Another Look at the Stationarity of Inflation rates in OECD countries: Application of Structural break-GARCH-based unit root tests

This paper re-investigates unit root hypotheses in inflation rates for 21 OECD countries using the newly proposed GARCH-based unit root tests with structural break and trend specifications. The results showed that classical tests over-accept unit roots in inflation rates, whereas these tests are not robust to heteroscedasticity. As observed from the pre-tests, those tests with structural break reject more null hypotheses of unit roots of most inflation series. By applying variants of GARCH-based unit root tests which include those with structural breaks and time trend regression specifications, we found that unit root tests without time trend gave most rejections of the conventional unit root. Thus, care should be taken while applying variants of the new unit root tests on weak trending time series as indicated in this work

Symmetric Variants of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences

The Smooth Transition Autoregressive (STAR) models are becoming popular in modeling economic and financial time series. The asymmetric type of the model is the Logistic STAR (LSTAR) model, which is limited in its applications as a result of its asymmetric property, which makes it suitable for modelling specific macroeconomic time series. This study was designed to develop the Absolute Error LSTAR (AELSTAR) and Quadratic LSTAR (QLSTAR) models for improving symmetry and performance in terms of model fitness. Modified Teräsvirta’s Procedure (TP) and Escribano and Jordá\u27s Procedure (EJP) were used to test for nonlinearity in the series. The performance of the AELSTAR and QLSTAR models showed that TP and EJP realized time series with improved symmetry as indicated by the lower relative frequencies than that realized with the existing LSTAR model. The AELSTAR model performed better than QLSTAR model at higher nonlinearity, and the selection of both models showed evidence of asymptotic property. The AELSTAR and QLSTAR models showed improved symmetry over the existing asymmetric LSTAR model

Specifying Asymmetric STAR models with Linear and Nonlinear GARCH Innovations: Monte Carlo Approach

Economic and finance time series are typically asymmetric and are expected to be modeled using asymmetrical nonlinear time series models. Smooth Transition Autoregressive (STAR) models: Logistic (LSTAR) and Exponential (ESTAR) are known to be asymmetric and symmetric respectively. Under non-normal and heteroscedastic innovations, the residuals of these models are estimated using Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models with variants which include linear and nonlinear forms. The small sample properties of STAR-GARCH variants are yet to be established but these properties are investigated using Monte Carlo (MC) simulation. An MC investigation was conducted to investigate the performance of selections of STAR-GARCH models by classical nonlinear selection approaches. The ARCH(1) and GARCH(1,1) models were the linear GARCH specifications while the Logistic Smooth Transition-ARCH (LST-ARCH(1,1)), Logistic Smooth Transition- GARCH (LST-GARCH(1,1)) and Asymmetric Nonlinear Smooth Transition-GARCH (ANST-GARCH(1,1)) models were the nonlinear GARCH specifications. The nonlinearity parameter in the variance equations and Autoregressive (AR) parameters were varied along with different sample sizes. With the assumption of normality, the results showed that the selection of LSTAR models were actually affected by the structure of the innovations and this improved as sample size increased. Misspecification tests showed that these models cannot be misrepresented in the real sense

Modelling Long-Range Dependence and Non-linearity in the Infant Mortality Rates of African Countries.

Infant mortality rates in 34 Sub-Saharan African countries (1960–2016), obtained from the Federal Reserve Bank of St. Louis database, were examined in this paper by focusing on the degree of persistence and non-linearities in the growth rate series. Persistence deals with the degree of association between the observations. Non-linearity occurs when departing from the linear assumption as in a time trend. These two issues are relevant in this context because they are intimately related. Based on the high degree of persistence observed in the series examined, instead of investigating structural breaks, which produce abrupt changes in the data, a non-linear approach was used based on Chebyshev polynomials in time, producing smooth rather than abrupt changes. This approach has never been examined in a unified framework in the treatment of infant mortality rates. The results indicate that half of the countries examined display non-linearities and the orders of integration of the series are extremely large in all cases, being around two in the majority of them. Looking at the growth rate series, significant negative trends were observed for: Chad, Equatorial Guinea and Mozambique. Evidence of mean reversion and thus transitory shocks, were observed for Lesotho, Rwanda, Botswana and Mozambique. Time dynamics of the series were expected to persist in order to ascertain the decline in mortality rates. Therefore, serious government interventions are required in managing infant health in these countries.pre-print314 K

The Persistence of Stock Market Returns during the Presidential elections in Nigeria

