The purpose of this paper is to introduce and examine two alternative, although similar, approaches to the Moving Blocks and subsampling Bootstraps to bootstrapping the estimator of the parameters for time series regression models. More specifically, the first bootstrap is based on resampling...

We propose a modification of kernel time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that has to be estimated from the data. We establish the asymptotic...

This study aims to analyze the series of daily prices of soybeans in the North of Parana, which includes the timeline of the years 2000 (from January) to 2011 (until October) and describe their behavior with short-term forecasts. That is, verify if the temporal dynamics of the variable is better...

The intermittency of a time series can be defined as its normalized difference in scaling parameters. We establish the central limit theorem for the estimates of intermittency under the null hypothesis of a random walk. Simulations of random walks indicate that the distribution of intermittency...

We develop an anomaly-detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly-free inputs (i.e., those before contamination) to originate from a wide class of...

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso...

In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in...

This paper develops a novel method to correct small-sample bias in autoregressive roots of AR(p) models. We evaluate median-bias properties and variability of the bias-adjusted parameters by examining the accuracy of bias-adjusted impulse responses. Our simulation results show that bias...

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different robust' policy recommendations. Therefore, we develop new methods to analyze uncertainty about...