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 Economic Growth, Inflation and Monetary Policy in Pakistan: Preliminary Empirical Estimates

2021 ◽  
Vol 4 (2) ◽  
pp. 321-333
Author(s):  
Hina Ali ◽  
Malka Liaquat ◽  
Noreen Safdar ◽  
Saeed ur Rahman
Keyword(s):  
Time Series ◽  
Monetary Policy ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Economic Activities ◽  
Data Set ◽  
Explanatory Variables ◽  
Empirical Estimates

In economic policy, construction Inflation is a core variable to be considered that determines the economic activity. To make a suitable monetary policy, it is very essential to check the price level and later on, many other variables are considered to achieve the goal. This study aims to reveal the affiliation of inflation on the growth of economic activities in Pakistan. Time series data set for the period 1989-2020 was used to have the empirical estimates.  Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag techniques are used for empirical estimates. The present research uses Inflation as a dependent variable and Gross Domestic Product, Interest Rate, Money Supply, and Exchange Rate as the explanatory variables of the study. The findings of this analysis reveal that there's an antagonistic relation between Inflation and GDP.

scholarly journals Tinjauan terhadap Metode Ekonometrika Lanjutan

2003 ◽  
Vol 4 (1) ◽  
pp. 59-74
Author(s):  
Telisa Aulia Falianty
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Panel Data ◽  
Unit Root ◽  
Time Series Data ◽  
Random Effect ◽  
Unit Root Test ◽  
Cointegration Analysis ◽  
Series Data ◽  
Theory Of Evolution

Econometric models have been played an increasingly important role in empirical analysis in economics. This paper provides an overview on some advanced econometric methods that increasingly used in empirical studies.A panel data combines features of both time series and cross section data. Because of increasing availability of panel data in economic sciences, panel data regression models are being increasingly used by researcher. Related to panel data model, there are some methods that will be discussed here such as fixed effect and random effect. A new approach to panel data that developed by Im, Shin, and Pesaran (2002) for testing unit root in heterogenous panel is included in this overview.When we work with time series data, there are many problems that we must handle, most of them are unit root test, cointegration among non stationary variables, and autoregressive conditional heteroscedasticity. Provided these problems, author also review about ADF and Philips-Perron test. An approch to cointegration analysis developed by Pesaran (1999), ARCH and GARCH model are also interesting to be discussed here.Bayesian econometric, that less known than classical econometric, is includcd in this overview. The genctic algorithm, a relatively new method in econometric, has bcen increasingly employed the behavior of economic agents in macroeconomic models. The genetic algorithm is based on thc process of Darwin’s Theory of Evolution. By starting with a set of potential solutions and changing them during several iterations, the Genetic Algorithm hopes to converge on the most ‘fit’ solutions.

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.

scholarly journals KOINTEGRASI DAN KAUSALITAS INDEKS HARGA SAHAM GABUNGAN DAN DOW JONES INDUSTRIAL INDEX

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Paulus Sulluk Kananlua
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Unit Root ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Unit Root Test ◽  
Variance Decomposition ◽  
Cointegration Test ◽  
Series Data ◽  
The Impact

This research is obviously intended to analyze the impact of global financial crisis which happened in America and surrogated by the Dow Jones Industrial Index (DJI) towards the Indonesian Stock Exchange, represented by the composite index (IHSG). The study is conducted by using time series data ranging from January 2007 to July 2014. Data used consists of 60 months observation. In order to examine the time series data, Vector Autoregressive Model (VAR) is employed. We run the statistical tool to estimate the respon caused by the shock of research variable. Before estimating the model of Vector Autoregression (VAR), the data used must following the unit root test, cointegration test, granger causality test, and then runned by using VAR model. Our result reveals that the data is not stationer at level, but stationer at first difference. The interpreted estimation output resulting from impulse response function and variance decomposition show that DJI’s respons is much bigger caused by the shock from DJI itself with average number stand on 99.36%. Further, the proportion of IHSG on average is 0.64%. Meanwhile the respon of IHSG sparked by the DJI is 53.10% on average. The remained value as 46.90% is caused by the shock from IHSG.  Key Words: DJI, IHSG, VAR, Unit Root Test, Cointegration Test, Granger Test, Impulse Response,Variance Decomposition

scholarly journals Economic Transformation in Bangladesh and the Income Velocity of Broad Money: An Econometric Analysis

