Testing For Serial Correlation Research Articles

Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?

To better understand the actual rating variables that affects Auto insurance Policyholders’ premium, this paper attempts to provide empirical evidence to justify which ones are significant and needed to be considered by insurers by adopting the autoregressive distributed lag (ARDL) model. In satisfying all the conditions for ARDL application, unit root, Heteroskedasticity, normality, dynamic stability and serial correlation tests were conducted. We estimate the effects of each rating variable on Premium taking into consideration whether the policy of the insured is Third-Party or Comprehensive. These rating variables in the ARDL model serves as the independent variables that establishes the short and long-run relationships between them and the Premium as the dependent variable. The results suggest that not all the classical rating variables used in the market significantly impact Premium. Whiles, to some extent, we found a varying degree of variables impact on Premium depending on the insurance type, the autos cubic capacity, which plays a cogent role on the basic Premium in Ghana, is insignificant. Also, policyholders’ age characteristics are statistically significant but are excluded in the premium calculations. Thus, this paper shows the need to consider all the other possible rating variables, including policyholders’ age into the Ghanaian insurance pricing system, whiles autos cubic capacity considering the weight it put on the basic Premium should be re-examined. This would help to obtain a financially balanced and optimal pricing system for policyholders.

Testing for Serial Correlation in Autoregressive Exogenous Models with Possible GARCH Errors.

Autoregressive exogenous, hereafter ARX, models are widely adopted in time series-related domains as they can be regarded as the combination of an autoregressive process and a predictive regression. Within a more complex structure, extant diagnostic checking methods face difficulties in remaining validity in many conditions existing in real applications, such as heteroscedasticity and error correlations exhibited between the ARX model itself and its exogenous processes. For these reasons, we propose a new serial correlation test method based on the profile empirical likelihood. Simulation results, as well as two real data examples, show that our method has a good performance in all mentioned conditions.

Analysis on the Relationship Between Coal Production and Coal Demand in Downstream Industries in Shanxi Province

Raw coal production is closely related to downstream demand, and it is crucial to grasp the relationship between downstream demand changes and changes in raw coal production. This quantitative relationship is not only an academic study, but also has practical significance for government management and enterprise production. In view of this, the relationship between raw coal production and downstream demand is studied through the systematic understanding of the current research status at home and abroad, and the relationship between raw coal production and downstream demand is studied through a combination of econometric model and Eviews analysis, qualitative and quantitative analysis. Data is obtained through official channels. Model the variables to obtain quantitative relationships. The final model was obtained by using the modified test of multicollinearity and serial correlation.

Healthy interaction between fiscal and monetary policies; A panacea for economic development and sustainability

A healthy fiscal and monetary policies form the basis for economic growth and sustainability of an economy. This work seeks to look at the effect of monetary and fiscal policies on economic growth in Nigeria, to examine how government tax alter the effect of monetary policy, To see how interaction of government recurrent expenditure with monetary policy does alter the effect of monetary policy rate, money supply and inflation on economic growth in Nigeria, and to see if the interaction of government capital expenditure with monetary policy affects monetary policy rate, money supply and inflation on economic growth in Nigeria. This study uses time series data from 1995 to 2021 which were sourced from the Central Bank of Nigeria Statistical Bulletin. The variables used in this study include; Money Supply (MS), Inflation (INF), Monetary Policy Rate (MPR), Government Tax Revenue (GTAX), Total Government Capital Expenditure (TGCE) and Total Government Recurrent Expenditure (TGRE). The data were examined using Ordinary Least Square Regression method and the robustness of the model was tested using post-estimation techniques such as Durbin-Watson test for autocorrelation, Jarque-Bera test for normality, Breusch-Godfrey Serial Correlation test and the Ramsey RESET test for specification correctness. Findings indicate that contractionary policies have caused reduced economic growth and the increase in monetary base in the economy has contributed significantly to economic growth. The study recommends Contractionary monetary policies should be matched with increase in taxation while expansionary monetary policies should be combined with a downward review of tax rates in the country.

