Time series

is significant. For d2, t-statistic= 1.8774, t-statistic < t-critical. Thus we do not reject Ho and d2 is not significant. For d3, t-statistic= 8.8773, t-statistic > t-critical. Thus we reject Ho and d3 is significant. This shows that there is a time trend and seasonality in the quantity demanded of PVB. Therefore, the equation for PVB forecast is the following: Q= 32561.2+ 1977.6t - 13193.1D1 +9631.5D2 + 45122.1D3 Forecasting for Fire Valves: The past demand pattern of Fire Valves, different

forecasting systems of interrelated time series and for analyzing the dynamic impact of random disturbances on the system of variables. The VAR approach sidesteps the need for structural modeling by treating every endogenous variable in the system as a function of the lagged values of all of the endogenous variables in the system. 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

do after being influenced by the best TV series. Thesis: Criminal Minds is the most influential TV series of all time. Audience Connection: It should be the next TV series you watch on Netflix. Preview: Today I will convince you why Saturday Night Live influential but telling you what the show is all about, awards it has received, and the effects it has had on society. Body I. So what exactly is Criminal Minds? A. For one, it is a popular TV series that many people view. 1. According to

1) The repeated observations of demand for a product or service in their order of occurrence form a pattern known as a time series. Answer: TRUE Reference: Demand Patterns Difficulty: Easy Keywords: time series, repeated observations 2) One of the basic time series patterns is random. Answer: TRUE Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, random 3) Random variation is an aspect of demand that increases the accuracy of the forecast. Answer:

financial time series is stationary. 4.3.2 Unit Root Test The Unit Root Test, also named Augmented Dickey-Fuller Test (ADF Test), is put forward according to whether the macroeconomic datum or financial datum has some special characteristics, which is a particular method to test stationarity of the financial time series (Choi, 2015). To put it simply, testing the unit root is to examine whether there will be a unit root in the analysis of time series or not. The financial time series would be considered

Hausman test Hausman test which usually accepted method of selecting between random and fixed effects which is running on regression equation. Hausman (1978) provided a tectonic change in interpretation related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric

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: VNSP= β_0 + β_1S&P500 + β_2VNER + ε (e1) By using OLS model we can determine how much the dependent variable is

Description Series=claim inflation Sample 1984-2014 Observations=30 Mean=2.748 Median=2.415 Minimum=1.25 Maximum=7.15 Standard deviation=1.43012 Kurtosis=1.679 Skewness=1.354 4.2.1 Descriptive Statistics for the claim inflation series The data is not stationary since it does not exhibit a certain state of statistical equilibrium showing that the variance changes with time. Performing a log transformation still produces a non-stationary process in which case we should difference the series before

CHAPTER THREE RESEARCH METHODOLOGY 3.1 Nature and Source of Data The present study is associated with the utilization of secondary data on Money Supply and Price Level for the economy of Nepal. The data of concerned variables are taken from various issues of Economic Bulletin of Nepal Rastra Bank. Quarterly data on money supply and price level ranging from 1976Q1 to 2012Q2, a total of 143 periods have been used in the present study. The present study has employed the data sets of money supply and

that the selected variables must be nonstationary (i.e., I(1) series). The presence of a unit root in the variables is thus tested using the Dickey Fuller generalized least squares (DF-GLS) test (Elliott et al., 1996). Panel A of Table 1 reports the results of the DF-GLS test. Since the null hypothesis of a unit root cannot (can) be rejected for any of the levels (first differences) of the three variables at the 5% level, all the series are found to be nonstationary I(1) processes. It should be emphasized