2019 ◽  
pp. 408-417
Author(s):  
Ms. Sharmina Khanom
Keyword(s):  
Unit Root ◽  
Positive Relationship ◽  
Time Series Data ◽  
Negative Relationship ◽  
Unit Root Test ◽  
Economic Transformation ◽  
Econometric Analysis ◽  
Series Data ◽  
Gdp Growth ◽  
Velocity Of Money

This study has undertaken an econometric analysis of economic transformation and income velocity of broad money. To find out the relevant determinants of income velocity of money this paper used time series data on year basis. This paper focus to discover the key determinants of the velocity of money in Bangladesh using the Augmented Dicky Fuller (ADF) unit root test to inspect the stationary, Engle-Granger residual-based cointegration approach to demonstrate the co-integrating association among variables. The main conclusions of this paper are: (i) relationship exists between the velocity of money and financial development. Other important variables that determine GDP growth show a negative relationship with the velocity of money but maintain a positive relationship with the deposit interest rate. Finally, this study concludes by giving some policy recommends for Bangladesh with respect to the velocity of broad money and the monetary policy.

scholarly journals E-Fraud and Bank Performance: Empirical Evidence from Nigeria

2021 ◽  
Vol 06 (02) ◽  
Author(s):  
TYONA Timothy ◽  
Keyword(s):  
Empirical Evidence ◽  
Internal Control ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Bank Performance ◽  
Least Squares Regression ◽  
Online Fraud ◽  
Adf Test

This study examines e-fraud and bank performance: empirical evidence from Nigeria. Expo facto research design was used while time series data for the period of ten (10) years sourced from Central Bank of Nigeria (CBN) statistical Bulletin. Unit root test and correlation matrix was used as a diagnostic tests. The Augmented Dickey Fuller (ADF) test is used to test for stationarity. The results of the stationarity or unit root test show that all the variables, return on equity (ROE), Automated Teller Machine Fraud (ATF) and Online Fraud (OLF) have unit roots and are only stationary at first difference and integrated of order one I (1). The fully modified least squares regression (FMOLS) is used for the analysis. The result of the study indicates that both variables, online fraud, (OLF) and ATM fraud (ATF) show negative effect on bank performance proxied in Nigeria in line with a priori expectation. In order words, fraud and fraudulent activities impede on the profitability of the banks. Based on the results obtained from the regression and the analysis conducted, the study recommends among others that bank managers should strengthen their internal control systems at all times. The regulatory authorities should be up and doing concerning their supervisory functions. Appropriate disciplinary measures should be taken against culprits of e-frauds so as deter others with such intentions. Also, banks should hold regular trainings for their Information Technology staff to counter the activities of fraudsters that use electronic means to commit fraud.

Into the Civic life: The Nexus of Urbanization, Education and Women’s Empowerment in Pakistan

2021 ◽  
Vol 3 (2) ◽  
pp. 212-222
Author(s):  
Muhammad Abdullah ◽  
Ayza Shoukat ◽  
Muhammad Gulzaib Chaudhary
Keyword(s):  
Rural Areas ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Positive Association ◽  
Women's Empowerment ◽  
Women’S Empowerment ◽  
Series Data ◽  
Special Focus ◽  
Depth Analysis

Women comprise nearly 50 percent of the population of Pakistan which is enriched with a variety of regional, cultural, and ethnic values.  These values are traditionally responsible for limiting opportunities for women and keeping them less empowered. This study examines the link between education and urbanization that is empowering women in Pakistan. Time series data for the period of 1980 to 2019 has been used for empirical analysis. The stationarity of data has been checked by using the ADF unit root test. All the variables used in the study have a unit root at the level and become stationary at first difference. Johansen's co-integration technique is utilized to check the long-run relationship between the variables used in the study. Instead of using any single variable, we have constructed the Women Empowerment Index (WEI) by using multiple women-related indicators for in-depth analysis. Empirical findings indicate that women's empowerment is positively associated with education and urbanization in Pakistan. Other controlled variables include domestic credit with a positive association and inflation with a negative association. The study shows that empowering women is sensitive to urbanization and education. There must be women-specific educational and training institutions across the country with a special focus on rural areas for equal availability of opportunities for women of all cultures. Urbanization provides greater social, economic and political opportunities for women. Same opportunities should be provided for women in rural areas to make them more empowered. Moreover, control of inflation and the provision of credit on easy terms will also help to enhance women's contribution to economic activity in Pakistan.