Development of a flexible pavement condition index for urban road network

In the urban pavement management, it is essential to develop urban pavement condition indexes to support Municipal Secretariat for Infrastructure on decisions making in relation to maintenance and rehabilitation (M&R). The aim was to develop an urban pavement condition index (UPCI) for flexible pavements and perform a comparative analysis with some pavement condition indexes (PCI). The work method followed such steps: definition of sampling size; formation of rating panel and training; data collection about present serviceability rating (PSR) and flexible pavement distress; development of an UPCI; and, comparative analysis with some PCI. Multilinear regression modeling based on distresses and PSR carried out the UPCI, in accordance with some statistical assumptions such as homoscedasticity, serial correlation, multicollinearity and normality tests. The PCI chosen carried out variability between them and the PSR, which was statistically analyzed by errors and correlation tests. Lastly, the proposition of an urban pavement condition index for flexible pavement allows a better M&R planning, budget distribution and benefits to the population, because well-kept pavements promote comfort, travel time reduction and safety to the users.

THE IMPACT OF BANK RATE AND EXCHANGE RATE ON STOCK MARKET PERFORMANCE OF PAKISTAN: A CO-INTEGRATION ANAYSIS

The aim of this study is to highlight the impact of interest rate and exchange rate on stock market performance of Pakistan. Time series data for 20 years (2001-2021) collected from world development indicator. Multiple linear regression model is applied. For the analysis of data Ordinary Least Square, tests like multi-collinearity, Augmented Dickey Fuller test, serial correlation test, co-integration techniques were used to locate out the long as well as short run and significant relationship. The results show that the interest rate has negatively while the exchange rate positively and significantly affected the stock market performance. The study has a good scope for policy implication and suggest recommendation for the government to handle the bank rate and exchange rate to regulate the capital market of Pakistan to boost economic activity and accelerate GDP growth.

A novel hybrid ARFURIMA-APARCH model for modeling interminable long memory and asymmetric effect in time series

In this paper, we introduce a new hybrid model namely Autoregressive Fractional Unit Root Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The Nigeria daily COVID19 records and Bitcoin to EURO exchange rate that exhibit a type of Long Memory (LM) called Interminable LM (ILM), volatility and asymmetric (leverage) effect were used to show the applications of the proposed ARFURIMA-APARCH model. The existing Autoregressive Fractional Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFIMA-APARCH) model were estimated and compared with the ARFURIMA-APARCH model. Results showed that the new hybrid model is better based on goodness-of-fit, serial correlation tests and forecast measures of accuracy. As a conclusion, our study showed that the ARFURIMA-APARCH model performed better compared to the ARFIMA-APARCH hybrid model. Therefore, the ARFURIMA-APARCH model is a better option for modeling ILM, volatility and leverage effect of health and financial data. Future study should focus on the application of the developed hybrid ARFURIMA-APARCH model using some major economic indicators, for example, Gross Domestic Product (GDP), currency exchange rate, stock price index, interest rate and other financial data.

MULTIVARIATE TIME SERIES ANALYSIS IN MODELLING MALARIA CASES IN JIMETA METROPOLIS OF ADAMAWA STATE, NIGERIA

Sub-Sahara Africa harbours most of the Malaria burden including Nigeria. There are scanty studies that aim at modelling these cases particularly in the study area. This study therefore, focused on a multivariate time series model for malaria cases among the residents in Jimeta metropolis of Adamawa State. A secondary data on reported malaria cases for adults, pregnant women and children was collected from January 2011 to December 2020 on monthly basis from medical records at the specialist Hospital, Jimeta, Yola, Adamawa State. The vector autoregressive (VAR) model was employed for modelling. A descriptive analysis was performed on the data. The lag order selection for stationary VAR model suggest lag three as the optimal lag for VAR model with malaria cases among children, adult and pregnant women. To assess how well the model fit the data set, AIC of 26.9458 for model with lag (3) was best. The Breusch-Godfrey LM test for residual serial correlation of VAR model suggest no autocorrelation at each lag, there is no problem of autocorrelation, since the associated p-value is greater than the conventional 0.05 level of significance. Jarque-Bera test shows that the residuals are not normally distributed and the forecast made showed that, rates of malaria cases are higher among adult followed by children and then pregnant women.

A robust test for serial correlation in panel data models

We consider a new nonparametric test for serial correlation of unknown form in the estimated residuals of a panel regression model, where individual and time effects can be fixed or random, and the panel data can be balanced or unbalanced. Our test is robust against potential weak error cross-sectional dependence and error serial dependence in higher-order moments. This is in contrast to existing tests for serial correlation in panel data models, which assume error components to be cross-sectionally and serially independent. Our test has an asymptotic N(0, 1) distribution under the null hypothesis and is consistent against serial correlation of unknown form. No common alternative is assumed and hence our test allows for substantial inhomogeneity in serial correlation across individuals. A simulation study highlights the merits of the proposed test relative to a variety of existing tests in the literature. We apply the new test to the empirical study of Wolfers on the relationship between unilateral divorce laws and divorce rates and find strong evidence against serial uncorrelatedness even controlling for the fixed effect.

