Roberto Patuelli

Università di Bologna, Economics, Faculty Member

Rimini Centre for Economics Analysis, Fellows, Scientific secretary

Vrije Universiteit Amsterdam, Spatial Economics, Alumnus

George Mason University, School of Public Policy, Alumnus

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I have a Bachelor in Statistics and Economics (University of Bologna, Italy), a Master's of Arts in Transportation Policy, Operations and Logistics (George Mason University, USA), and a Ph.D. in Economics (VU University Amsterdam, The Netherlands). I have worked in Belgium, Germany, Italy, The Netherlands, Switzerland and the USA, concentrating, over the years, on topics such as complex networks, regional employment forecasting, regional unemployment differentials, commuting networks and regional innovation processes. Methodologically, I'm currently interested in spatial statistics and econometrics, in particular with applications to nonlinear and mixed methods. I was the local unit coordinator at the University of Bologna for a FIRB 2012 project on "Social and Spatial Interactions in the Accumulation of Civic and Human Capital", and I am an Editor of REGION -The journal of the European Regional Science Association.
Supervisors: Aura Reggiani and Peter Nijkamp
Phone: +39-0541-434276
Address: Università di Bologna
Rimini Campus
via Angherà 22
47921 Rimini
Italy

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Papers by Roberto Patuelli

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

by Simonetta Longhi, Roberto Patuelli, and Aura Reggiani

Studies in Regional Science, 2003

This paper develops a flexible multi-dimensional assessment method for the comparison of differen... more This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms, with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical- econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models – based on classes of Neural Network (NN) techniques – that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests.

Complex Network Phenomena in Telecommunication Networks

by Aura Reggiani, Roberto Patuelli, and Andy Gillespie

Networks and Spatial Economics, 2005

Many networks such as the Internet have been found to possess scale-free and small-world network ... more Many networks such as the Internet have been found to possess scale-free and small-world network properties reflected by power-law distributions. Scale-free properties evolve in large complex networks through self-organizing processes and, more specifically, preferential attachment. New nodes in a network tend to attach to other vertices that are already well-connected. Because traffic is routed mainly through a few highly connected and concentrated vertices, the diameter of the network is small in comparison to other network structures, and movement through the network is therefore efficient. At the same time, this efficiency feature puts scale-free networks at risk for becoming disconnected or significantly disrupted when super-connected nodes are removed, either unintentionally or through a targeted attack or external force. The present paper will examine and compare properties of telecommunication networks for both the United States and Europe. Both types of networks will be examined in terms of their network topology and specifically, whether or not they are scale-free networks to be further explored by identifying and plotting power-law.

Environmental Tax Reform and the Double Dividend: A Meta-analytical Performance Assessment

Ecological Economics, 2005

In this paper, we offer a meta-analytical synthesis of recent (simulation) studies on environment... more In this paper, we offer a meta-analytical synthesis of recent (simulation) studies on environmental tax reform (ETR). The studies considered here look at both environmental effects (e.g., reduction in CO2 emission) and economic effects (e.g., employment gains) consequent upon such a tax reform. The existing statistical results from the literature mainly suggest that the tax type, the recycling policy, and the economic model used in the simulations significantly influence the chance that a ‘double dividend’ effect can be obtained. These empirical results are, however, not entirely conclusive regarding the question of which combination of policies and models will lead to a higher double dividend. This issue is investigated in our study by a quantitative meta-analytic approach. Our meta-analytic statistical experiment demonstrates that the total effect of a tax-and-recycle policy has a significant influence on the economic variables (second dividend), when employment is used. It is also shown that different definitions of the double dividend contribute in determining the success of ETR, in particular since the effects on GDP are less clear than for employment. These findings should be taken into consideration when deploying an ETR in a policy context, in order to prevent a situation where ETR is rejected or accepted solely due to characteristics of one simulation study rather than through a wide set of results from different studies.

Small-World Phenomena in Communications Networks: A Cross-Atlantic Comparison

by Aura Reggiani and Roberto Patuelli

Methods and Models in Transport and Communications. Cross Atlantic Perspectives, 2005

The Development of Regional Employment in Germany: Results from Neural Network Experiments

by Roberto Patuelli and Aura Reggiani

Scienze Regionali (Italian Journal of Regional Science), 2006

This paper offers an overview of experimental results, based on neural networks (NNs) used to for... more This paper offers an overview of experimental results, based on neural networks (NNs) used to forecast regional employment variations in Germany. NNs are statistical optimization tools, whose main characteristics are non-linear data processing and the ability to find functional relationships within the data. We present the results – for a set of NN models – based on regional data concerning full-time employment in Germany. The database used in our experiments consists of two panels of 326 and 113 NUTS 3 districts, which represent West and East Germany, respectively. In order to forecast employment growth rates for the years 2004, 2005, and 2006, NN models – also embedding shift-share analysis components – were developed and evaluated for West and East Germany. The paper concludes with theoretical, methodological and empirical observations in the light of future research developments.

New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets

by Roberto Patuelli and Aura Reggiani

Spatial Economic Analysis, 2006

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts o... more In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying increasing interest in several fields because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not specified explicitly. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with shift-share analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German (NUTS 3) districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

A Rank-order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts

by Roberto Patuelli, Simonetta Longhi, and Aura Reggiani

The Review of Regional Studies, 2007

Using a panel of 439 German regions we evaluate and compare the performance of various Neural Net... more Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

format_quoteFriedman test suggests consistent hierarchies among German districts' employment variations, rejecting null hypothesis at 99% confidence.format_quote