463,369 research outputs found
Long-Run Structural Modelling
The paper develops a general framework for identification, estimation, and hypothesis testing in cointegrated systems when the cointegrating coefficients are subject to (possibly) non-linear and cross-equation restrictions, obtained from economic theory or other relevant a priori information. It provides a proof of the consistency of the maximum likelihood (ML) estimators, establishes the relative rates of convergence of the ML estimators of the short-run and the long-run parameters, and derives their asymptotic distribution; thus generalizing the results already available in the literature for the linear case. The paper also develops tests of the over-identifying (possibly) non-linear restrictions on the cointegrating vectors. The estimation and hypothesis testing procedures are applied to an Almost Ideal Demand System estimated on U.K. quarterly observations. Unlike many other studies of consumer demand this application does not treat relative prices and real per capita expenditures as exogenously given.Cointegration, identification, testing non-linear restrictions, consistency, asymptotic distribution, Almost Ideal Demand Systems
Robust nonparametric detection of objects in noisy images
We propose a novel statistical hypothesis testing method for detection of
objects in noisy images. The method uses results from percolation theory and
random graph theory. We present an algorithm that allows to detect objects of
unknown shapes in the presence of nonparametric noise of unknown level and of
unknown distribution. No boundary shape constraints are imposed on the object,
only a weak bulk condition for the object's interior is required. The algorithm
has linear complexity and exponential accuracy and is appropriate for real-time
systems. In this paper, we develop further the mathematical formalism of our
method and explore important connections to the mathematical theory of
percolation and statistical physics. We prove results on consistency and
algorithmic complexity of our testing procedure. In addition, we address not
only an asymptotic behavior of the method, but also a finite sample performance
of our test.Comment: This paper initially appeared in 2010 as EURANDOM Report 2010-049.
Link to the abstract at EURANDOM repository:
http://www.eurandom.tue.nl/reports/2010/049-abstract.pdf Link to the paper at
EURANDOM repository: http://www.eurandom.tue.nl/reports/2010/049-report.pd
On the estimation and testing of functional-coefficient linear models
In this paper we investigate the estimation and testing of the functional coefficient linear models under dependence, which includes the functional coefficient autoregressive model of Chen and Tsay (1993). We use local linear smoothing to estimate the coefficient functions of a functional-coefficient linear model, prove their uniform consistency, and derive their asymptotic distributions in terms of Gaussian processes. From these distributions we can obtain some tests about coefficient functions and the model. Some simulations and a study of real data are reported.published_or_final_versio
A latent factor model for ordinal data to measure multivariate predictive ability of financial market movements
In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a Laplace approximation, and show its consistency and asymptotic normality. Monte Carlo experiments reveal that both the estimation method and the testing procedure perform well in small samples. An empirical illustration is given for mid-term forecasts simultaneously made by two broker-dealers for several countries.structural equation model, latent variable, generalised linear model, factor analysis, multinomial logit, forecasts, LAMLE, canonical correlation
A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection
In this article, we propose a factor-adjusted multiple testing (FAT)
procedure based on factor-adjusted p-values in a linear factor model involving
some observable and unobservable factors, for the purpose of selecting skilled
funds in empirical finance. The factor-adjusted p-values were obtained after
extracting the latent common factors by the principal component method. Under
some mild conditions, the false discovery proportion can be consistently
estimated even if the idiosyncratic errors are allowed to be weakly correlated
across units. Furthermore, by appropriately setting a sequence of threshold
values approaching zero, the proposed FAT procedure enjoys model selection
consistency. Extensive simulation studies and a real data analysis for
selecting skilled funds in the U.S. financial market are presented to
illustrate the practical utility of the proposed method. Supplementary
materials for this article are available online
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