Simulation-Based Assessment of the Effectiveness of Tests for Stationarity

Non-stationarity potentially comes from many sources and they impact the analysis of a wide range of systems in various fields. There is a large set of statistical tests for checking specific departures from stationarity. This study uses Monte Carlo simulations over artificially generated time series data to assess the effectiveness of 16 statistical tests to detect the real state of a wide variety of time series (i.e., stationary or non-stationary) and to identify their source of non-stationarity, if applicable. Our results show that these tests have a low statistical power outside their scope of operation. Our results also corroborate with previous studies showing that there are effective individual statistical tests to detect stationary time series, but no effective individual tests for detecting non-stationary time series. For example, Dickey-Fuller (DF) family tests are effective in detecting stationary time series or non-stationarity time series with positive unit root, but fail to detect negative unit root as well as trend and break in the mean, variance, and autocorrelation. Stationarity and change point detection tests usually misclassify stationary time series as non-stationary. The Breusch-Pagan BG serial correlation test, the ARCH homoscedasticity test, and the structural change SC tests can help to identify the source of non-stationarity to some extent. This outcome reinforces the current practice of running several tests to determine the real state of a time series, thus highlighting the importance of the selection of complementary statistical tests to correctly identifying the source of non-stationarity.

Analysis of the Determinants of Public Education Expenditures in the Philippines

This study examines the values, trends, and variables that determine the public education expenditures of the Philippines from 1990 to 2019. The researchers used the following variables: Public Education, Culture, and Manpower Development Expenditure in Millions, Tax Revenue in % share GDP, Total Population in Millions, and Real Gross Domestic Product Per Capita, to pave the way for a coherent understanding of the determinants of public education expenditure and theories used to build up this economic construct. The study utilized a multivariate Ordinary Least Squares regression analysis needed for the variables, along with statistical measures to assess the significance of the model. Which includes the following: the values of the t-test and f-stat in their respective p valued forms for the significance of the economic model, tests for serial correlation through the Durbin-Watson test and Breusch-Godfrey test, a test for multicollinearity through Variance Inflation Factor, a test for heteroscedasticity through Goldfeld-Quandt test and White's Heteroscedasticity test, testing for specification errors will be done through Ramsey's RESET test, and test for normality will be through a graphical method Histogram. The key findings of this study suggest that the variables; economic growth, tax, and population growth show signs of positive relationship and negation with the dependent variable education expenditure.

The Impact Of Asset Management Efficiency Ratios on Earnings per Share Case Study of Industrial Companies Listed on the Amman Stock Exchange from 2005 to 2019)

This study aims to analyze the effect of the represented asset management efficiency (total asset turnover (TAT), fixed asset turnover (FAT), and working capital turnover (WCT)) on the earnings per share (EPS) of industrial companies listed on the Amman Stock Exchange (IASE) as the data were obtained from the Amman Stock Exchange (ASE) from 2005 to 2019, where the unit root test was analyzed for the time series of the study variables. Results revealed that all the variables stabilize at the first differences 1 (1), several diagnostic tests, such as variance instability, Ramsay stability, and serial correlation tests were also performed, all of which confirmed the fit and validity of the model used. Results showed the positive and strong impact of the asset turnover rate on EPS, the positive and strong impact of the fixed asset turnover rate on the return on profitability, and the positive impact of the (WCT) on EPS. Therefore, asset management efficiency positively affects the EPS. Moreover, this result indicates the efficiency of industrial companies in managing assets during the study period.

The role of climate change in food security; empirical evidence over Punjab regions, Pakistan

