scholarly journals Unit root testing in the presence of innovation variance breaks: a simple solution with increased power

2002 ◽  
Vol 2 (5) ◽  
pp. 233-240 ◽  
Author(s):  
Steven Cook
Keyword(s):  
Monte Carlo ◽  
Unit Root ◽  
Unit Root Test ◽  
Simple Solution ◽  
Size Distortion ◽  
Unit Root Testing ◽  
Innovation Variance

The Dickey-Fuller unit root test is known to suffer severe oversizing in the presence of innovation variance breaks. In this paper, forward and reverse Dickey-Fuller regressions are proposed as a means of correcting this size distortion. The results of Monte Carlo experimentation show such an approach to result in both satisfactory size properties and increased power relative to previously suggested solutions.

scholarly journals THE WEAK FORM MARKET EFFICIENCY IN THE MSCI ETF INDICES: CONVENTIONAL AND THE FOURIER UNIT ROOT TEST ON THE DEVELOPED AND DEVELOPING COUNTRIES

2020 ◽  
Vol 8 (4) ◽  
pp. 409-423
Author(s):  
Sümeyra GAZEL
Keyword(s):  
Developing Countries ◽  
Unit Root ◽  
Structural Break ◽  
Unit Root Test ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Developed Countries ◽  
Exchange Traded Funds ◽  
Unit Root Testing ◽  
Daily Data

In this study, weak form efficiency of the Exchange Traded Funds (ETF) in the Morgan Stanley Capital International (MSCI) Index of developed and developing countries is tested. The Fourier Unit Root test, which does not lose its predictive power in terms of structural break date, number and form, is used on daily data. Also, conventional unit root tests are used for comparison between two different tests. Analysis results indicate common findings in some countries for both unit root testing. However, the Fourier unit root test results relatively more support the assumption of efficient market hypothesis that developed countries may be more efficient than developing countries.

scholarly journals A Different Look at Cointegration Relationship between Quarterly Inflation Rates and Growth via Seasonal Integration Tests

2019 ◽  
Author(s):  
Sera Şanlı ◽  
Mehmet Özmen
Keyword(s):  
Economic Growth ◽  
Unit Root ◽  
Unit Root Test ◽  
Unit Root Tests ◽  
Maximum Order ◽  
Unit Root Testing ◽  
Testing Sequence ◽  
Inflation Rates ◽  
Cointegration Relationship ◽  
Integration Analysis

Detecting the direction of inflation-growth relationship has been a controversial issue in terms of the theoretical framework, notedly since the rise of Mundell-Tobin effect which is based upon the assumption of substitutability between money and capital. In this study, it has been aimed to investigate the cointegrating relationship and its direction between inflation and economic growth covering the period 1998Q1:2014Q4 for Turkey as grounded on the testing sequence that is illustrated by Ilmakunnas (1990) in order to handle unit root testing in a seasonal context by testing the appropriate order of differencing and concerns with the case where SI(2,1) (seasonally integrated of order (2,1)) is the maximum order of seasonal integration. It has been also utilized from ADF unit root test and DHF, HEGY & OCSB seasonal unit root tests in seasonal integration analysis. In the study, five cointegration regressions have been considered in the level, seasonally averaged, quarterly differenced, first differenced and twice differenced forms and two series have been found to have the same degree of seasonal integration as SI(1,1). Applying various residual tests have revealed the presence of a cointegrating relationship between two variables. In addition, the inflation-growth relationship in Turkey has been concluded to perform in an opposite direction.

scholarly journals MEASURING CO-MOVEMENT OF REAL INTEREST RATE AND INFLATION RATE: USING VEC APPROACH

2020 ◽  
Vol 7 (6) ◽  
pp. 66-76
Author(s):  
Md. Rasel Hossain ◽  
Ahsanul Haque ◽  
Md. Abdullah Amir Hamja ◽  
M. Shohel Rana
Keyword(s):  
Causal Relationship ◽  
Unit Root ◽  
Unit Root Test ◽  
Testing Procedure ◽  
Vector Error Correction ◽  
Unit Root Testing ◽  
Vec Model ◽  
Long Run ◽  
Short Run ◽  
Johansen Cointegration Test

