Matched block bootstrap for resampling multiseason hydrologic time series

2005 ◽  
Vol 19 (18) ◽  
pp. 3659-3682 ◽  
Author(s):  
V. V. Srinivas ◽  
K. Srinivasan
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Hydrologic Time Series

An Application of Elman Networks In Treatment and Prediction of Hydrologic Time Series

2011 ◽  
Vol 9 (3) ◽  
pp. 148-156
Author(s):  
Leonardo G. Tampelini ◽  
Clodis Boscarioli ◽  
Sarajane M. Peres ◽  
Silvio C. Sampaio
Keyword(s):  
Time Series ◽  
Hydrologic Time Series

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

A Smooth Block Bootstrap for Statistical Functionals and Time Series

2015 ◽  
Vol 36 (3) ◽  
pp. 442-461 ◽  
Author(s):  
Karl B. Gregory ◽  
Soumendra N. Lahiri ◽  
Daniel J. Nordman
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Statistical Functionals

Measuring nonlinear dependence in hydrologic time series

2008 ◽  
Vol 23 (7) ◽  
pp. 907-916 ◽  
Author(s):  
H. S. Kim ◽  
K. H. Lee ◽  
M. S. Kyoung ◽  
B. Sivakumar ◽  
E. T. Lee
Keyword(s):  
Time Series ◽  
Nonlinear Dependence ◽  
Hydrologic Time Series

A New Non-Parametric Subspace-Based Approach for Unit Root Test

2019 ◽  
Vol 18 (01) ◽  
pp. 1950009 ◽  
Author(s):  
Mehran Farahikia ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Unit Root Test ◽  
Statistical Testing ◽  
Block Bootstrap ◽  
Testing Methodology ◽  
Non Parametric

A new non-parametric subspace-based approach is introduced for unit root test in AR(1) process. The proposed approach contains block-bootstrap and spectrum properties of a time series as well as statistical testing methodology. The simulation result validates the proposed test and indicates its superiority compared with other existent procedures.

scholarly journals Models for Expected Returns with Statistical Factors

2020 ◽  
Vol 13 (12) ◽  
pp. 314
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Stock Prices ◽  
Bootstrap Method ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Two Factors ◽  
Resampling Procedure

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

Sign in / Sign up

Export Citation Format

Share Document