Climate change is bringing drastic changes to the food availability, accessibility, quality, and stability in the world. Pakistan heavily relies on production of wheat for food security. This study investigates the role of climate change on food security over Punjab regions, Pakistan, from 1979-2020. The study utilized the total production of wheat to measure food security. Moreover, the study utilized mean, maximum, minimum temperature, wind speed, and rainfall as a measurement of climate change. This study utilized auto-regressive distributed lag (ARDL) bound test cointegration approach to test the long-run cointegration, while ARDL model is applied to investigate the short-run relationship among modeled variables. This study adopted Augmented Dicky Fuller (ADF) and Phillips Parron (PP) test to check the stationarity of the data. Moreover, the study ensures reliability and validity of the model by utilizing Breusch-Godfrey serial correlation, Breusch-Pagan-Godfry HSK, and Ramsey RESET test. This study found that wheat cultivated area (0.46*), total irrigated area (2.67***), total un-irrigated area (1.93***), and total area sown ([1.0809**) have a positive and significant long-run impact on food production in all regions of Punjab Pakistan. However, while average, the maximum and minimum temperature is negatively and significantly associated with food production in all regions of Punjab (- 1.07**; - 3.33***; - 1.84**), except northern Punjab, where maximum temperature affects positively food production Punjab. Rainfall negatively and significantly affects food production in northern (- 0.34**) and central and southern (- 0.13***) Punjab. Furthermore, wind speed negatively affects food production in all regions of Punjab (- 0.22**; - 0.21*; - 0.11**), except northern Punjab. This study implies that government should develop policies to increase irrigation facilities and loan facilities to increase the total area sown that will help to increase wheat yield and ensure food security. Moreover, the government should devise policies for large-scale plantations to minimize climate change impacts. The study also suggests new improved varieties of the wheat crop that can survive and flourish in the presence of adverse climatic changes, high temperature, and high wind speed.

Impacts of Major Crop Production on the Agricultural Sector in Bangladesh: A Time Series Analysis

The main objective of this research article is to analyze the impact of the production of some major crops, namely rice, jute, wheat, sugarcane, and potato on the agriculture sector in Bangladesh. The agriculture sector is considered an important sector of the Bangladeshi economy, which supplies a significant proportion of the gross domestic product (GDP). Annual time-series data for the period 1988–2017 is used for the study. Several econometric techniques are employed to examine the data, including the augmented Dickey-Fuller (ADF) test, Johansen cointegration test, and ordinary least squares (OLS) method. To validate the model, some residual diagnostic tests, namely a normality test, heteroskedasticity test, serial correlation test, and stability diagnostic test are conducted. The findings of the study demonstrate that there are two cointegrating equations in the model. Furthermore, both wheat and jute production have a significant positive impact on the agriculture sector. Although the impacts of rice and potato production on the agriculture sector are positive, these are not significant at the 5 or 10 percent significance level. On the other hand, sugarcane production has a negative and significant impact on the agriculture sector, which was not expected. Based on the results, this paper recommends that the government of Bangladesh should focus on designing and implementing new funding policies and subsidies to improve the growth, production, and development of the sugarcane sector. Finally, the government should be concerned about climate change-related factors that negatively affect crop production in Bangladesh.

Comovement of Fear Index, Stock Returns, Brent Oil Prices in BRIC Countries: The Case of COVID-19

The study investigates long-run and short-run cointegrating relationship between stock market returns, fear index (VIX), brent crude oil prices and growth in deaths due to the COVID-19 pandemic for BRIC countries using daily data from 23 January 2020 to 24 August 2020 using Autoregressive Distributed Lag (ARDL) model. CUSUM test and serial correlation test estimates point towards the robustness of the model used. The evidence reveals that for India and Brazil, with the outbreak of COVID-19, decrease in crude oil prices and increase in volatility index, the stock returns started declining in the short run, but the impact has declined and the stock returns have regained in the long run. For China, due to the outbreak of COVID-19 and increase in fear index, stock returns declined in the short run, but the Chinese economy has recovered well due to a strong industrial and services sector. For Russia, increase in deaths due to COVID and decline in oil prices has impacted the stock returns in the long- and short run. Due to a decline of 53% for crude oil prices from January 2020 to May 2020, the Russian economy would face the consequences in the long run as well. The results suggest that though BRIC countries were impacted by growth in COVID-19 deaths, but the recovery trajectory and stability has resumed for all countries except Russia. Results of Granger Causality indicate a bidirectional causality between VIX and stock returns for the Indian market.

Effect of Electronic and Non-Electronic Fraud on Performance of Deposit Money Banks in Nigeria

Fraud in the Nigerian payments system and globally has been on the increase over the past few years as technological advancement impact on the way we pay. The objectives of the study are to examine the effect of electronic and non-electronic fraud on profit before tax, return on assets and return on equity of deposit money banks in Nigeria. The study obtained data from Nigeria Interbank Settlement System Plc (NIBSS) reports. Ordinary least square diagnostic test was done through serial correlation, heteroskedasticity and Ramsey RESET Test specification to ensure that variables are in line with classical linear regression assumptions. The result of the findings among others shows a negative relationship between electronic and non-electronic fraud on profit before tax of deposit money banks in Nigeria. The study recommends that financial institutions must thoroughly report all frauds recorded so as to curb the rising trend.

