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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.

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.

In this paper we present a model‐based approach for the development of physical hypermedia applications, i.e. those mobile (Web) applications in which physical and digital objects are related and explored using the hypermedia paradigm. We describe an extension of the Object‐Oriented Hypermedia Design Method (OOHDM) and present an improvement of the popular Model‐View‐Controller (MVC) metaphor to incorporate the concept of located object we illustrate the idea with a framework implementation using Jakarta Struts. We first review the state of the art of this kind of software systems, stressing the need of a systematic design and implementation approach we briefly present a light extension to the OOHDM design approach, incorporating physical objects and “walkable” links. We next present a Web application framework for deploying physical hypermedia software and show an example of use. We evaluate our approach and finally we discuss some further work we are pursuing.

This article reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: (1) from DSGE to state-space model; (2) from state-space model to VAR( ∞ ); (3) from VAR( ∞ ) to finite-order VAR. The focus is on discussing what can go wrong at each step of this mapping and on critically highlighting the hidden assumptions. I also point out some open research questions and interesting new research directions in the literature on the econometrics of DSGE models. These include, in no particular order: understanding the effects of log-linearization on estimation and identification; dealing with multiplicity of equilibria; estimating nonlinear DSGE models; incorporating into DSGE models information from atheoretical models and from survey data; adopting flexible modeling approaches that combine the theoretical rigor of DSGE models and the econometric model’s ability to fit the data.

This chapter discusses methodological challenges that may be faced by researchers interested in financial markets in relation to the COVID-19 pandemic. In particular, we focus on the behaviour of investors and consider three aspects that affect their investment decision process, namely comovement, cross-sectional asset pricing, and out-of-sample forecasting. We argue that, in relation to the pandemic, relevant financial time series such as asset returns exhibit nonlinear dynamics, which should be suitably incorporated within appropriate methodological tools. We discuss possible existing approaches that ensure that those nonlinearities are properly accounted for. Finally, possible areas of future research are touched upon.

This paper investigates how individuals determine their tax morale levels and tax compliance decisions. Using a questionnaire survey and a multivariate tests procedure, the paper revealed that tax evasion is morally acceptable in Jordan under some circumstances, indeed there could be an affirmative duty to evade taxes since the government is perceived to be highly corrupted. The findings also show that while the extent of the governmental corruption has a positive (negative) effect on tax non-compliance (tax morale), the efficient expenditure of governmental tax revenues has a negative (positive) impact on tax non-compliance (tax morale). The individuals’ tax non-compliance decisions are likewise positively affected by the tax rates and by the taxation system’s being perceived as unjust, but decline with the increase of audit rates and the subsequent penalty rates. The degree and effectiveness of these determinants are dependent on the individual’s level of risk aversion, financial constraints and the surrounding referent groups. The results also confirm that individual factors play a significant role in determining the level of tax morale. Overall, the tax morale level and the compliance decision of an individual are greatly influenced by gender, age, educational level, occupational status and religious background.

The erosion of autonomy in traditional professions has been explained by client capture – professionals increasingly work under close control of powerful corporate clients. However, research is missing on how knowledge workers in rapidly rising knowledge professions of the twenty-first century experience and respond to the risk of client capture. Evaluation is one such exploding field. This study examines the narratives of professional evaluators to understand how they navigate their mandate to deliver independent assessments of complex social programs under the threat of client capture. Data come from 29 interviews with evaluators of 65 interdisciplinary graduate training projects funded by the US National Science Foundation in the first two years of the program (2015–2016). Evidence of client capture is found in how evaluators discuss scope creep with limited resources, being asked to misrepresent their findings, and burying of evaluation reports. The authors also find evidence of evaluators navigating client capture by rationing their labor, using state-based rules to mediate demands, drawing on professional expertise, and generating savvy emotional labor. But this study argues the client capture concept obscures the dynamics of knowledge production, in which evaluators shape scientific programs in innovative ways. This study sheds new light on the context in which inequalities operate in this emerging profession, and how the structure of knowledge work may generate novel pathways of professional influence where work conditions might otherwise rule against it.

The purpose of this paper is to analyse algorithms for fluid‐structure interaction (FSI) from a purely algorithmic point of view.

First of all a 1D model problem is selected, for which both the fluid and structural behavior are represented through a minimum number of parameters. Different coupling algorithm and time integration schemes are then applied to the simplified model problem and their properties are discussed depending on the values assumed by the parameters. Both exact and approximate time integration schemes are considered in the same framework so to allow an assessment of the different sources of error.

The properties of staggered coupling schemes are confirmed. An insight on the convergence behavior of iterative coupling schemes is provided. A technique to improve such convergence is then discussed.

All the results are proved for a given family of time integration schemes. The technique proposed can be applied to other families of time integration techniques, but some of the analytical results need to be reworked under this assumption.

The problems that are commonly encountered in FSI can be justified by simple arguments. It can also be shown that the limit at which trivial iterative schemes experience convergence difficulties is very close to that at which staggered schemes become unstable.

All the results shown are based on simple mathematics. The problems are presented so to be independent of the particular choice for the solution of the fluid flow.

This chapter contributes to the Public Value (PV) literature in relation to accounting by providing evidence on its content's operationalization through performance measurement. In particular, it establishes the link with Sustainable Development Goals (SDGs), which may work as guiding principles of a public organization’s action. Accordingly, organizations embedding SDGs include them in their strategic decisions and disclose them through performance measurement and narratives.

The SDGs' presence is explored in the PV of a sample of Italian health institutes through documentary analysis of their performance plans. The aim is to verify if and how SDGs are pursued and whether the COVID-19 pandemic has affected PV content.

Besides Goal No. 3 (Health), the PV content of the investigated institutes contains various SDGs. Before the pandemic, their PV was aligned with SDGs mainly related to prosperity, economic growth and social inclusion. In the following period, the number of SDGs increased, introducing planet and environmental protection dimensions. No one explicitly mentions pursuing SDGs, revealing a non-institutionalized sensitivity of managers towards SDGs. The analysis distinguishes between ‘core’ SDGs, revealed mostly by traditional performance measures disclosing the achievement of institutes' mission, and ‘complementary’ SDGs, expressed mostly through narratives. This can derive from performance measurement, which employs the language of performativity, while the contribution to society is relegated in the narratives, making them less incisive.