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Estimation in Elusive Populations Using Multiple Frames and Two-Phase Multiple Frames in the Presence of Measurement and Response Errors
Mutanu Beth,
Kahiri James,
Odongo Leo
Issue:
Volume 9, Issue 5, September 2020
Pages:
173-184
Received:
4 August 2020
Accepted:
17 August 2020
Published:
8 September 2020
Abstract: Accurate survey data is important for planning and decision making. The presence of measurement and response errors in surveys has been known to negatively affect the efficiency of estimates as well as to create biases in estimates. It is important to investigate the effects of measurement and response errors when computing survey data so as to obtain reliable information for use by statisticians and policy makers. Unavailability of a sampling frame in a survey for elusive populations has led to the application of multiple frames in sample selection processes. This paper investigates the effect of measurement and response errors in population estimation under multiple and two-phase multiple frames for elusive populations. The effect of random errors and biases from systematic errors on simple and correlated response variances under various levels of multiplicity adjustment factor in multiple frames is carried out. A numerical example is given assuming simple random sampling. The net effect of the errors has been found to inflate simple and correlated response variances and hence overestimation of the variances under different variance estimators. It is therefore recommended that both measurement and response errors be put into consideration when designing and carrying out a survey for more accurate results.
Abstract: Accurate survey data is important for planning and decision making. The presence of measurement and response errors in surveys has been known to negatively affect the efficiency of estimates as well as to create biases in estimates. It is important to investigate the effects of measurement and response errors when computing survey data so as to obt...
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Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices
Mostafa Ahmed Aly,
Ahmed Fathy Abd Elaal Elwaqdy
Issue:
Volume 9, Issue 5, September 2020
Pages:
185-200
Received:
8 August 2020
Accepted:
24 August 2020
Published:
14 September 2020
Abstract: This study evaluates the performance of a group of GARCH models under three different distributions in terms of their ability to estimate and forecasting the volatility of Egyptian Stock Exchange General Index (EGX30) in some horizon of forecasting using daily data for the period from January 2, 2000 to April 30, 2019, and tries to determine the best model according to some criteria. The primary purpose of the study is to investigate whether the two-regime MSW-GARCH model outperforms the uni-regime GARCH models in a very volatile time period during the global financial crisis. Hence, evaluating the predictive accuracy of the MSW-GARCH, and whether the MSW-GARCH assessed on the EGX30 would be successful. We explore and compare different possible sources of forecasts improvements: asymmetry in the conditional variance, fat-tailed distributions and regime-switching methodology. The results show that; there is an evidence that the EGX30 index has been affected by the crisis, and the TGARCH models are superior in predictive ability on EGX30 compared to the other tested models. Consequently, uni-regime GARCH models has priority in MSW-GARCH models in their forecasting performance. These models yield significantly better out-of-sample volatility forecasts.
Abstract: This study evaluates the performance of a group of GARCH models under three different distributions in terms of their ability to estimate and forecasting the volatility of Egyptian Stock Exchange General Index (EGX30) in some horizon of forecasting using daily data for the period from January 2, 2000 to April 30, 2019, and tries to determine the be...
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Application of Principal Component Analysis on Perceived Barriers to Youth Entrepreneurship
Alice Constance Mensah,
Joseph Dadzie
Issue:
Volume 9, Issue 5, September 2020
Pages:
201-209
Received:
20 August 2020
Accepted:
5 September 2020
Published:
21 September 2020
Abstract: Entrepreneurship is an imperative driving force for innovation in a country. Nevertheless, there is lack of systematic investigation in the area of barriers to entrepreneurship and its effects on the intentions of the youth becoming an entrepreneur. As a result, the primary objective of the study is to analyze perceived barriers to youth entrepreneurship. The study used responses from 186 students of a tertiary institution, who were selected based on convenience sampling method. A 5 point likert scale was used to measure the responses and the data analyzed with descriptive statistics, correlation and principal component analysis. The results indicate that youth perceive lack of capital, lack of skill, lack of support, lack of market opportunities and risk as the main barriers to youth entrepreneurship. Nine (9) factors with Eigenvalues greater than one accounted for 73.35% of the variance explained. The study recommends that, stakeholders precautiously design courses and policies to minimize the perception of entrepreneurship barriers and maximize motivational factors. Entrepreneurship education be designed to enhance skills and knowledge in entrepreneurship and also to reorient students’ career choices towards entrepreneurship. Awareness campaign of government support instruments should be done. Policy makers should implement sound economic policies to boost the country’s economic environment.
Abstract: Entrepreneurship is an imperative driving force for innovation in a country. Nevertheless, there is lack of systematic investigation in the area of barriers to entrepreneurship and its effects on the intentions of the youth becoming an entrepreneur. As a result, the primary objective of the study is to analyze perceived barriers to youth entreprene...
