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266
A Brief History of Generative Models for Power Law and Lognormal Distributions
 INTERNET MATHEMATICS
"... Recently, I became interested in a current debate over whether file size distributions are best modelled by a power law distribution or a a lognormal distribution. In trying ..."
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Cited by 252 (7 self)
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Recently, I became interested in a current debate over whether file size distributions are best modelled by a power law distribution or a a lognormal distribution. In trying
Agentbased computational economics: Growing economies from the bottomup
 Artificial Life
, 2002
"... Abstract: Agentbased computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining ch ..."
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Cited by 127 (4 self)
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Abstract: Agentbased computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology, and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well some potential difficulties. Keywords: Agentbased computational economics; artificial life; learning; evolution of norms; markets; networks; parallel experiments with humans and computational agents; computational laboratories. 1
2007), “What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns,” Working Paper 13068, National Bureau of Economic Research
"... paper was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant (ITR0427889) is gratefully acknowledged. Research results and conclusions expressed are our own and do not ..."
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Cited by 66 (18 self)
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paper was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant (ITR0427889) is gratefully acknowledged. Research results and conclusions expressed are our own and do not necessarily reflect the views of the Census Bureau or NSF. This paper has been Many industries are geographically concentrated. Many mechanisms that could account for such agglomeration have been proposed. We note that these theories make different predictions about which pairs of industries should be coagglomerated. We discuss the measurement of coagglomeration and use data from the Census Bureau’s Longitudinal Research Database from 1972 to 1997 to compute pairwise coagglomeration measurements for U.S. manufacturing industries. Industry attributes are used to construct measures of the relevance of each of Marshall’s three theories of industry agglomeration to each industry pair: (1) agglomeration saves transport costs by proximity to input suppliers or final consumers, (2) agglomeration allows for labor market pooling, and (3) agglomeration facilitates intellectual spillovers. We assess the importance of the theories via regressions
Geographic Localization of International Technology Diffusion
, 2001
"... Income convergence across countries turns on whether technological knowledge spillovers are global or local. I estimate the amount of spillovers from RD expenditures on a geographic basis, using a new data set which encompasses most of the world's innovative activity between 1970 and 1995. ..."
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Cited by 65 (5 self)
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Income convergence across countries turns on whether technological knowledge spillovers are global or local. I estimate the amount of spillovers from RD expenditures on a geographic basis, using a new data set which encompasses most of the world's innovative activity between 1970 and 1995.
Zipf’s law for cities: An explanation
 Quart J Econ 1999
"... Zipf’s law is a very tight constraint on the class of admissible models of local growth. It says that for most countries the size distribution of cities strikingly fits a power law: the number of cities with populations greater than S is proportional to 1/S. Suppose that, at least in the upper tail, ..."
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Cited by 64 (1 self)
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Zipf’s law is a very tight constraint on the class of admissible models of local growth. It says that for most countries the size distribution of cities strikingly fits a power law: the number of cities with populations greater than S is proportional to 1/S. Suppose that, at least in the upper tail, all cities follow some proportional growth process (this appears to be verified empirically). This automatically leads their distribution to converge to Zipf’s law. I.
The Lost Decades: Developing Countries' Stagnation in Spite of Policy Reform 19801998
 JOURNAL OF ECONOMIC GROWTH
, 2001
"... I document in this paper a puzzle that has not received previous attention in the literature. In 198098, median per capita income growth in developing countries was 0.0 percent, as compared to 2.5 percent in 196079. Yet I document in this paper that variables that are standard in growth regression ..."
