Unit Root Test for Panel Data AR(1) Time Series Model With Linear Time Trend and Augmentation Term: A Bayesian Approach

The objective of present study is to develop a time series model for handling the non-linear trend process using a spline function. Spline function is a piecewise polynomial segment concerning the time component. The main advantage of spline function is the approximation, non linear time trend, but linear time trend between the consecutive join points. A unit root hypothesis is projected to test the non stationarity due to presence of unit root in the proposed model. In the autoregressive model with linear trend, the time trend vanishes under the unit root case. However, when non-linear trend is present and approximated by the linear spline function, through the trend component is absent under the unit root case, but the intercept term makes a shift with r knots. For decision making under the Bayesian perspective, the posterior odds ratio is used for hypothesis testing problems. We have derived the posterior probability for the assumed hypotheses under appropriate prior information. A simulation study and an empirical application are presented to examine the performance of theoretical outcomes.

Download Full-text

Purchasing Power Parity in Commonwealth of Independent States

International Conference on Eurasian Economies 2014 ◽

10.36880/c05.01011 ◽

2014 ◽

Author(s):

Gülçin Güreşçi Pehlivan ◽

Esra Ballı ◽

Muammer Tekeoğlu

Keyword(s):

Time Series ◽

Panel Data ◽

Cross Section ◽

Purchasing Power Parity ◽

Unit Root ◽

Unit Root Test ◽

Purchasing Power ◽

Power Parity ◽

Panel Unit Root ◽

Independent States

The Purchasing Power Parity suggests that differences in relative prices in two countries move together with nominal exchange rates in the long run. This study examines the validity of PPP as transition economies for Commonwealth of Independent States (CIS). Purchasing Power Parity holds only when the real exchange rate is stationary in the equation. To test the stationary, we used both time series and panel data analysis. Testing unit root both with time series and panel data in this study, provides us double check of the results. We also test the cross sectional dependence to choose the appropriate panel unit root test. Our test statistics indicate that there is cross section dependence between countries. Hence, one needs to take into consideration the cross section dependence while undertaking unit root tests. Otherwise, the results would be biased. ADF and KPPS indicate that PPP cannot be accepted for the countries except for Russia. According to the panel unit root test results indicate that PPP does not hold for Armenia, Belarus, Georgia, Kazakhstan and Kyrgyzstan except for Russia.

Download Full-text

Panel data unit root test with structural break: A Bayesian approach

Hacettepe Journal of Mathematics and Statistics ◽

10.15672/hjms.2018.626 ◽

2018 ◽

Vol 48 (3) ◽

Author(s):

Jitendra Kumar ◽

Varun Agiwal

Keyword(s):

Panel Data ◽

Bayesian Approach ◽

Unit Root ◽

Structural Break ◽

Unit Root Test

Download Full-text

Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm

10.20944/preprints202007.0306.v1 ◽

2020 ◽

Author(s):

Varun Agiwal ◽

Jitendra Kumar ◽

Yau Chun Yip

Keyword(s):

Time Series ◽

Time Trend ◽

Spline Function ◽

Linear Time ◽

Linear Trend ◽

Model Parameters ◽

Linear Pattern ◽

Linear Time Trend ◽

Proposed Model ◽

Non Linear

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.

Download Full-text

Tinjauan terhadap Metode Ekonometrika Lanjutan

Jurnal Ekonomi dan Pembangunan Indonesia ◽

10.21002/jepi.v4i1.133 ◽

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.

Download Full-text