Application of Least Square Dummy Variable (LSDV) in Estimation of Compensation of Employee (Published)
This research was conducted to estimate compensation of employee using least square dummy variable (LSDV) regression model. The data used in this work were secondary data sourced from National Bureau of Statistics (NBS) from 1981 to 2006. The variables considered were compensation of employee as the dependent variable, fixed capital, price of goods, tax and surplus as the independent variables. The data were analyzed using (STATA 13). The results obtained revealed that F-value of 3874.05 was statistically high suggesting the overall model was good fitted. The R2 -value 0.9989 was also high which indicated that 99.89% of the total variation was accounted for by the independent variables included in model while the remaining 0.11% unexplained was accounted for by the white noise. Again, all the differential intercept coefficients have negative signs. Also, several differential slope coefficients have negative signs which implied that they were negatively related to compensation. Again, the result revealed that compensation is not statistically significantly related to fixed capital, price, tax and surplus. However, none of the differential slope coefficients is statistically significant. Of all the three differential intercept coefficients only was statistically significant. Since none of the differential slope coefficients was statistically significant, it concluded that the differential slope coefficients are not different from the slope coefficient of the base/comparison group (power sector.
The crucial difficult in estimating covariates effects in panel analysis, is when there is correlation of the unobserved heterogeneity with the covariates and the fact that estimation of conditional mean effects seems potentially limited. Much consideration has not really been given to curb this difficulty especially in the context of quantile regression. In this work Panel Quantile regression was applied in other to investigate the correlated random effects (i.e. effects of the correlation between the covariates and the unobserved heterogeneity) and the penalized fixed effect (i.e. effects after eliminating the unobserved heterogeneity). We employed the use of real data and simulated data sets at different sample sizes. The results showed significant correlated random effect for both covariates in the real data only at the low level (0.25 quantile), but when the unobserved heterogeneity was eliminated both variables were seen to significantly affect the response at the 0.25, 0.5 and 0.75 quantiles of its distribution. The simulation study also confirmed it. We also noticed that as the sample size increases in the simulation study the correlated random effects become insignificant, while the penalized fixed effect and quantile regression effects were evidently significant at all quantiles considered. Comparison of these methods showed that the penalized fixed effect had the least value for both MSE and RMSE. This analysis was done in R environment using the quantreg package.
Evaluation of Some Estimators Performance on Linear Models with Heteroscedasticity and Serial Autocorrelation (Published)
In many, if not most, econometric applications, economic data arises from time-series or cross-sectional studies which typically exhibit some form of autocorrelation and/or heteroskedasticity. If the covariance structure were known, it could be taken into account in a (parametric) model, but more often than not the form of autocorrelation and heteroskedasticity is unknown. In such cases, model parameters can typically still be estimated consistently using the usual estimating functions, but for valid inference in such models a consistent covariance matrix estimate is essential. In this study, the strength of some methods of estimating classical linear regression model with both negative and positive autocorrelation in the presence of heteroscedasticity were investigated. The Ordinary Least Square (OLS) estimator, Heteroskedasticity and Autocorrelation (HAC) estimators which includes Cluster-Robust Standard Errors estimators, Newey-West standard errors and Feasible Generalized Least Squares Estimator (FGLS) were considered in this study. Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. The result revealed the superiority of the Newey-West standard errors over others using root mean squared error (RMSE) of the parameter estimates and relative efficiency (RR) as assessment criteria among others over various considerations for the distribution of the serial correlation and heteroskedasticity.