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Cited by 50 (3 self)
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I document in this paper a puzzle that has not received previous attention in the literature. In 198098, median per capita income growth in developing countries was 0.0 percent, as compared to 2.5 percent in 196079. Yet I document in this paper that variables that are standard in growth regressions  policies like financial depth and real overvaluation, and initial conditions like health, education, fertility, and infrastructure generally improved from 196079 to 198098. Developing country growth should have increased instead of decreased according to the standard growth regression determinants of growth. The stagnation seems to represent a disappointing outcome to the movement towards the "Washington Consensus" by developing countries. I speculate that worldwide factors like the increase in world interest rates, the increased debt burden of developing countries, the growth slowdown in the industrial world, and skillbiased technical change may have contributed to the developing countries' stagnation, although I am not able to establish decisive evidence for these hypotheses. I also document that many growth regressions are misspecified in a way similar to the Jones (1995) critique that a stationary variable (growth) is being regressed on nonstationary variables like policies and initial conditions. It may be that the 196079 period was the unusual period for LDC growth, and the 198098 stagnation of poor countries represents a return to the historical pattern of divergence between rich and poor countries.
Under the hood: issues in the specification and interpretation of spatial regression models
 Agricultural Economics
, 2002
"... This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven persp ..."
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Cited by 44 (1 self)
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This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.
Statistical properties of the volatility of price fluctuations
, 1999
"... We study the statistical properties of volatility—a measure of how much the market is likely to fluctuate. We estimate the volatility by the local average of the absolute price changes. We analyze (a) the S&P 500 stock index for the 13year period Jan 1984 to Dec 1996 and (b) the market capitalizati ..."
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Cited by 43 (2 self)
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We study the statistical properties of volatility—a measure of how much the market is likely to fluctuate. We estimate the volatility by the local average of the absolute price changes. We analyze (a) the S&P 500 stock index for the 13year period Jan 1984 to Dec 1996 and (b) the market capitalizations of the largest 500 companies registered in the Trades and Quotes data base, documenting all trades for all the securities listed in the three major stock exchanges in the US for the 2year period Jan 1994 to Dec 1995. For the S&P 500 index, the probability density function of the volatility can be fit with a lognormal form in the center. However, the asymptotic behavior is better described by a powerlaw distribution characterized by an exponent 1 + µ ≈ 4. For individual companies, we find a similar power law asymptotic behavior of the probability distribution of volatility with exponent 1 + µ ≈ 4. In addition, we find that the volatility distribution scales for a range of time intervals. Further, we study the correlation function of the volatility and find power law decay with long persistence for the S&P 500 index and the individual companies with a crossover at approximately 1.5 days. To 1 quantify the powerlaw correlations, we apply power spectrum analysis and a recentlydeveloped modified rootmeansquare analysis, termed detrended fluctuation analysis (DFA). For the S&P 500 stock index, DFA estimates for the exponents characterizing the power law correlations are α1 = 0.66 for short time scales (within ≈ 1.5days) and α2 = 0.93 for longer time scales (up to a year). For individual companies, we find α1 = 0.60 and α2 = 0.74, respectively. The power spectrum gives consistent estimates of the two powerlaw exponents. PACS numbers: 89.90.+n 2 Typeset using
The evolution of city size distributions
 in V. Henderson and J.F. Thisse, eds, ‘Handbook of Regional and Urban Economics
, 2004
"... We review the accumulated knowledge on city size distributions and determinants of urban growth. This topic is of interest because of a number of key stylized facts, including notably Zipf’s law for cities (which states that the number of cities of size greater than S is proportional to 1/S) and the ..."
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Cited by 29 (7 self)
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We review the accumulated knowledge on city size distributions and determinants of urban growth. This topic is of interest because of a number of key stylized facts, including notably Zipf’s law for cities (which states that the number of cities of size greater than S is proportional to 1/S) and the importance of urban primacy. We first review the empirical evidence on the upper tail of city size distribution. We offer a novel discussion of the important econometric issues in the characterization of the distribution. We then discuss the theories that have been advanced to explain the approximate constancy of the distribution across very different economic and social systems, emphasizing both barebone statistical theories and more developed economic theories. We discuss the more recent work on the determinants of urban growth and, in particular, growth regressions, economic explanations of city size distributions other than Gibrat’s law, consequences of major