Following empirical evidences that political activities impact stock market performance, this present paper examines efficiency and volatility of Nigerian stock market during presidential elections. We use a 5-month event window approach to obtain the data for each election period. This implies that for each election period, we obtain the daily stock price index for the election month (4 weeks) and two months (8 weeks) before and after it. Our fractional integration technique reveals that the stock price index was persistent during most of the election years, with the exemptions of 2011 and 2019 election year, while 2015 election period recorded the highest volatility. However, accounting for structural breaks following the approach of Enders and Lee (2012a,b) that inculcates nonlinear smooth breaks in the Fourier function, the stock market seemed to be efficient only during the 1999, 2011 and 2019 presidential election periods. The 2011 and 2019 are periods when the elections produced candidates that ran for a two-term each. On the other hand, the highest stock market volatility is still maintained at the 2015 election which was also interestingly the year that the recent 2015/2016 recession in the country kick-started. Our findings have important policy implications for potential investors

Estimates and Forecasts of GARCH Model under Misspecified Probability Distributions: A Monte Carlo Simulation Approach

The effect of misspecification of correct sampling probability distribution of Generalized Autoregressive Conditionally Heteroscedastic (GARCH) processes is considered. The three assumed distributions are the normal, Student t, and generalized error distributions. The GARCH process is sampled using one of the distributions and the model is estimated based on the three distributions in each sample. Parameter estimates and forecast performance are used to judge the estimated model for performance. The AR-GARCH-GED performed better on the three assumed distributions; even, when Student t distribution is assumed, AR-GARCH-Student t does not perform as the best model

Misspecification of Variants of Autoregressive GARCH Models and Effect on In-Sample Forecasting

Generally, in empirical financial studies, the determination of the true conditional variance in GARCH modelling is largely subjective. In this paper, we investigate the consequences of choosing a wrong conditional variance specification. The methodology involves specifying a true conditional variance and then simulating data to conform to the true specification. The estimation is then carried out using the true specification and other plausible specification that are appealing to the researcher, using model and forecast evaluation criteria for assessing performance. The results show that GARCH model could serve as better alternative to other asymmetric volatility models

On Structural Breaks and Nonstationary Fractional Intergration in Time Series

The growth of an economy is determined largely by the growth of its Gross Domestic Product (GDP) over time. However, GDP and some economic series are characterized by nonstationarity, structural breaks and outliers. Many attempts have been made to analyze these economic series assuming unit root process even in the presence of changes in the mean level without considering possible fractional integration. This paper aims at examining the structural breaks and nonstationarity in the GDP series of some selected African countries with a view to determining the influence of structural breaks on the level of stationarity of these series. These series are found to be nonstationary with some evidence of long memory. They were found to experience one or more breaks over the years and this may be due to instability in the government and economic policies in the selected African countries. The measure of relative efficiency shows that autoregressive fractional integrated moving average (ARFIMA) models is better than the corresponding autoregressive integrated moving average (ARIMA) models for the series considered in this study. Keywords: fractional integration, gross domestic product, structural break

Determinants of Desired and Actual Number of Children and the Risk of having more than Two Children in Ghana and Nigeria

This paper investigates the levels and determinants of desired and actual number of children, and the risk of having more than two children in Ghana and Nigeria using 2013 and 2014 Demographic Health Survey (DHS) conducted in the two countries, respectively. The question is whether the rate of childbearing in sub-Saharan African countries can be slowed down by changes in fertility demand-driven factors such as economic, socio-cultural and family planning programs. Our analysis results showed that these two countries are yet to adopt replacement level policy. Thus, putting every socio-demographic conditions that support fertility in place, these sub-Saharan African countries will continue to increase average childbearing per household, particularly in the case of Nigeria. Thus, government could intervene in the area of education of mothers since this will delay the age at first birth and serve as avenue to orientate women on family planning measures

Social Structure and Variation in the Family Formation Process: The Case of Age at First Marriage and Duration between First Marriage and First Birth in selected sub-Saharan African Countries

The present study employed the Cox proportional hazard regression model to examine the effect of select socio-demographic factors on the family formation process in five sub-Saharan African countries using the most recent Demographic and Health Surveys (DHS) data for the respective countries. Specifically, the study examined the effects of education, residence, religion and age at first marriage on age at first marriage, age at first birth and the duration between first marriage and first childbirth in Ethiopia, Ghana, Kenya, Nigeria and Zambia. On the whole, we found that Ghana has the highest median age at first marriage, while Ethiopia has the lowest median age at first marriage. Both level of urbanisation and education were inversely related to the proportion of women ever married in all five countries. While education was positively associated with age at first marriage in all five countries, it was negatively associated with duration between marriage and first birth in all the countries with the exception of Ghana. With the exception of Ghana and Nigeria, there were no statistically significant differences in the median age at first marriage between rural and urban areas in Ethiopia and Kenya; the hazard of early marriage was higher in urban than rural settings in Zambia