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The Impact of Electronic Clearing on Currency in Circulation in Mozambique
Issue:
Volume 9, Issue 5, September 2020
Pages:
210-218
Received:
21 August 2020
Accepted:
8 September 2020
Published:
21 September 2020
Abstract: Similar to the experience of other countries, payment habits in Mozambique have been changing. Indeed, innovations in transaction technology such as electronic payments have revolutionized the payment landscape in the economies. Although, in general, cash has remained the dominant payment instrument, it is important to analyze the extent to which its course has been influenced by several alternative payment instruments used in the intermediation of commercial transactions. Among the alternative payment instruments, this paper examines the impact of some of the components of interbank electronic clearing subsystem, namely interbank cheques clearing and interbank electronic funds transfer, on currency in circulation in Mozambique, using monthly data between 2010 and 2019. Preliminary analysis of the data revealed a decreasing trend in interbank cheques clearing against an increase in interbank electronic funds transfer, which is in line with the trends registered in some countries referenced in this paper. A Vector Error Correction Model was used based on the cointegration behavior of the variables used. The empirical results suggested that, interbank cheques clearing substitute currency while interbank electronic funds transfer complement it. Thus, this allows us to conclude that, generally, the beneficiaries of interbank electronic funds transfer convert funds ordered into cash, whereas the beneficiaries of cheques transacted via interbank clearing prefer to keep funds into deposits. However, the statistical significance of the latter was tenuous.
Abstract: Similar to the experience of other countries, payment habits in Mozambique have been changing. Indeed, innovations in transaction technology such as electronic payments have revolutionized the payment landscape in the economies. Although, in general, cash has remained the dominant payment instrument, it is important to analyze the extent to which i...
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Comparative Study of GCV-MCP Hybrid Smoothing Methods for Predicting Time Series Observations
Samuel Olorunfemi Adams,
Yahaya Haruna Umar
Issue:
Volume 9, Issue 5, September 2020
Pages:
219-227
Received:
15 June 2020
Accepted:
7 July 2020
Published:
12 October 2020
Abstract: Generalized Cross Validation (GCV) has been considered a popular model for choosing the complexity of statistical models, it is also well known for its optimal properties. Mallow’s CP criterion (MCP) has been considered a powerful tool which is used to select smoothing parameters for spline estimates with non-Gaussian data. Most of the past works applied Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP) smoothing methods to time series data, this methods over fits data in the presence of Autocorrelation error. A new Smoothing method is proposed by taking the hybrid of Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP). The predicting performance of the Hybrid GCV-MCP is compared with Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP) using data generated through a simulation study and real-life data on all SITC export and import price index in Nigeria between the years, 2001-2018, performed by using a program written in R and based on the predictive Mean Score Error (PMSE) criterion. Experimental results obtained show that the predictive mean square error (PMSE) of the three smoothing methods decreases as the sample size and smoothing parameters increases. The study discovered that the Hybrid GCV-MCP smoothing methods performed better than the classical GVV and MCP for both the simulated and real life data.
Abstract: Generalized Cross Validation (GCV) has been considered a popular model for choosing the complexity of statistical models, it is also well known for its optimal properties. Mallow’s CP criterion (MCP) has been considered a powerful tool which is used to select smoothing parameters for spline estimates with non-Gaussian data. Most of the past works a...
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Strategies of Households Resilience in Adapting to Challenges in Turkana County
Loice Yoda,
Karanja Anthony,
Pius Kihara
Issue:
Volume 9, Issue 5, September 2020
Pages:
228-237
Received:
28 August 2020
Accepted:
22 September 2020
Published:
12 October 2020
Abstract: Turkana County experiences re-occurring drought and conflict leading to an increased dependency ratio, injuries, both physical and emotional as well as displacement. This study, using Resilience Index Measurement, Analysis is to determine which factors have the capability to maximize resilience in livelihoods by minimizing the effect of the shock by looking at different ways of how livelihood contributes to household’s coping strategies and capacity during the calamity. Data used in this study was obtained through quantitative method where a sample (n≥384) was drawn from the target population by random sampling from the data collected between 2015 and 2016. Factor loading analysis was done to establish the weights of each resilience component. RIMA model has shown the ability to be an appropriate tool that can deal with both linear and nonlinear regression concepts. The overall Resilience Index of Turkana county was 0.0457 and that gender to some extent is contributing factor in determining the resilience index. The household head for Pastoral category were between 24-41 years, which is young with 28 years as the average age. Access to market facility determines the kind of what livelihood activity individual engages in at 79%. Access to credit significantly affects Resilience of an individual (p < 0.1) thus contributing to diversity in choosing livelihood negatively. Remittances have a negative effect on the fishery and farming livelihoods by 7%.
Abstract: Turkana County experiences re-occurring drought and conflict leading to an increased dependency ratio, injuries, both physical and emotional as well as displacement. This study, using Resilience Index Measurement, Analysis is to determine which factors have the capability to maximize resilience in livelihoods by minimizing the effect of the shock b...