It is important to know the future movement of economic variables for the planning and development of a country, Vector Error Correction (VEC) Model has been applied to disclose hidden long run as well as short-run patterns of the selected variables. ADF unit root testing procedure was applied to satisfy the conditions of applying the VEC Model. Using Johansen cointegration test long-run cointegration has been justified. But the VEC model reveals that long run significant causal relationship between the variables whereas there is no short-run causal relationship. The parameter was estimated using the OLS estimation technique. The validity of the model was confirmed by applying different quantitative approaches such as normality test, autocorrelation test, Portmanteau test, Unit root test, and various graphical approaches which suggested model selection and estimation were correct. The result of this present study may help Govt. agencies as well as planners to take an idea.

A CORRECTION FACTOR FOR UNIT ROOT TEST STATISTICS

1999 ◽  
Vol 15 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Francesco Bravo
Keyword(s):  
Likelihood Ratio ◽  
Unit Root ◽  
Unit Root Test ◽  
Ratio Test ◽  
Test Statistics ◽  
Size Distortion ◽  
Size Distortions ◽  
Type Factor ◽  
First Four Moments

Despite the fact that it is not correct to speak of Bartlett corrections in the case of nonstationary time series, this paper shows that a Bartlett-type correction to the likelihood ratio test for a unit root can be an effective tool to control size distortions. Using well-known formulae, we obtain second-order (numerical) approximations to the moments and cumulants of the likelihood ratio, which makes it possible to calculate a Bartlett-type factor. It turns out that the cumulants of the corrected statistic are closer to their asymptotic value than the original one. A simulation study is then carried out to assess the quality of these approximations for the first four moments; the size and the power of the original and the corrected statistic are also simulated. Our results suggest that the proposed correction reduces the size distortion without affecting the power too much.

scholarly journals MEASURING CO-MOVEMENT OF REAL INTEREST RATE AND INFLATION RATE: USING VEC APPROACH

2020 ◽  
Vol 7 (6) ◽  
pp. 66-76
Author(s):  
Md. Rasel Hossain ◽  
Ahsanul Haque ◽  
Md. Abdullah Amir Hamja ◽  
M. Shohel Rana
Keyword(s):  
Causal Relationship ◽  
Unit Root ◽  
Unit Root Test ◽  
Testing Procedure ◽  
Vector Error Correction ◽  
Unit Root Testing ◽  
Vec Model ◽  
Long Run ◽  
Short Run ◽  
Johansen Cointegration Test

It is important to know the future movement of economic variables for the planning and development of a country, Vector Error Correction (VEC) Model has been applied to disclose hidden long run as well as short-run patterns of the selected variables. ADF unit root testing procedure was applied to satisfy the conditions of applying the VEC Model. Using Johansen cointegration test long-run cointegration has been justified. But the VEC model reveals that long run significant causal relationship between the variables whereas there is no short-run causal relationship. The parameter was estimated using the OLS estimation technique. The validity of the model was confirmed by applying different quantitative approaches such as normality test, autocorrelation test, Portmanteau test, Unit root test, and various graphical approaches which suggested model selection and estimation were correct. The result of this present study may help Govt. agencies as well as planners to take an idea.

Unit Root Testing in the Presence of Mean Reverting Jumps: Evidence from US T-Bond Yields

2019 ◽  
Vol 20 (2) ◽  
pp. 145-152
Author(s):  
Deniz Ilalan ◽  
Özgür Özel
Keyword(s):  
Interest Rates ◽  
Unit Root ◽  
Unit Root Test ◽  
Mean Reversion ◽  
Point Of View ◽  
Bond Yields ◽  
Unit Root Testing ◽  
Augmented Dickey Fuller ◽  
Treasury Bond ◽  
Adf Test

AbstractMean reversion of financial data, especially interest rates is often tested by linear unit root tests. However, there are times where linear unit root test results can be misleading especially when mean reverting jump formations are at stage. Considering this framework, we provide a new unit root testing methodology and compute its asymptotic critical values via Monte Carlo simulation. Moreover, we numerically compare the power of this generalized mean reversion test with the pioneering linear unit root test in the literature namely the Augmented Dickey Fuller (ADF) test. We deduce that our test is a refinement of ADF test with a higher power. We apply our findings to US 10-year Treasury bond yields. We aim to shed light to the discussion among researchers whether interest rates can sometimes revert to a long-term constant mean or not from an unorthodox point of view.

