6,242 research outputs found
Business Cycle Asymmetries: Characterisationand Testing Based on Markov-Switching Autoregression.
We propose testing for business cycle asymmetries in Markov-switching autoregressive (MS-AR) models. We derive the parametric restrictions on MS-AR models that rule out types of asymmetries such as deepness, steepness, and sharpness, and set out a testing procedure based on Wald statistics which have standard asymptotic. For a two-regime model, such as that popularized by Hamilton (1989), we show that deepness implies sharpness (and vice versa) while the process is always non-steep. We illustrate with two and three-state MS models of US GNP growth, and with models of US output and employment. Our findings are compared with those obtained from standard non-parametric tests.BUSINESS CYCLES ; TESTS
Robust estimators of ar-models : a comparison
Many regression-estimation techniques have been extended to cover the case of dependent
observations. The majority of such techniques are developed from the classical least
squares, M and GM approaches and their properties have been investigated both on theoretical
and empirical grounds. However, the behavior of some alternative methods- with
satisfactory performance in the regression case- has not received equal attention in the context
of time series. A simulation study of four robust estimators for autoregressive models containing
innovation or additive outliers is presented. The robustness and efficiency properties
of the methods are exhibited, some finite-sample results are discussed in combination with
theoretical properties and the relative merits of the estimators are viewed in connection with
the outlier-generating scheme.peer-reviewe
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models
This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models, their extensions – spike preprocessed, threshold and semiparametric autoregressions (i.e. AR models with nonparametric innovations), as well as, mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.Electricity market, Price forecast, Autoregressive model, Nonparametric maximum likelihood, Interval forecast, Conditional coverage
A Local Instrumental Variable Estimation Method for Generalized Additive Volatility Models
We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models already discussed in the literature. We also propose a new estimation procedure based on a localization of the econometric method of instrumental variables. Our method has considerable computational advantages over the competing marginal integration or projection method.ARCH, kernel estimation, nonparametric, volatility.
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
As a powerful statistical image modeling technique, sparse representation has
been successfully used in various image restoration applications. The success
of sparse representation owes to the development of l1-norm optimization
techniques, and the fact that natural images are intrinsically sparse in some
domain. The image restoration quality largely depends on whether the employed
sparse domain can represent well the underlying image. Considering that the
contents can vary significantly across different images or different patches in
a single image, we propose to learn various sets of bases from a pre-collected
dataset of example image patches, and then for a given patch to be processed,
one set of bases are adaptively selected to characterize the local sparse
domain. We further introduce two adaptive regularization terms into the sparse
representation framework. First, a set of autoregressive (AR) models are
learned from the dataset of example image patches. The best fitted AR models to
a given patch are adaptively selected to regularize the image local structures.
Second, the image non-local self-similarity is introduced as another
regularization term. In addition, the sparsity regularization parameter is
adaptively estimated for better image restoration performance. Extensive
experiments on image deblurring and super-resolution validate that by using
adaptive sparse domain selection and adaptive regularization, the proposed
method achieves much better results than many state-of-the-art algorithms in
terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI
- …