The Influence of Macroeconomic Factors on Indonesian Banking Performance (In Buku 3 and Buku 4 of 2012-2017 Period) (Published)
Banks should have adequate capacity, especially in holding capital, to be able to manage risks. In its development, the requirements of capital’s components and instruments as well as the calculation of bank capital adequacy need to be adjusted to the international standard. Strong capital will make banks healthier and more competitive in the face of the competition from major banks in the ASEAN region and globally. Therefore, the Financial Services Authority (OJK) issued a number of regulations so that the national banking industry is stronger and more trustworthy by the public. The rule is the OJK Regulation (POJK) Number 6/POJK.03/2016 concerning Business Activities and Office Networks Based on the Core Capital. In 2012, the dynamics of the global economy set a negative trend and began to have an impact on the Asian economies, such as Indonesia. In this study, the focus was on the improvement in banking risk indicators that occurred to evaluate the performance of banks by using CAR, ROA, NIM, LDR and NPL variables, and analyze macroeconomic factors such as inflation, interest rates, exchange rates, and GDP. This study aims to describe the performance of banking in Bank Umum Kegiatan Usaha 3 and 4 (BUKU 3 and BUKU 4) consist of NIM, NPL, CAR, ROA, and LDR and analyze the response of macroeconomic variables to banking performance in banks in BUKU 3 and BUKU 4. The method used was the VECM estimation model that was then analyzed with the Impulse Response Function (IRF) and Forecast Error Decomposition of Variance (FEVD). The results of the research on banking performance, if it was grouped based on bank business activities in BUKU 3 and BUKU 4, showed that overall bank performance in BUKU 4 was better than which was in BUKU 3. The result of bank IRF and FEVD in BUKU 3 was that the macroeconomic variable that provide the greatest response and contribution was interest rates. While the result of bank IRF and FEVD in BUKU 4 was that the macroeconomic variable that gave the biggest response and contribution was inflation.
Using annual data from 1980-2014, this paper employs a random effect model to estimate the effect of regional integration on private investment in East African Community (EAC). Levin-Lin-Chu Test (LLC) and Pedroni Cointegration Test were used to investigate the properties of data with respect to unit root and cointegration respectively while the Hausman Test was used to select the random model. The error correction model was used to capture the short-run dynamics in the model. The findings suggest that regional integration (proxied by intra-EAC openness), has a positive significant effect on private investment in the EAC. Hence the respective EAC governments should sustain policies that promote free trade so as to boost private investment in the region through the removal of tariffs which leads to efficiency in production and hence economies of scale.
The credit spreads are the interpretation of the bond returns received by investors as measured by the difference between the corporate bonds yield rate and government bonds. The purpose of this study is to analyze the impact of changes in macroeconomic variables. Such as volatility of stock returns, default probability and inflation on banking sub-sector credit spread bonds. This study analyzes the change of credit spreads bonds based on the category of the grades, the investment grade and non-investment grade. The data were analyzed using panel data which consist of several companies with investment grade and non-investment grade categories during 2014 – 2016. The result showed that the relationship of default probability and inflation variables had significant effect in the credit spreads of investment grade bonds, while the variable volatility of stock return had no significant effect. While significant effect was found inthe non-investment grade bonds, the variable volatility of stock returns, default probability and inflation.
AN EMPIRICAL DETERMINATION OF FOREIGN DIRECT INVESTMENT IN WEST AFRICA COUNTRIES: A PANEL DATA ANALYSIS (Published)
Most countries in Africa have undertaken significant steps to attract FDI by adopted FDI-specific regulatory frameworks to support their investment related objectives. Thus, this study investigates the determinants of FDI in sixteen countries in West African by empirically examining the influence of growth rate of GDP in all the sixteen countries; GDP per capita; government policy in attracting foreign investors; infrastructural development; openness of the economy to trade; inflation rate; natural resources, official exchange rate and labour availability. Panel data were used because of its advantage over OLS and because it is better use in cross-country regressions. An important implication of the empirical result is that FDI in West Africa is mainly affected by natural resources and labour availability, GDP per capita which is used as a proxy for capital-labour endowment, Market size of the countries proxy by GDP growth rate and official exchange rate. The rule of thumb regarding the issue of FDI in West Africa sub-region suggests that the sub-region can be the top receipt in Africa in the next decade if other countries discover resources available in their countries