Table Of Contents
• Data Research Writing Service involving Literature Review
• Data Research Homework Help on the Methodology
• A thorough Conclusion by Online Data Research Tutors

## Data Research Writing Service involving Literature Review

Studies have been carried out on the effect of oil prices. One such study is by Bahmani-Oskooee (1988), who studied the effect of oil price shock on the stability of demand for external reserves.  In this data research writing service, the main research question was if a sharp and sudden increase in oil price could lead to a structural shift in the reserve demand function. The data for the study is time-series data for 19 developed and 17 less developed countries for the period of 1973-1985. He used GLS estimation techniques to estimate a pooled regression model, and they used Quandt's log-likelihood ratio test to determine if there is a structural change in demand pre and post-crisis. His result shows that the reserve demand function experienced structural shifts as a result of oil price shock. Another study was carried out in Nigeria, a less developed, African oil-exporting country, using monthly data from 1995 to 2013 (Imarhiagbe 2015). The question the research is interested in answering is if there is any effect of crude oil prices on Nigeria's external reserve. He used the volatility model GARCH-M to estimate the model and found that the price of oil has a positive effect on the variability of external reserves. Bahmani-Oskooee and Brown (2004) used the Kalman filter approach to approximate international reserves demand. They observed that because of the variations in the exchange rates and fluctuations of oil price, the approximated elasticity's are dependent on time. Using quarterly data from1959 to 1994 for 19 industrialized countries and a rolling window regression method to examine time-varying properties of elasticity's, he found that the elasticity of reserve demand has been unstable over time.

## Data Research Homework Help on the Methodology

The data consists of time series data from 1995 to 2017 for ten countries. The countries are China, UAE, Norway, Singapore, Saudi Arabia, South Africa, Australia, France, Canada, and Nigeria. Five of the countries are net oil exporter, while five are not oil exporter. The data set include data on External Reserves, GDP at a constant price, Import, Current account sourced from World Bank (data.worldbank.org) while the oil price is proxied by the Western Intermediate Select (WTI) petroleum price sourced from the LCDMA.

### Model for Data Research

Previous studies have modeled the demand for foreign reserves as follows

Where R -reserves specifics country holds at t; M-real imports of a country; VR-a measure of the variance of the bop m is the mean propensity to import. Normally id country has higher imports; it's expected that it required more reserves. if this holds is should be positive. Thus, a  higher the VR value, implies that more reserves will be needed to yield a  strong positive estimation of. The idea of including(m) is as a result of the multiplier of the Keynesian foreign trade. A higher value of m  leads to a smaller multiplier.  This then implies that there will be a reduced demand for the reserves. (Bahmani-Oskooee and Brown 2014).   However, in line with the purpose of this study and given that the data is a panel,  the data research homework helper modified this reserve demand model by including oil price, an indicator of whether a country is a net exporter of oil or not, and the interaction of both variables. The model is re-specified as below.

Where OP represents oil price, oil is an indicator variable which equals one suppose that a country is an oil exporter and 0 of a  country isn't an oil exporter. VR is measured as a change in the current account balance, and a constant is added to all observations in order for the logarithm to be possible in case of negative values.   Results Table 1 presents the summary statistics of external reserves, import oil price, and current account balance. The average reserves for all the countries are $3.38e+11, with a variation of$4.36e+11. Within country variation in reserves is $3.29e+11 while between-country variation is$3.01e+11. Average import is 3.02e+11 and the overall variation is $3.95e+11. Variation between countries is 2.98e+11 while variation within country is 2.72e+11. The average oil price over the year is$50.48, and the variation is 27.57. Between variation is 0 because the price of crude oil is the same for all countries, and the within variation is $27.57. Average change in current account balance is$9.21e+08 and the overall variation is $3.02e+10. The between-country change in the current account balance is 3.16e+09 while the within-country variation is$3.00e+10 Table 1: Summary Statistics

Detailed Regression Result for Data Research Homework Help

A detailed interpretation is provided for the data research homework help. The first decision to make in a static panel data analysis is to decide between pooled regression and random effect regression. Pooled regression treats all units as the same and does not cater to unit-specific effect. The fixed-effect model considers the unit-specific effect but assumes the effect is fixed. The random effect model treats this effect as random. Therefore to decide between these models, the first step is to estimate the fixed-effect model and test for the presence of a unit-specific effect. The result shows that F(7, 184)=19.38, p<0.001, which means rejection of the null hypothesis that the individual-specific effect is 0. This means we cannot use a pooled regression model. The next step after confirming the fixed effect is to determine if they are random. The Breusch-Pagan test LM was used for the result in table 2 shows that chi2(1)=275.91, p<0.001. This means that we do not go with the null hypothesis, which states that random effect is not present. Now that we have confirmed the presence of a fixed and random effect, we turn to the Hausman test to decide between the two models. The Hausman test result presented in Table 2 shows that chi-square(5) =4.05, p=0.5428. This means that we have to accept the null hypothesis that states that effect is not correlated with other regressors, which connotes that the fixed effect model is better a model. Table 2: Model Specification Test

The regression result is presented in table 3. From the result, the coefficient of the dummy variable is not significant (p=0.065). This connotes that demand for foreign reserves does not depend on whether a country is a net exporter of oil or not. We also observe that oil prices have a significant positive effect on reserves. A 1% increase in oil price increases reserves by 0.074% (p=0.019). This is in line with the findings of Imarhiagbe (2015), who also found a positive effect of oil price on external reserves. Moreover, the coefficient of the interaction is significant (p=0.041) and is positive. This connotes that the effect of oil price on the external reserve is greater for countries that are net oil exporter than countries that are not net oil exporter. Specifically, a 1% increase in oil price increase reserves by 0.14% but 0.074% for non-net oil-exporting countries. The sign of coefficients of M, VR, and m are consistent with what has been proposed in the literature (Bahmani-Oskooee and Brown 2014). A 1% increase in import increases reserves by 0.95% (p<0.001), while a 1% increase in the balance of payment variation increases reserve by 1.95%, and a 1% increase in average propensity to consume reduces reserve by 0.12%. Table 3: Regression result using a fixed-effect model

## A thorough Conclusion by Online Data Research Tutors

This study examined the effect of oil price on external reserves and whether the effect depends on the country being a net oil exporter or not. Using a panel data of 10 countries between 1995 and 2017, the online data research tutor has shown that the effect of oil price on reserves largely depends on if a country an exporter of oil or not, and oil price have more effect on reserve for net oil exporter than non-net oil exporter. The parameter estimate of the traditional variables that have been included in the reserve demand function was also found to be consistent with theory. This work is robust because individual country heterogeneity was considered in the model to cater for the bias that would have arisen from pooling together less developed and developed countries.