scholarly journals Unit Roots in Time Series with Changepoints

2017 ◽  
Vol 6 (6) ◽  
pp. 127
Author(s):  
Ed Herranz ◽  
James Gentle ◽  
George Wang
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Null Hypothesis ◽  
Structural Changes ◽  
Linear Trend ◽  
Unit Root Test ◽  
Unit Roots ◽  
Cointegration Analysis ◽  
Unit Root Tests ◽  
Standard Unit

Many financial time series are nonstationary and are modeled as ARIMA processes; they are integrated processes (I(n)) which can be made stationary (I(0)) via differencing n times. I(1) processes have a unit root in the autoregressive polynomial. Using OLS with unit root processes often leads to spurious results; a cointegration analysis should be used instead. Unit root tests (URT) decrease spurious cointegration. The Augmented Dickey Fuller (ADF) URT fails to reject a false null hypothesis of a unit root under the presence of structural changes in intercept and/or linear trend. The Zivot and Andrews (ZA) (1992) URT was designed for unknown breaks, but not under the null hypothesis. Lee and Strazicich (2003) argued the ZA URT was biased towards stationarity with breaks and proposed a new URT with breaks in the null. When an ARMA(p,q) process with trend and/or drift that is to be tested for unit roots and has changepoints in trend and/or intercept two approaches that can be taken: One approach is to use a unit root test that is robust to changepoints. In this paper we consider two of these URT's, the Lee-Strazicich URT and the Hybrid Bai-Perron ZA URT(Herranz, 2016.)  The other approach we consider is to remove the deterministic components with changepoints using the Bai-Perron breakpoint detection method (1998, 2003), and then use a standard unit root test such as ADF in each segment. This approach does not assume that the entire time series being tested is all I(1) or I(0), as is the case with standard unit root tests. Performances of the tests were compared under various scenarios involving changepoints via simulation studies.  Another type of model for breaks, the Self-Exciting-Threshold-Autoregressive (SETAR) model is also discussed.

LONG MEMORY IN FINANCIAL TIME SERIES DATA WITH NON-GAUSSIAN DISTURBANCES

2003 ◽  
Vol 06 (02) ◽  
pp. 119-134 ◽  
Author(s):  
LUIS A. GIL-ALANA
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Unit Root ◽  
Time Series Data ◽  
Financial Time Series ◽  
Critical Values ◽  
Series Data ◽  
Finite Sample ◽  
Financial Time ◽  
Non Gaussian

In this article we propose the use of a version of the tests of Robinson [32] for testing unit and fractional roots in financial time series data. The tests have a standard null limit distribution and they are the most efficient ones in the context of Gaussian disturbances. We compute finite sample critical values based on non-Gaussian disturbances and the power properties of the tests are compared when using both, the asymptotic and the finite-sample (Gaussian and non-Gaussian) critical values. The tests are applied to the monthly structure of several stock market indexes and the results show that the if the underlying I(0) disturbances are white noise, the confidence intervals include the unit root; however, if they are autocorrelated, the unit root is rejected in favour of smaller degrees of integration. Using t-distributed critical values, the confidence intervals for the non-rejection values are generally narrower than with the asymptotic or than with the Gaussian finite-sample ones, suggesting that they may better describe the time series behaviour of the data examined.

scholarly journals A box-jenkins sarima analysis of dysentery cases in children aged below five at a provincial hospital in Zimbabwe

2020 ◽  
Vol 3 (7) ◽  
pp. 33-38
Author(s):  
Dr. Smartson. P. Nyon ◽  
Mr. Thabani Nyoni
Keyword(s):  
Unit Root ◽  
Catchment Area ◽  
Time Series Data ◽  
Unit Root Tests ◽  
Series Data ◽  
Policy Recommendation ◽  
Sample Period ◽  
Policy Makers ◽  
Provincial Hospital ◽  
Out Of Sample

This piece of work uses monthly time series data on new dysentery cases at Gweru Provincial Hospital (GPH) from Janaury 2010 to December 2018, to predict dysentery cases over the period January 2019 to December 2020. As confirmed by unit root tests, the series under consideration is basically an I (1) variable. The study applied the Box-Jenkins “catch all” model. Residual analysis of this model indicates that the model is stable and thus suitable for predicting dysentery cases at GPH over the out-of-sample period. The results of the study reveal that dysentery cases will be on the rise at GPH over the out-of-sample period; characterized by seasonal repeats in December each year. The study offers a two-fold policy recommendation in order to help policy makers in the fight against dysentery in children under five years of age within the GPH catchment area.

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