scholarly journals Sieve Bootstrap Test for Changes between Unit Root Process and Fractional Integrated Processes

2018 ◽  
Vol 07 (02) ◽  
pp. 111-116
Author(s):  
明灿 何
Keyword(s):  
Unit Root ◽  
Bootstrap Test ◽  
Sieve Bootstrap ◽  
Unit Root Process ◽  
Integrated Processes

Integrated processes and the discrete cosine transform

2001 ◽  
Vol 38 (A) ◽  
pp. 105-121
Author(s):  
Robert B. Davies
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Brownian Motion ◽  
White Noise ◽  
Discrete Cosine Transform ◽  
Unit Root ◽  
Stationary Time Series ◽  
Cosine Transform ◽  
Stationary Time ◽  
Integrated Processes

A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.

scholarly journals A SIEVE BOOTSTRAP TEST FOR COINTEGRATION IN A CONDITIONAL ERROR CORRECTION MODEL

2009 ◽  
Vol 26 (3) ◽  
pp. 647-681 ◽  
Author(s):  
Franz C. Palm ◽  
Stephan Smeekes ◽  
Jean-Pierre Urbain
Keyword(s):  
Error Correction ◽  
Single Equation ◽  
Error Correction Model ◽  
Small Sample ◽  
Bootstrap Test ◽  
Correction Model ◽  
Sieve Bootstrap ◽  
Asymptotic Test ◽  
True Value ◽  
Deterministic Components

In this paper we propose a bootstrap version of the Wald test for cointegration in a single-equation conditional error correction model. The multivariate sieve bootstrap is used to deal with dependence in the series. We show that the introduced bootstrap test is asymptotically valid. We also analyze the small sample properties of our test by simulation and compare it with the asymptotic test and several alternative bootstrap tests. The bootstrap test offers significant improvements in terms of size properties over the asymptotic test, while having similar power properties. The sensitivity of the bootstrap test to the allowance for deterministic components is also investigated. Simulation results show that the tests with sufficient deterministic components included are insensitive to the true value of the trends in the model and retain correct size.

Fathoming the theta method for a unit root process

2012 ◽  
Vol 25 (1) ◽  
pp. 105-124 ◽  
Author(s):  
D. Thomakos ◽  
K. Nikolopoulos
Keyword(s):  
Unit Root ◽  
Unit Root Process ◽  
Theta Method

AN INVARIANCE PRINCIPLE FOR SIEVE BOOTSTRAP IN TIME SERIES

2002 ◽  
Vol 18 (2) ◽  
pp. 469-490 ◽  
Author(s):  
Joon Y. Park
Keyword(s):  
Time Series ◽  
Invariance Principle ◽  
Unit Root ◽  
Autoregressive Process ◽  
Linear Process ◽  
Linear Processes ◽  
Testing Procedures ◽  
Sieve Bootstrap ◽  
Model Driven ◽  
Unit Root Models

This paper establishes an invariance principle applicable for the asymptotic analysis of sieve bootstrap in time series. The sieve bootstrap is based on the approximation of a linear process by a finite autoregressive process of order increasing with the sample size, and resampling from the approximated autoregression. In this context, we prove an invariance principle for the bootstrap samples obtained from the approximated autoregressive process. It is of the strong form and holds almost surely for all sample realizations. Our development relies upon the strong approximation and the Beveridge–Nelson representation of linear processes. For illustrative purposes, we apply our results and show the asymptotic validity of the sieve bootstrap for Dickey–Fuller unit root tests for the model driven by a general linear process with independent and identically distributed innovations. We thus provide a theoretical justification on the use of the bootstrap Dickey–Fuller tests for general unit root models, in place of the testing procedures by Said and Dickey and by Phillips.

scholarly journals A Sieve Bootstrap For The Test Of A Unit Root

2003 ◽  
Vol 24 (4) ◽  
pp. 379-400 ◽  
Author(s):  
YOOSOON CHANG ◽  
JOON Y. PARK
Keyword(s):  
Unit Root ◽  
Sieve Bootstrap

scholarly journals Convolution Based Unit Root Processes: a Simulation Approach

2016 ◽  
Vol 5 (6) ◽  
pp. 22
Author(s):  
Fabio Gobbi
Keyword(s):  
Negative Correlation ◽  
Unit Root ◽  
Unit Roots ◽  
Copula Function ◽  
The State ◽  
Dependence Structure ◽  
Simulation Approach ◽  
State Variable ◽  
Unit Root Process

We propose a convolution based approach to the simulation of a modified version of a unit root process where the state variable $Y_{t-1}$ is dependent on the innovation $\varepsilon_t$. The dependence structure is given by a copula function $C$. We study by simulation the effect of a negative correlation on the properties of unit roots. We call this process C-UR(1).

Integrated processes and the discrete cosine transform

2001 ◽  
Vol 38 (A) ◽  
pp. 105-121 ◽  
Author(s):  
Robert B. Davies
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Brownian Motion ◽  
White Noise ◽  
Discrete Cosine Transform ◽  
Unit Root ◽  
Stationary Time Series ◽  
Cosine Transform ◽  
Stationary Time ◽  
Integrated Processes

A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.

THE EFFICIENT MARKET HYPOTHESIS REVISITED: EVIDENCE FROM THE FIVE SMALL OPEN ASEAN STOCK MARKETS

2012 ◽  
Vol 57 (03) ◽  
pp. 1250021 ◽  
Author(s):  
QAISER MUNIR ◽  
KOK SOOK CHING ◽  
FUMITAKA FUROUKA ◽  
KASIM MANSUR
Keyword(s):  
Stock Returns ◽  
Unit Root ◽  
Stock Prices ◽  
Stock Price ◽  
Efficient Market Hypothesis ◽  
Linear Series ◽  
Efficient Market ◽  
Threshold Autoregressive ◽  
Non Linear ◽  
Unit Root Process

The efficient market hypothesis (EMH), which suggests that returns of a stock market are unpredictable from historical price changes, is satisfied when stock prices are characterized by a random walk (unit root) process. A finding of unit root implies that stock returns cannot be predicted. This paper investigates the stock prices behavior of five ASEAN (Association of Southeast Asian Nations) countries i.e., Indonesia, Malaysia, Philippines, Singapore and Thailand, for the period from 1990:1 to 2009:1 using a two-regime threshold autoregressive (TAR) approach which allows testing nonlinearity and non-stationarity simultaneously. Among the main findings, our results indicate that stock prices of Malaysia and Thailand are a non-linear series and are characterized by a unit root process, consistent with the EMH. Furthermore, we find that stock prices of Indonesia, Philippines and Singapore follow a non-linear series, however, stock price indices are stationary processes that are inconsistent with the EMH.

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