Research
Econometric Analysis of Effect Of Import of Oil And Some Other Economic Indicators on Stability of Exchange Rate Using Simultaneous Equation
This paper considers the use of econometric techniques in estimating some of the economic indicators in Nigeria. A time series data was collected on yearly basis from 1974-2006, giving a total of 33years. Simultaneous equation model was used in estimating the stability of exchange rate. The various factors considered as affecting stability of exchange rate are imports of oil, gross domestic product, inflation rate and Total expenditure on Agriculture. From the result,it was gathered that Total expenditure on Agriculture is a good predictor of exchange rate, i.e. it contributes to the stability of a country’s exchange rate. Based on the findings, necessary recommendations were made for possible solution on how to enhance stability of exchange rate. Based on the findings, necessary recommendations were made for possible solution on how to enhance stability of exchange rate.
On The Comparison of Two Methods of Analyzing Panel Data Using Simulated Data
The study focus on the use of simulated panel data to compare the performance of two methods of analyzing such data. Features of the ordinary least squares model that uses pooled data and fixed effects of the least square dummy variable (LSDV) model were discussed. The samples of size 60 and 100 were generated and replicated five and ten periods respectively. Several statistics (R2, Standard error, t and f distributions) were used in comparing the result of the estimates obtained from the two methods applied to two sample sizes.The comparison of the results showed that the analysis based on the bigger sample is more consistent and efficient than the one based on the smaller sample size and this made the least square dummy variables to be more superior than the pooled data model. The conclusion is that the fixed effects model (LSDV) is superior and better in the analysis of panel data.The study focus on the use of simulated panel data to compare the performance of two methods of analyzing such data. Features of the ordinary least squares model that uses pooled data and fixed effects of the least square dummy variable (LSDV) model were discussed. The samples of size 60 and 100 were generated and replicated five and ten periods respectively. Several statistics (R2, Standard error, t and f distributions) were used in comparing the result of the estimates obtained from the two methods applied to two sample sizes.The comparison of the results showed that the analysis based on the bigger sample is more consistent and efficient than the one based on the smaller sample size and this made the least square dummy variables to be more superior than the pooled data model. The conclusion is that the fixed effects model (LSDV) is superior and better in the analysis of panel data.
