Variables Of The Independent Variable Time

We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.

We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.

Upon, identifying the aforementioned data we proceeded onto creating our chart. We then moved onto to literally type our chart title and axis titles for this particular chart. Subsequently, we advanced forward and inserted the trend line, in addition, to discovering the manner. In which, we could access and display our equation (Kleen, 2013). Therefore, the following equation was disclosed as an integral component of this process, this modus operandi was displayed as y=0.648x+111.65. Consequent to, us completing the previously mentioned steps. We proceeded to search for methods via utilization of YouTube with regards to forecasting sales for year two.

Quantitative methods: These types of forecasting methods are based on quantitative models, and are objective in nature. They rely heavily on mathematical computations.

1.) Cyclone destroyed some farms of banana which results in decrease in supply of banana in the market as 100 boxes per week due to which supply curve shows downward movement whereas demand get increases by 100 boxes per week and shows upward movement in demand curve.

Since non-stationarity can be presumed for the series examined, the option left here is to remove it by differencing the individual analyzed series. However, researches have proven that this process will result in the loss of important information on long-term relationships between the elements of time series. For the examination of unsteady relationships between series, the EG test was hence used, which can analyze the co-integration of non-stationary time series using the given hypotheses:

The demand curve is likely to change upwards or rise as a result of changes in a number of factors. One, if there is a move up in the price of an alternative commodity, or decrease in price of the given commodity’s accompaniment. Two, if there is a rise in buyers’ income. Three, if the taste as well as preferencs of the consumers shifts in regard to the particular product or service under consideration. Four, when there is a decrease in the cost of borrowing. And finally, if there is an overall increase in the buyers’ trust accompanied with optimism for the particular product or service.

• Forecasting, • Factors influencing Demand • Basic Demand Patterns • Basic Principles of Forecasting • Principles of Data Collection • Basic Forecasting Techniques, Seasonality • Sources & Types of Forecasting Errors

The vector autoregression (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic multivariate time series. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from univariate time series models and elaborate theory-based simultaneous equations models. Forecasts from VAR models are quite flexible because they can be made conditional on the potential future paths of specified variables in the model.

Study of devaluation analysis based on exchange rate systems will be done by econometric analysis. For this study time series data will be applied.

The methods of data analysis involve the use of descriptive statistics and correlation matrix test which helps in describing the nature of our data. In testing the hypothesis, the use of econometric techniques will employed as unit root test – ADF and co-integration test will be conducted. Co-integration test will be used to examine the stable long

Testing the stationary properties of time series is a very important exercise as the use of stationary time series data in the Classical Linear Regression Model will result in inflated results. The results are likely to be inconsistent and with a low Durbin Watson (DW) statistic. Several methods can be employed to test whether the time series variables are stationary , these includes residual plot but this paper will employ the Augmented Dickey Fuller (ADF) to test the existence of a unit root. Conclusion of stationarity is going to be considered at 1% and 5% level of significance only. Any probability of each variable below the two values will be considered stationary. If the model fails to meet the stationary requirement, we will use the differencing method to make our model stationary.

fundamental component in managerial decision-making. Demand forecasting is of importance because an estimate of future sales is a primer for preparing production schedule and employing productive resources. Demand analysis helps the management in identifying factors that influence the demand for the products of a firm. Thus, demand analysis and forecasting is of prime importance to business planning.

The purpose of this paper is concentrated on relationship between Vietnamese stock price relative to exchange rate and United State stock market. In order to have a better view about this relationships, the suitable econometrics model will be used in the research are OLS and ARMA. To determine the correlation, coefficients among the variables from the test we will be able to find out the β, R2, P-value, Standard Error, Durbin-Watson stat statistic etc... With the time series dataset, in other to get a good forecast, the regressions will be run and tested on EVIEW program. The main model will be use is:

After testing the stationarity of variables, we fit a VAR model. And we choosea VAR model by correlogram, information criteria (i.e. LR, FPE, SC, AIC, and HQ), and AR roots graph. The method are based on Chapter 5(pp.285-287).