The Test Of Multicollinearity Test And Then Generating The First Difference Variables

The statistical significance of a coefficient tests determines coefficients potential of being zero. The zero potential increases when there is significant variance in the independent variables. A large variance also suggests that the variable used have no effect on the dependent variable.

As economic growth increases moderately in 2014, the rate of inflation is expected to remain below 2 percent. The price of goods in the country will continue to be restricted by global competition and use of production capacities that are relatively low as compared to historical averages. In addition, the inflation rate will remain below 2 percent because of decrease in energy prices and small increase in the prices of food. Nonetheless, while energy prices continue to reduce this year, the percentage of the decrease will be less while food prices may regain normal rate of growth.

This support that Multiple Linear Regression model is better than the Simple Linear Regression, because it show the relationship with the variables more accurately and you can know which one to discard, and which one to

The regression analysis was initially run using all variables to determine the significance of each when associated

Leading indicators often exhibit measurable economic changes before the economy as a whole does. One theory suggest commodity prices respond quickly to general economic shocks such as an increase in demand. The second is that changes in prices reflect systemic shocks such as a hurricane which decimates the supply of certain agricultural and is subsequently raises prices of supply costs. By the time it reaches consumers, overall prices would have increased and inflation would be realized. The strongest case for commodity prices as a leading indicator of expected inflation is that commodities respond quickly to widespread economic

There are an unlimited number of components that influence silver prices today. The price of silver changes every few seconds and many factors play a role in silver prices. Some of the causes for fluxes in silver prices include:curb speculation, market speculation,currency fluctuations, supply and demand, and buying power. The price of silver is always moving. This is partly because large entities and governments typically have substantial buying power and can impact silver prices through supply and demand. If a government makes a large silver purchase, the demand for that product could affect silver markets immediately. Supply and demand determines prices for commodities, and silver is no exception. Silver prices have increased over the last

Inflation has been below the Federal Reserve’s target for more than 3 and a half years. Inflation is expected to keep declining under desired target as long as the oil prices are declining as well. “Yield movements in the Treasury inflation-protected securities, or TIPS, market indicate that compensation for inflation expected in five to 10 years has dropped to 1.56% annually, according to Barclays. That is down from 1.67% when the Fed raised short-term rates in December. Moreover, it is down from 2.5% two years ago” (Leubsdorf). If less inflations is expected in the future, it could change the way people are spending their money, but if they assume that the inflation is going to keep increasing, the prices are more likely to keep rising at a faster pace.

Considering the following regression model: BRi=β0++β1(Y)+β2(Z)+ui which connects the bank rate (BR) of Canada to foreign exchange rates(Y) and CPI(Z). In this model X1 and X2 are the corresponding independent variables exchange rates and CPI measured in decimals. There were three estimation methods that were used to estimate the model: The Durbin Watson test is used to test the presence of autocorrelation. The residual values from the regression analysis helps determine if there is a relationship between values that are lagged. The result of the Durbin Watson test lies between 0 and 4 and depending on the value it will show the presence or absence of autocorrelation. The value that is closer to 0 indicates that there is positive autocorrelation, 2 indicates that there is no autocorrelation and values approaching 4 indicate that there is negative autocorrelation. For the hypothesis testing I’ve used the F-Statistic testing, in the later section of the paper I will explain my findings and the results.

The below table presents the mean and standard deviation of the absolute price, observed price, and the mean and standard deviation of changes in price for the price risk variables analyzed in Cases 1 and 2. These statistics were calculated after daily and weekly observations were aggregated into monthly average data set. The below table also defines the time period over which prices were observed and

One could also look at situations where only one variable is changes (e.g. child first vs. child second or child second vs. adult second) but these comparisons are not represented in this paper.

The concept of the coefficient of determination, R-square, is the same in both the simple regression model and multiple regression model. It is the percentage of variations of the dependent variable explained by the changes in the set of independent variables (Lind, Marchal, & Wathen, 2015). The R-square can also be obtained which tells how good the overall fit of

The prediction interval is to forecast the MBA GPA of a 40-year-old student who studies six hours per week, works full time, and has a BS GPA of 3.0. With changing the variables from the initial regression model with a 95% confidence level which forecast the MBA GPA of 2.96 makes the forecast irrelevant. Presumably, after removing the gender variable from the calculations, I moved the other two columns of data over to columns three and four respectively. Therefore the p-value for the BS GPA remained 0.0000 which is less than the significance level of 0.05 determining this variable to remain as significant. The next variable was hours the student studies per week, and the p-value changed to 0.0018, which resulted in this variable is a significant variable as well. As a result, the multiple regression model is MBA GPA = 0.38381 + 0.77785 (BS GPA) + 0.0444 (Hours Studying) + 0.012 (Works Full-time) + -0.0004 (Age). The BS GPA is the student’s undergraduate grade point average, hours studying is the average hours the student spent studying each week, works full time is if the student worked full time or not, and the age is the age of the MBA student. Therefore, we can conclude with 95% confidence level that a 40-year-old graduate student who had an undergraduate grade point average of 3.0, spends six hours studying each week, and works full time will have an MBA GPA of 2.978. Given individual differences

The research data was analyzed using a statistical software called STATA. Before running the data for the regression analysis, panel unit-root test was performed to see if data is stationary over time so that it can be used in estimating the variables in question. The value of the variables in the model throughout the research period is not constant; that means, there are some periods where there are spikes. These periods of ups and downs in value of economic variables are called shock in economic jargon. The notion behind testing for stationarity is to identify whether the effect of such shocks is permanent or transitory. If the effect of such shocks is temporary, the subsequent values of the variables will return to their long term equilibrium suggesting that the data is stable even with the presence of shocks.

The two stage regressions are reported in Table 8. Column 1 in Panel A shows that institutional trading frequency is decreasing in the stamp tax, which is consistent with the fact that raised stamp tax produces higher trading cost. Kleibergen-Paap rk Wald F statistic is a standard test for the weak instrument problem, which is ruled out since the p-value is 0.000. Columns 1 and 2 in Panel B suggest that the results from baseline regressions hold in IV regressions, where more frequent trading generates lower price informativeness. Difference-in-Sargan statistics show that the 2SLS and OLS estimates are the same. (p-value ranges from 0.35 to 0.55)

The one-step approximation the estimates, generalized cook distance and likelihood distance are considered. Cook and likelihood distance are two statistical diagnostics method. Models diagnostics procedures imply both graphical and formal statistical tests. These procedures recognize us to inspect whether the supposition of the regression model are justifiable and decide whether we can belief subsequent inference outcome. We use these methods to explore the performance of residual and influence in nonlinear regression model. These residuals can produce confusing results when used in diagnostic method corresponding to those for linear regression. Simulation provide to