be found in a variety of attempts to redefine institutionalism in new theoretical or methodological terms. Perhaps the most important of these is to be found in Clarence Ayres’ The Theory of Economic Progress (1944), although there were many others. These developments were accompanied by a significant amount of debate, disagreement, and uncertainty over future directions. Some of this is reflected in the early history of The Association for Evolutionary Economics.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which conditions. In doing so, the author generalizes the Pesaran and Timmermann (2005)’s forecast error decomposition and shows that it depends on four terms: (1) a period ahead risk; (2) a bias due to a conditional mean shift; (3) a bias due to a variance mismatch; (4) a gap term valid only conditionally. The author also derives new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, the author introduces new simulation-based estimators for the finite sample forecast properties which are based on the derived decomposition. The author’s finding points out that, in some cases, parameter instability can be neglected by extending the window backward and forecasters can be insured against higher forecast risk under this model class as well, generalizing Pesaran and Timmermann (2005)’s result. The author’s result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.