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Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

In this paper the authors aim to investigate the importance of factors for the adoption of free/libre open source software (FLOSS) in the public sector. They seek to evaluate how different factors impact during the initiation and implementation phases of the adoption process.

The authors base the methodological approach on two exploratory case studies with a contrasting result logic. They build a multi‐level framework grounded both on literature review, and feedback from stakeholders. They then apply the framework to two case studies to better frame the findings. They consider phases of adoption (initiation, implementation) and the levels of adoption (technological, organizational, environmental, individual).

In the case studies, the authors found the importance of a strong and decision‐centric management board to give the impulse for the initiation phase of the process. As perceived by the stakeholders, a strong governmental support is of paramount importance to increase the adoption at the public level, although in the case studies examined the initiation stage started from the impulse of a championing management. Both case studies passed the initiation phase successfully. Continuous employees' training, organizational objectives consensus, and business process reengineering have been found important for the implementation phase. In the case study in which these factors were not in place, the implementation phase of adoption failed. Environmental factors – although relevant for the initiation of the adoption process – are less significant during the actual implementation of the adoption process, as the contrasting result logic from the case studies shows.

The study refers to two public organizations in a specific environmental setting. No causality among factors has been inferred. Quantitative objective data have been used to determine the success of adoption, for qualitative data multiple sources have been used when possible to limit threats to validity.

The framework can be used by stakeholders in public organizations to better frame their adoption strategies and to compare results across institutions. Lessons learnt from the case studies can be useful to drive future adoptions of FLOSS.

The framework combines phases of adoption and levels making it possible to frame the analysis of the case studies. It has been operationalized with a set of metrics, and with a protocol for the case studies to increase replicability value.

The purpose of this paper is to define suitable tracers that are particularly significant in assessing the impact on the surrounding environment caused by a steel making plant.

The authors focussed on the detection and quantification of inorganic pollutants such as heavy metals in the soil and in the air in the surrounding area of the steel making plant. The presented data, concerning a plant in the North part of Italy, come from an approach that took into account the advantages of different devices and sampling criteria: apart from conventional devices, two kinds of deposimeters have been used (for characterizing either total or wet/dry depositions). Their locations have been chosen taking into account the distance from the plant.

The paper demonstrates that the presence of diffused emissions plays an important (negative) role still today; thus a modern plant should reduce this kind of emission in order to be accepted from the population living in the surroundings. In the analyzed case study, the benefits of adopting the best available technologies can be seen from the temporal variability of the incidence of the plant.

The value of the paper is in the support that it can give to decision makers that must manage a steel making plant in an area.

This chapter proposes a simple, fairly general, test for global identification of unconditional moment restrictions implied from point-identified conditional moment restrictions. The test is a Hausman-type test based on the Hausdorff distance between an estimator that is consistent even under global identification failure of the unconditional moment restrictions, and an estimator of the identified set of the unconditional moment restrictions. The proposed test has a χ2 limiting distribution and is also able to detect weak identification. Some Monte Carlo experiments show that the proposed test has competitive finite sample properties already for moderate sample sizes.

The purpose of this paper is to contribute to the qualitative and quantitative knowledge on ultrafine particles in air near a steel making plant located in an Italian site.

A combination of experimental methodologies was used for the online and offline monitoring and chemical characterization of particulate matter (PM) in the air near the plant. Two unfiltered twin-sampling systems were adopted, working when the plant was on/off. All condensed air samples were submitted to Ion Chromatography analysis. The same samples were submitted to acid digestion before Graphite Furnace Atomic Absorption Spectroscopy Analysis. Continuous daily PM10 samples were collected to characterize ambient air. PM10 samples were also analysed to estimate metals content. The PM size distribution was achieved by continuous online monitoring. The adopted ultrafine particulate monitor classifies particles in the range 20-200 nm. The overall size distribution was inferred from an Optical Particulate Counter able to classify particles in the range 0.3-10 µm.

The obtained results show that no causal relationship can be found between the measurements of anions and metal in the air near the plant under investigation and the presence of the steel making plant. The trend in emissions of micro-particles was found quite characteristic of similar semi-urban areas.

The paper demonstrates that a steel making plant adopting best available techniques could have a local impact compatible with the surrounding environment.

Who works, how much one works, and what one earns are the cornerstones of labor economics. However, determining the answers to these questions can be tricky because many factors are involved in estimating labor supply, explaining the implications of labor demand, and determining the resulting earnings. This volume contains 13 chapters on these components of the labor market. Five deal directly with labor supply; four deal with labor demand, most notably the effect of cyclical demand fluctuations; and the remaining four deal with compensation, particularly wages, wage distributions, and fringe benefits.

Understanding how worker well-being is distributed across the population is of paramount importance. With such knowledge policy makers can devise efficient strategies to improve social welfare. This volume contains 13 chapters on topics enhancing our comprehension of inequality across workers. The issues addressed deal directly with the economic institutions that affect individual and family earnings distributions. The themes explored include job training, worker and firm mobility, minimum wages, wage arrears, unions, collective bargaining, unemployment insurance, and schooling. Among the questions answered are: To what extent do greater work hours of women mitigate the widening family earnings distribution? To what extent does deunionization widen the distribution of earnings? Do computers really cause a widening of the earnings distribution? How would the Russian wage distribution change if one accounted for wage arrears? How much of job creation and job destruction comes about because of business relocation? To what extent does maternal education increase children's education? Why do increases in the minimum wage fail to substantially decrease employment as economic theory would predict? And, to what extent do job skills matter for low-income workers?