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Open-Economy models are central to the discussion of the trade-offs monetary policy faces in an increasingly more globalized world (e.g., Marínez-García & Wynne, 2010), but bringing them to the data is not without its challenges. Controlling for misspecification bias, we trace the problem of uncertainty surrounding structural parameter estimation in the context of a fully specified New Open Economy Macro (NOEM) model partly to sample size. We suggest that standard macroeconomic time series with a coverage of less than forty years may not be informative enough for some parameters of interest to be recovered with precision. We also illustrate how uncertainty also arises from weak structural identification, irrespective of the sample size. This remains a concern for empirical research and we recommend estimation with simulated observations before using actual data as a way of detecting structural parameters that are prone to weak identification. We also recommend careful evaluation and documentation of the implementation strategy (specially in the selection of observables) as it can have significant effects on the strength of identification of key model parameters.

We investigate the Bayesian approach to model comparison within a two-country framework with nominal rigidities using the workhorse New Keynesian open-economy model of Martínez-García and Wynne (2010). We discuss the trade-offs that monetary policy – characterized by a Taylor-type rule – faces in an interconnected world, with perfectly flexible exchange rates. We then use posterior model probabilities to evaluate the weight of evidence in support of such a model when estimated against more parsimonious specifications that either abstract from monetary frictions or assume autarky by means of controlled experiments that employ simulated data. We argue that Bayesian model comparison with posterior odds is sensitive to sample size and the choice of observable variables for estimation. We show that posterior model probabilities strongly penalize overfitting, which can lead us to favor a less parameterized model against the true data-generating process when the two become arbitrarily close to each other. We also illustrate that the spillovers from monetary policy across countries have an added confounding effect.

This volume of Advances in Econometrics is devoted to dynamic stochastic general equilibrium (DSGE) models, which have gained popularity in both academic and policy circles as a theoretically and methodologically coherent way of analyzing a variety of issues in empirical macroeconomics. The volume is divided into two parts. The first part covers important topics in DSGE modeling and estimation practice, including the modeling and role of expectations, the study of alternative pricing models, the problem of non-invertibility in structural VARs, the possible weak identification in new open economy macro models, and the modeling of trend inflation. The second part is devoted to innovations in econometric methodology. The papers in this section advance new techniques for addressing key theoretical and inferential problems and include discussion and applications of Laplace-type, frequency domain, empirical likelihood, and method of moments estimators.

The purpose of this paper is to describe the process through which an organization develops organizational capabilities by tapping the technical skills and social skills of its employees in the use of Building Information Modeling (BIM) to deliver lean and green project outcomes. The resulting framework for BIM-based organizational capabilities development comprising of three hierarchical layers – technology, process and outcomes – is explained.

For this study, BIM has been identified as an enabler and a process for achieving lean and green outcomes on construction projects. Based on a detailed literature review, this paper identifies the organizational capabilities needed by the architecture, engineering and construction organizations to effectively implement BIM on construction projects. The study has been conducted through a sequential mixed-method approach involving semi-structured interviews, focus groups and qualitative comparative analyses.

It was discovered that to attain desired project outcomes, an organization needs to embrace an underlying BIM adoption culture not only within its project teams but also within the organization as a whole. The study also concluded that an integrated approach to BIM usage – connecting it with lean and green initiatives – on construction projects resulted in improved project outcomes, especially ones targeting lean and green aspects of improvements.

The proposed outline for BIM-based organizational capabilities will help the organizations focus on the “human factors” along with the technical factors while striving for successful usage within their organizations.

Using the organizational capabilities matrix, this paper highlights the importance of technical and social skill sets of an individual employee and their role in developing the organizational capabilities to gain the desired lean and green outcomes.