6,683 research outputs found
Covariance estimation for multivariate conditionally Gaussian dynamic linear models
In multivariate time series, the estimation of the covariance matrix of the
observation innovations plays an important role in forecasting as it enables
the computation of the standardized forecast error vectors as well as it
enables the computation of confidence bounds of the forecasts. We develop an
on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is
empirically found that, for a range of simulated time series, the proposed
covariance estimator has good performance converging to the true values of the
unknown observation covariance matrix. Over a simulated time series, the new
method approximates the correct estimates, produced by a non-sequential Monte
Carlo simulation procedure, which is used here as the gold standard. The
special, but important, vector autoregressive (VAR) and time-varying VAR models
are illustrated by considering London metal exchange data consisting of spot
prices of aluminium, copper, lead and zinc.Comment: 21 pages, 2 figures, 6 table
spGARCH: An R-Package for Spatial and Spatiotemporal ARCH models
In this paper, a general overview on spatial and spatiotemporal ARCH models
is provided. In particular, we distinguish between three different spatial
ARCH-type models. In addition to the original definition of Otto et al. (2016),
we introduce an exponential spatial ARCH model in this paper. For this new
model, maximum-likelihood estimators for the parameters are proposed. In
addition, we consider a new complex-valued definition of the spatial ARCH
process. From a practical point of view, the use of the R-package spGARCH is
demonstrated. To be precise, we show how the proposed spatial ARCH models can
be simulated and summarize the variety of spatial models, which can be
estimated by the estimation functions provided in the package. Eventually, we
apply all procedures to a real-data example
Estimation of the Spatial Weights Matrix under Structural Constraints
While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section
dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we
propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional
housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models
We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling window forecasts of quoted bid-ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 13 % of spread transaction costs.Bid-ask spreads, forecasting, high-frequency data, stock market liquidity, count data time series, long memory Poisson autoregression
Algorithms for Estimating Trends in Global Temperature Volatility
Trends in terrestrial temperature variability are perhaps more relevant for
species viability than trends in mean temperature. In this paper, we develop
methodology for estimating such trends using multi-resolution climate data from
polar orbiting weather satellites. We derive two novel algorithms for
computation that are tailored for dense, gridded observations over both space
and time. We evaluate our methods with a simulation that mimics these data's
features and on a large, publicly available, global temperature dataset with
the eventual goal of tracking trends in cloud reflectance temperature
variability.Comment: Published in AAAI-1
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