Time series analysis

The existing literature proposes various methodologies and procedures to predict GHG emissions in the transport sector. In general, these studies utilize time series analysis, regression analysis, decomposition, and optimization models, as explained below: • Time series analysis (Sultan 2010) introduces the use of co-combination of pay per capita and fuel price (FP) to measure transport fuel consumption (FC), while (Bekhet, H & Yasmin 2013), (Bekhet, HA & Yusop 2009), (Ang 2008), (Ediger & Akar

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

chart utilizing alpha 0.15 to forecast and analysis the data. To the right of that is similar information but the alpha utilized in this scenario is 0.90. The analysis with the lowest MAPE will help us determine which forecasting equation achieves the best outcome for our analysis. Looking at the diagram it appears that utilizing 0.15 is a better forecasting method than utilizing 0.9. As you can tell, mean error (ME) were almost four times higher using the alpha 0.9. The average sales

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

Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself,

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:

results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis are fairly good which

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