Evolutionary modeling in economics : recent history and immediate prospects

Abstract not availablemathematical economics and econometrics ;

Long Waves: Conceptual, Empirical and Modelling Issues

The theory of long waves is exceptionally fortunate in that, while there is no general consensus that they exist or, assuming that they do, what an appropriate theory should be, due to the unstinting efforts of several researchers, we have encyclopaedic compendia of the literature (Freeman 1996, Reijnders and Louçã 1999) and a recent valiant attempt to write modern economic history from a long-wave perspective (Freeman and Louçã 2001). The purpose of this entry is to succinctly review the controversy about what long waves might mean as a phenomenon, how they might be measured and modelled, and where they might fit into an overarching theory of economic dynamics and evolution.economics of technology ;

When is a Wave a Wave? Long Waves as Empirical and Theoretical Constructs from a Complex Systems Perspective

While long waves have been seriously discussed by economists for almost one hundred years, to date there is no scientific consensus that particular frequency components are in any way privileged in the undoubtedly fluctuating history of modern economic and political development. This is disappointing for two reasons. First, the demonstration that robust, well-defined periodic components existed would present us with a plausible tool for forecasting. And second, they could (and their purported existence has variously been thought to) provide insight into underlying causal mechanisms that generate the observed patterns. The data, I argue, only provide support for a continuous spectral pattern of a power law, 1/fa. This is borne out in the paper by the analysis of political indicators such as the newly revised Modelski/Thompson sea power index and the Levy great powers conflict data. Claims for underlying low-dimensional chaos are only partly substantiated. Individual peaks at various frequencies in the spectrum are probably only due to “random noise” factors unique to segments of the record and not robust across countries and historical episodes. While one could then play the game of finding ad hoc explanations for why the ‘K-wave’ did not take its expected form in this or that century, from the perspective of the theory of complex dynamics it seems more plausible to conclude that a periodic model is not appropriate. Rather, the underlying model is more likely to be of the self-organized criticality or percolation type, characterized by power-law or fractal behavior rather than well-defined periodicity. I highlight some features common to several models of innovation/ economic dynamics and war/hegemonic cycles, such as highly clustered but nonperiodic critical events and resulting long life cycles of rise and decline, that may serve as a plausible explanatory mechanism for this ‘revisionist’ interpretation of the empirical record on long waves.Economics ;

A Note on Michelacci and Zaffaroni, Long Memory, and Time Series of Economic Growth

In a recent paper in The Journal of Monetary Economics, Michelacci and Zaffaroni (2000)estimate long memory parameters for GDP per capita of 16 OECD countries. In this note weargue that these estimations are questionable for the purposes of clarifying the time seriesproperties of these data (presence of unit roots, mean reversion, long memory) because theauthors a) filter out a deterministic linear-in-logs trend instead of first-differencing in logs,and manipulate the data in other highly questionable ways, b) rely on the semiparametricGeweke and Porter-Hudak (GPH) method as modified by Robinson, which is known to behighly biased in small samples. We re-examine these results using Beran’s nonparametricFGN estimator and Sowell’s exact maximum likelihood ARFIMA estimator. These methodsavoid the small-sample bias and arbitrariness of the cut-off parameters of Robinson’s methodand allow us to control for short memory effects, although the parametric ARFIMA estimatorintroduces specification problems of its own. We also look at the influence of the choice ofsub-periods on the results. Finally, we apply Robinson’s method to our treatment of the dataand show that MZ’s results no longer hold, nor are their cut-off parameter and filteringinsensitivity claims substantiated.economics of technology ;

Long Memory in Time Series of Economic Growth and Convergence

not availableeconomics of technology ;

The size distribution of innovations revisited: an application of extreme value statistics to citation and value measures of patent significance