Deregulation of Interest Rate in Nigeria and Deposit Money Bank’s Performance (1996 – 2018)

This study looked at the relationship between interest rate deregulation and performance of Nigerian deposit money banks for the period 1996-2018. Interest rate deregulation was broken down into prime lending rate, maximum lending rate, 3-month deposit rate, and over 12-month deposit rate, with return on assets (ROA) serving as a proxy for deposit money bank performance. Dates for the variables listed above were obtained from the Central Bank of Nigeria Statistical Bulletin (2018 edition) and the World Bank data base. The data were assessed for stationarity with the Dickey-Fuller (D-F) test, long-run relationship with the Bound's co-integration test, and ARDL reliability with serial correlation, heteroscedasticity, and normality tests. The results of the tests revealed that all of the variables were integrated of order zero or one, and that the variables have a long-run relationship. As a result, the ARDL model for parameter estimation revealed that only prime lending rate was positively connected to bank ROA, whereas none of the explanatory factors were statistically significant. The researcher then claimed that there is no meaningful association between interest rate deregulation and the performance of Nigerian deposit money banks during the study period. As a result, deposit money institutions should attempt to mobilise appropriate savings from surplus units by offering deposit rates capable of enticing savers to grow their savings and increasing the availability of loanable funds.

Corporate Characteristics and Implementation of Integrated Reporting Framework of Listed Oil and Gas Firms in Nigeria

The demand for better corporate information by various stakeholders across the globe has necessitated a paradigm shift from the traditional financial reporting to more comprehensive financial and non-financial information in a single report, known as integrated reporting. The aim of this study is to empirically investigate the effect of corporate characteristics (firm size, board size, share ownership structure and profitability) on the implementation of integrated reporting framework in listed oil and gas firms in Nigeria. A census sampling technique was adopted, using the total population of eleven (11) oil and gas firms listed on the Nigerian Stock Exchange as of 31st December 2020 as the sample, since the firms are few. Data was drawn from annual reports obtained from the companies’ websites from the period of 2011 – 2020. The data was analyzed using descriptive statistics, a serial correlation test, and panel least square regression technique. The findings revealed a positive effect of corporate characteristics on integrated reporting frameworks, which is statistically significant for profitability, firm and board size. It was concluded that size of the firm, board size, and profitability have a positive and momentous statistical influence on implementation of integrated reporting framework, while share ownership structure has an insignificant influence. It was recommended that the accounting regulatory authorities consider making an integrated reporting framework mandatory, especially for listed firms in Nigeria, in line with the international integrated reporting council framework.

Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation

In this paper, we consider the daily Xtrackers CAC 40 UCITS from 2009 to 2020 for the analysis as it is supposed to capture more information compared to other French stock markets. After application of different statistical tests including BDS test, Hinich bispectrum test, Tsay test for linearity, long memory test and automatic serial correlation tests, we try to test the weak form efficiency of French ETF market through a logistic smooth transition AR model with nonlinear asymmetric logistic smooth transition GARCH errors using semiparametric maximum likelihood where the innovation distribution is replaced by a nonparametric estimate based on the kernel density function. After analyzing the forecasting results, we show that the price fluctuations appear as the result of transitory shocks and the predictions provided by the LSTAR-ANSTGARCH model are better than those of other models for some time horizons. The predictions from this model are also better than those of the random walk model; accordingly, the XCAC 40 price is not weak form of efficient market for the entire period because its successive return are nonlinearly dependent and doesn't generate randomly.

Relationship Between Money Market and Economic Growth in Nigeria

AbstractThis paper investigated the relationship between money market and economic growth in Nigeria. Secondary data for 38 years covering the period of 1981 to 2018 was used. Econometric methods of analysis of Lag Length Selection Criteria, ARDL Bound Test for Cointegration, Long Run and Short Run Estimations and Diagnostics Tests of Serial Correlation, Hetroskedascitity, Cusum Test and Ramsey Reset Test were carried out. The result found that there is long run relationship between money market and economic growth, as indicate by the F-statistics 4.48 which is higher than the upper bound values at all level of significance. This suggests that there exists a long run relation between money market and economic growth. The results of the error correction suggest the validity of long-run relationship between money market and economic growth. The study recommends that financial market regulatory body to increase the size of the money market through policy relaxation in order to achieve economic growth in the country.Keywords: Money Market, Economic Growth, Bound Test.JELClassification: C01, E05, G10