UNIT ROOT TESTS IN THE PRESENCE OF MULTIPLE BREAKS IN VARIANCE

2017 ◽  
Vol 62 (02) ◽  
pp. 345-361
Author(s):  
SOO-BIN JEONG ◽  
BONG-HWAN KIM ◽  
TAE-HWAN KIM ◽  
HYUNG-HO MOON
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Unit Root Tests ◽  
Series Data ◽  
Simulation Experiments ◽  
Innovation Variance ◽  
Multiple Breaks

Spurious rejections of the standard Dickey–Fuller (DF) test caused by a single variance break have been reported and some solutions to correct the problem have been proposed in the literature. Kim et al. (2002) put forward a correctly-sized unit root test robust to a single variance break, called the KLN test. However, there can be more than one break in variance in time series data as documented in Zhou and Perron (2008), so allowing only one break can be too restrictive. In this paper, we show that multiple breaks in variance can generate spurious rejections not only by the standard DF test but also by the KLN test. We then propose a bootstrap-based unit root test that is correctly-sized in the presence of multiple breaks in variance. Simulation experiments demonstrate that the proposed test performs well regardless of the number of breaks and the location of the breaks in innovation variance.

scholarly journals BOOTSTRAP-ASSISTED UNIT ROOT TESTING WITH PIECEWISE LOCALLY STATIONARY ERRORS

2018 ◽  
Vol 35 (1) ◽  
pp. 142-166 ◽  
Author(s):  
Yeonwoo Rho ◽  
Xiaofeng Shao
Keyword(s):  
Unit Root ◽  
Unit Root Test ◽  
Linear Process ◽  
Wild Bootstrap ◽  
Finite Sample ◽  
Unknown Parameters ◽  
Null Distributions ◽  
Inference Problems ◽  
Unit Root Testing ◽  
Finite Sample Simulations

In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order properties. Under this framework, the limiting null distributions of the conventional unit root test statistics are derived and shown to contain a number of unknown parameters. To circumvent the difficulty of direct consistent estimation, we propose to use the dependent wild bootstrap to approximate the nonpivotal limiting null distributions and provide a rigorous theoretical justification for bootstrap consistency. The proposed method is compared through finite sample simulations with the recolored wild bootstrap procedure, which was developed for errors that follow a heteroscedastic linear process. Furthermore, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in a nonstationary setting is demonstrated for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.

scholarly journals A Simple Integration Order Test: An Alternative to Unit Root Testing

2021 ◽  
Vol 2 (3) ◽  
pp. 77-85
Author(s):  
C. G. Amaefula
Keyword(s):  
Unit Root ◽  
Time Series Data ◽  
Test Procedure ◽  
Unit Root Test ◽  
Unit Root Tests ◽  
Stationary Time Series ◽  
Series Data ◽  
Unit Root Testing ◽  
Integration Test ◽  
Integration Order

The paper introduces order of integration test (OIT) which serves as a simple alternative to unit root test built generally using auxiliary autoregressive AAR(3) model. The parametric boundary conditions necessary and sufficient for testing the null hypothesis that the non-stationary variable under test is integrated order zero I(0) were estimated via generalized least squares (GLS). The decision on the hypothesis is evaluated using t-statistic. The test procedure was applied to a simulated non-stationary series (y1) of sample size n = 2000 and a known non-stationary time series data (y2) with two unit roots. The results showed that y1 is integrated order one (I(1)) and y2 is I(2). These results were confirmed by Augmented Dickey Fuller (ADF); Phillips-Perron (PP); Kwiatkowski, Phillips, Schmidt, and Shin (KPSS); Elliot, Rothenberg, and Stock Point Optimal (ERS) and Ng and Perron (NP) unit root tests. For logarithm transformed variable, the divergent opinions of other unit root tests in clear-cut solution of the integrated order of such variable makes the new test procedure a better alternative. Nevertheless, the simplicity and aptness of the integration order test give it leverage over conventional methods of unit root test.

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