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Arima Forecasting Model for Uganda’s Consumer Price Index
Issue:
Volume 9, Issue 5, September 2020
Pages:
238-244
Received:
10 September 2020
Accepted:
23 September 2020
Published:
12 October 2020
Abstract: In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country. The monthly CPIs serve mostly those users who require past and current inflation rates. The main objective of this study is to identify and estimate an ARIMA model for the CPI and use it to make short term forecasts. We relied upon monthly Consumer Price Indices from January 2010 to July 2020 obtained from Uganda Bureau of Statistics. The time series was transformed so as to make it stationary, before identification and estimation of ARIMA (p, d, q) x (P, D, Q)12 models. An ARIMA (1, 1, 1) (0, 1, 1)12 with no constant was selected as the best model, because it had the least AIC and BIC. Additionally, all the coefficients of the ARs and MAs were significant at 1% level. Using the selected model, inflation forecasts were generated for 12 months (August 2020 to July 2021) and found to fluctuate between 4.7 and 6 percent. We recommend this model to Uganda Bureau of Statistics and Central Bank to use it to make forecasts and disseminate them to users. In conclusion, generally good forecasts are vital for better resource allocation, planning and decision making.
Abstract: In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country...
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Modeling Dependence Relationships of Anthropometric Variables Using Copula Approach
Funmilayo Westnand Oshogboye Saporu,
Isaac Esbond Gongsin
Issue:
Volume 9, Issue 5, September 2020
Pages:
245-255
Received:
3 September 2020
Accepted:
21 September 2020
Published:
22 October 2020
Abstract: Copula model is introduced in modeling the co-dependence structures of anthropometric variables-Body mass index (BMI), Abdominal circumference, Adiposity and Percent body fat-because it can capture monotonic dependence. Four copula-based Kumaraswamy-epsilon distributions are derived and used to determine the best fit to the anthropometric data, these are new. These are the Gaussian, Clayton, Frank and Gumbel copulas. Clayton model provided the best fit in four bivariate pairs-BMI and Percent body fat, BMI and Abdominal circumference, Adiposity and Abdominal circumference and Abdominal circumference and Percent body fat-while Gaussian is best for BMI and Adiposity pair and Frank is best for Adiposity and Percent body fat pair. Copula-based Kendall’s tau and tail dependence are used as estimates for measuring the strength of the co-dependence. The results strongly recommend the use of BMI as an anthropometric index for estimating human body composition of adiposity. However for individuals with BMI values in the two extreme tails, their adiposity should be measured directly. The results do not find any suitable anthropometric indices for estimating percent body fat and therefore is recommended that for such epidemiological research, percent body fat should be measured directly. The results also clearly show that the Kendall’s tau and the corresponding Pearson correlation coefficient estimates are largely at variance whenever the co-dependence structure cannot be described as linear dependence. This can prompt contradictory conclusions. It is therefore suggested that for such research, whenever Pearson correlation coefficient method is in use, a coefficient of determination of a minimum of 75% should be obtained before any anthropometric index can be recommended for body composition substitution.
Abstract: Copula model is introduced in modeling the co-dependence structures of anthropometric variables-Body mass index (BMI), Abdominal circumference, Adiposity and Percent body fat-because it can capture monotonic dependence. Four copula-based Kumaraswamy-epsilon distributions are derived and used to determine the best fit to the anthropometric data, the...
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Modeling Burglar Incidents Data Using Generalized and Quasi Poisson Regression Models: A Case Study of Nairobi City County, Kenya
Isaac Muchika,
Antony Ngunyi,
Thomas Mageto
Issue:
Volume 9, Issue 5, September 2020
Pages:
256-262
Received:
5 October 2020
Accepted:
19 October 2020
Published:
26 October 2020
Abstract: Serious violent crimes including Burglary, dangerous drug trafficking and sexual offenses make up the bulk of incidents filed at police stations daily. These crimes related activities poses a serious threat to the peace and serenity of a nation as far as safety is concerned. Burglar incidents data are often discrete and do not conform to the general assumptions of the linear model and its variants. Ordinarily, such data could be modeled using a linear regression approach to derive the relationship between the response variable to the underlying covariates. However, the narrowing of the gap between city and suburban burglar crime rates brings about variability invalidating the application of Ordinary linear regression approaches. The main objective of this study focused on the comparative use of Generalized Poisson and Quasi-Poisson models as an alternative to the classical linear regression approach in modeling Burglar incidents in Nairobi City County, Kenya. The prime advantage of applying Quasi Poisson in count data analysis is that it fixes the basic fallacy of assuming homogeneity in data and allows estimation of dispersion. The study used secondary data covering Eight (8) Nairobi's Administrative Divisions from the National Crime Research Center (NCRC) for the period 2016-2018. The comparison criteria were the Akaike Information (AIC) Criterion and Deviance Information Criterion (DIC) alongside other model diagnostics tests. Application of this results in burglar events revealed that the number of incidents in the study area are Under-dispersed with the risks of experiencing Burglar crime being above 5% in all the locations surveyed. In an attempt to explore Burglar to location relationship, results from study proved that Generalized Poisson Model performed better than the Quasi Poisson model having posted the lowest AIC value.
Abstract: Serious violent crimes including Burglary, dangerous drug trafficking and sexual offenses make up the bulk of incidents filed at police stations daily. These crimes related activities poses a serious threat to the peace and serenity of a nation as far as safety is concerned. Burglar incidents data are often discrete and do not conform to the genera...
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