596 research outputs found
Nonparametric Regression using the Concept of Minimum Energy
It has recently been shown that an unbinned distance-based statistic, the
energy, can be used to construct an extremely powerful nonparametric
multivariate two sample goodness-of-fit test. An extension to this method that
makes it possible to perform nonparametric regression using multiple
multivariate data sets is presented in this paper. The technique, which is
based on the concept of minimizing the energy of the system, permits
determination of parameters of interest without the need for parametric
expressions of the parent distributions of the data sets. The application and
performance of this new method is discussed in the context of some simple
example analyses.Comment: 10 pages, 4 figure
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Estimation of NAIRU with In ation Expectation Data
Estimating natural rate of unemployment (NAIRU) is important for understanding the joint dynamics of unemployment, inflation, and inflation expectation. However, existing literature falls short of endogenizing inflation expectation together with NAIRU in a model consistent way. We estimate a structural model with forward and backward looking Phillips curve. Inflation expectation is treated as a function of state variables and we use survey data as its noisy observations. Surprisingly, we find that the estimated NAIRU tracks unemployment rate closely, except for the high inflation period (late 1970s). Compared to the estimation without using the survey data, the estimated Bayesian credible sets are narrower and our model leads to better inflation and unemployment forecasts. These results suggest that monetary policy was very effective and there was not much room for policy improvement
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Inflation Co-movement across Countries in Multi-maturity Term Structure: An Arbitrage-Free Approach
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LASSO-Driven Inference in Time and Space
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors
Mixtures of nonparametric autoregressions
We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models.We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. © American Statistical Association and Taylor & Francis 2011.postprin
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Influencers and Communities in Social Networks
Integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter Θ typically much larger than the number of observations. To cope with this problem we introduce a new structural model which supposes that the network is driven by influencers. We additionally assume the community structure of the network, such that the users from the same community depend on the same influencers. An estimation procedure is proposed based on a greedy algorithm and LASSO. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when the observed time interval is smaller than the size of the network. Using a novel dataset of 1069K messages from 30K users posted on the microblogging platform StockTwits during a 4-year period (01.2014-12.2018) and quantifying their opinions via natural language processing, we model their dynamic opinions network and further separate the network into communities. With a sparsity regularization, we are able to identify important nodes in the network
Specialization of strategies and herding behavior of trading firms in a financial market
The understanding of complex social or economic systems is an important
scientific challenge. Here we present a comprehensive study of the Spanish
Stock Exchange showing that most financial firms trading in that market are
characterized by a resulting strategy and can be classified in groups of firms
with different specialization. Few large firms overally act as trending firms
whereas many heterogeneous firm act as reversing firms. The herding properties
of these two groups are markedly different and consistently observed over a
four-year period of trading.Comment: 8 pages, 5 figure
In praise of partially interpretable predictors
Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smallermean-squared error or integratedmean-squared error.We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both interpretability and noninterpretability in models seem to give the best results
Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction
We consider the problem of bandwidth selection by cross-validation from a
sequential point of view in a nonparametric regression model. Having in mind
that in applications one often aims at estimation, prediction and change
detection simultaneously, we investigate that approach for sequential kernel
smoothers in order to base these tasks on a single statistic. We provide
uniform weak laws of large numbers and weak consistency results for the
cross-validated bandwidth. Extensions to weakly dependent error terms are
discussed as well. The errors may be {\alpha}-mixing or L2-near epoch
dependent, which guarantees that the uniform convergence of the cross
validation sum and the consistency of the cross-validated bandwidth hold true
for a large class of time series. The method is illustrated by analyzing
photovoltaic data.Comment: 26 page
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