16,461 research outputs found
Extracting information from S-curves of language change
It is well accepted that adoption of innovations are described by S-curves
(slow start, accelerating period, and slow end). In this paper, we analyze how
much information on the dynamics of innovation spreading can be obtained from a
quantitative description of S-curves. We focus on the adoption of linguistic
innovations for which detailed databases of written texts from the last 200
years allow for an unprecedented statistical precision. Combining data analysis
with simulations of simple models (e.g., the Bass dynamics on complex networks)
we identify signatures of endogenous and exogenous factors in the S-curves of
adoption. We propose a measure to quantify the strength of these factors and
three different methods to estimate it from S-curves. We obtain cases in which
the exogenous factors are dominant (in the adoption of German orthographic
reforms and of one irregular verb) and cases in which endogenous factors are
dominant (in the adoption of conventions for romanization of Russian names and
in the regularization of most studied verbs). These results show that the shape
of S-curve is not universal and contains information on the adoption mechanism.
(published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI:
http://dx.doi.org/10.1098/rsif.2014.1044)Comment: 9 pages, 5 figures, Supplementary Material is available at
http://dx.doi.org/10.6084/m9.figshare.122178
Discourse network analysis: policy debates as dynamic networks
Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed
Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
We propose a new class of models specifically tailored for spatio-temporal
data analysis. To this end, we generalize the spatial autoregressive model with
autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting
the recent advancements in Score Driven (SD) models typically used in time
series econometrics. In particular, we allow for time-varying spatial
autoregressive coefficients as well as time-varying regressor coefficients and
cross-sectional standard deviations. We report an extensive Monte Carlo
simulation study in order to investigate the finite sample properties of the
Maximum Likelihood estimator for the new class of models as well as its
flexibility in explaining several dynamic spatial dependence processes. The new
proposed class of models are found to be economically preferred by rational
investors through an application in portfolio optimization.Comment: 33 pages, 5 figure
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