This paper focuses on the analysis of size distributions of innovations, which are known to be highly skewed. We use patent citations as one indicator of innovation significance, constructing two large datasets from the European and US Patent Offices at a high level of aggregation, and the Trajtenberg (1990) dataset on CT scanners at a very low one. We also study self-assessed reports of patented innovation values using two very recent patent valuation datasets from the Netherlands and the UK, as well as a small dataset of patent license revenues of Harvard University. Statistical methods are applied to analyse the properties of the empirical size distributions, where we put special emphasis on testing for the existence of ‘heavy tails’, i.e., whether or not the probability of very large innovations declines more slowly than exponentially. While overall the distributions appear to resemble a lognormal, we argue that the tails are indeed fat. We invoke some recent results from extreme value statistics and apply the Hill (1975) estimator with data-driven cut-offs to determine the tail index for the right tails of all datasets except the NL and UK patent valuations. On these latter datasets we use a maximum likelihood estimator for grouped data to estimate the Pareto exponent for varying definitions of the right tail. We find significantly and consistently lower tail estimates for the returns data than the citation data (around 0.7 vs. 3-5). The EPO and US patent citation tail indices are roughly constant over time (although the US one does grow somewhat in the last periods) but the latter estimates are significantly lower than the former. The heaviness of the tails, particularly as measured by financial indices, we argue, has significant implications for technology policy and growth theory, since the second and possibly even the first moments of these distributions may not exist. (JEL Codes: C16, O31, O33 Keywords: returns to invention, patent citations, extreme-value statistics, skewed distributions, heavy tails.)mathematical economics and econometrics ;

Breaking the Waves: A Poisson Regression Approach to Schumpeterian Clustering of Basic Innovations

The Schumpeterian theory of long waves has given rise to an intense debate on the existenceof clusters of basic innovations. Silverberg and Lehnert have criticized the empirical part ofthis literature on several methodological accounts. In this paper, we propose the methodologyof Poisson regression as a logical way to incorporate this criticism. We construct a new timeseries for basic innovations (based on previously used time series), and use this to test thehypothesis that basic innovations cluster in time. We define the concept of clustering invarious precise ways before undertaking the statistical tests. The evidence we find onlysupports the ‘weakest’ of our clustering hypotheses, i.e., that the data display overdispersion.We thus conclude that the authors who have argued that a long wave in economic life isdriven by clusters of basic innovations have stretched the statistical evidence too far.research and development ;

A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies

An obstacle to the widespread adoption of environmentally friendly energy technologies such as stationary and mobile fuel cells is their high upfront costs. While much lower prices seem to be attainable in the future due to learning curve cost reductions that increase rapidly with the scale of diffusion of the technology, there is a chicken and egg problem, even when some consumers may be willing to pay more for green technologies. Drawing on recent percolation models of diffusion by Solomon et al. [7], Frenken et al. [8] and Höhnisch et al. [9], we develop a network model of new technology diffusion that combines contagion among consumers with heterogeneity of agent characteristics. Agents adopt when the price falls below their random reservation price drawn from a lognormal distribution, but only when one of their neighbors has already adopted. Combining with a learning curve for the price as a function of the cumulative number of adopters, this may lead to delayed adoption for a certain range of initial conditions. Using agent-based simulations we explore when a limited subsidy policy can trigger diffusion that would otherwise not happen. The introduction of a subsidy policy seems to be highly effective for a given high initial price level only for learning economies in a certain range. Outside this range, the diffusion of a new technology either never takes off despite the subsidies, or the subsidies are unnecessary. Perhaps not coincidentally, this range seems to correspond to the values observed for many successful innovations.Innovation diffusion, learning economies, percolation, networks, heterogeneous agents, technology subsidies, environmental technologies

Self-organization of R&D search in complex technology spaces

We extend an earlier model of innovation dynamics based on invasive percolation by adding endogenous R&D search by economically motivated firms. The {0,1} seeding of the technol-ogy lattice is now replaced by draws from a lognormal distribution for technology ‘difficulty’. Firms are rewarded for successful innovations by increases in their R&D budget. We compare two regimes. In the first, firms are fixed in a region of technology space. In the second, they can change their location by myopically comparing progress in their local neighborhoods and probabilistically moving to the region with the highest recent progress. We call this the mov-ing or self-organizational regime. We find that as the mean and standard deviation of the log-normal distribution are varied, the relative rates of aggregate innovation switches between the two regimes. The SO regime has higher innovation rates, other things being equal, for lower means or higher standard deviations of the lognormal distribution. This results holds for in-creasing size of the search radius. The clustering of firms in the SO regime grows rapidly and fluctuates in a complex way around a high value which increases with the search radius. We also investigate the size distributions of the innovations generated in each regime. In the fixed one, the distribution is approximately lognormal and certainly not fat tailed. In the SO regime, the distributions are radically different. They are much more highly right skewed and show scaling over at least two decades with a slope of almost exactly one, independently of parame-ter settings. Thus we argue that firm self-organization leads to self-organized criticality. (Keywords: innovation, percolation, search, technological change, R&D, clustering, self-organized criticality.research and development ;