Andrea Pagano

Editorial: Emulation techniques for the reduction and sensitivity analysis of complex environmental models

Emulation (also denoted as metamodelling in the literature) is an important and expanding area of research and represents one of the major advances in the study of complex mathematical models, with applications ranging from model reduction to sensitivity analysis. Despite the stunning increase in computing power over recent decades, computational limitations remain a major barrier to the effective and systematic use of large-scale, process-based simulation models in rational environmental decision-making. Whereas complex models may provide clear advantages when the goal of the modelling exercise is to enhance our understanding of the natural processes, they introduce problems of model identifiability caused by over-parameterization and suffer from high computational burden when used in management and planning problems, i.e. when they are combined with optimization routines. Therefore, a combination of techniques for complex model reduction with procedures for data assimilation and learning-based control could help to bridge the gap between science and the operational use of models for decision-making. Furthermore sensitivity analysis is a well known and established tool for evaluating robustness of model based results in management and planning, and is often performed in tandem with emulation. Indeed, emulators provide an efficient means for doing a sensitivity analysis for large and expensive models. This thematic issue aims at providing a guide and reference for modellers in choosing appropriate emulation modelling approaches and understanding their features. Tools and applications of sensitivity analysis in the context of environmental modelling are also addressed, which is a typical complement of emulation in most applications. We hope that this thematic issue provides a useful benchmark in the academic literature for this important and expanding area of research, and will create an opportunity for dialogue between methodological and user-focused research.

Non-parametric estimation of conditional moments for sensitivity analysis

In this paper, we consider the non-parametric estimation of conditional moments, which is useful for applications in global sensitivity analysis (GSA) and in the more general emulation framework. The estimation is based on the state-dependent parameter (SDP) estimation approach and allows for the estimation of conditional moments of order larger than unity. This allows one to identify a wider spectrum of parameter sensitivities with respect to the variance-based main effects, like shifts in the variance, skewness or kurtosis of the model output, so adding valuable information for the analyst, at a small computational cost.

A comparison of structural reform scenarios across the EU member states - Simulation-based analysis using the QUEST model with endogenous growth

This paper calibrates the Roeger-Varga-Veld (2008) micro-founded DSGE model with endogenous growth for all EU member states using country specific structural characteristics and employs the individual country models to analyse the macroeconomic impact of various structural reforms. We analyse the costs and benefits of reforms in terms of fiscal policy instruments such as taxes, benefits, subsidies and administrative costs faced by firms. We find that less R&D intensive countries would benefit the most from R&D promoting and skill-upgrading policies. We also find that shifting from labour to consumption taxes, reducing the benefit replacement rate and relieving administrative entry barriers are the most effective measures in those countries which have high labour taxes and entry barriers.

State Dependent Regressions: From Sensitivity Analysis to Meta-modeling

State Dependent Parameter (SDP) modelling has been developed by Professor Peter Young in the 1990s to identify non-linearities in the context of dynamic transfer function models. SDP is a very efficient approach and it is based on recursive filtering and Fixed Interval Smoothing (FIS) algorithms. It has been applied successfully in many applications, especially to identify Data-Based Mechanistic models from observed time series data in environmental sciences. In this paper we highlight the role played by the SDP ideas, namely in the simplified State-Dependent Regression (SDR) form, in the context of sensitivity analysis and meta-modelling. Fruitful joint co-operation with Peter Young has led to a series of papers, where SDR has been applied to perform sensitivity analysis, to reduce model’s complexity and to build meta-models (or emulators) capable to reproduce the main features of large simulation models. Finally, we will describe how SDR algorithms can be effectively used in the context of the identification and estimation of tensor product smoothing splines ANOVA models, improving their performances.