Time series analysis

The stationary property of time series is tested by using Phillips-Perron (PP) unit root tests as PP-test has greater power than the Augmented Dickey and Fuller (ADF) test (Banerjee et al 1993). Another advantage of the PP tests over the ADF test is that the PP tests are robust to general

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

Kitov & Kitov (2011) provided an empirical model to check the impact of inflation and unemployment reactions to changes in the labor force in Switzerland using data from 1965 to 2010. Their overall, findings established that there exist long term equilibrium relations between the rate of labor force change and inflation rate. AMINU (2012) investigated the relationship between unemployment and inflation in Nigeria economy between 1977 and 2009. The results indicate that inflation had a negative impact

the forecast obtained by the univariate model. Both variables are collected over a time range from January 1985 until and including December 1997, whereas the last year is not used for constructing the optimal forecast, obtained by fitting a model through the data until the end of 1996. This will enable us to forecast the year 1997 using our model, and then comparing it to the actual data. Assuming no large one time shock, meaning that it is not captured by seasonality or cyclical behaviour in the

3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of Investment and Export in real terms from the period 1989/90 to 2013/14. The concerned variables are transformed into logarithm and hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t 〖LnI〗_t and 〖LnX〗_t . Fully Modified Ordinary Least Squares (FMOLS) is the main econometric methodology used in this paper to examine the role and impact of Indian FDI on Nepalese economic growth. The FMOLS of economic growth

telecommunication companies often utilize this technique. E.g. The demand for a new cell phone could be based upon the sales history of an existing model. Qualitative Methods like market research, Delphi technique etc. and causal method like life cycle analysis may be adopted in case of a new product launch. To assess the demand of an existing brand, moving average method and exponential smoothing may also be studied. |

growth of the company by slowly increasing its deposit during this time frame. The data also showed an increasing trend both for the GSP of Kansas and the Deposit. a. Naive Approach: Answer: Year Deposit GSP 2003 108.9 million 5.3 billion b. Moving Average: For Ma =3 For Ma=5 c. Exponential smoothing: A=.3 A=.5 A=.7 d. Linear Trend analysis e. f. e. Linear regression: DEPOSIT = -17

H0: There are no significant short-run and long-run impacts of trademarks on profit in Unilever (Gh.) Limited. H1: There are significant short-run and long-run impacts of trademarks on profit in Unilever (Gh.) Limited. H0: There are no significant short-run and long-run impacts of brand awareness on profit in Unilever (Gh.) Limited. H1: There are significant short-run and long-run impacts of brand awareness on profit in Unilever (Gh.) Limited. 3 Research Methods 3.1 Research Design The study

MANF 4615 PRODUCTION PLANNING & CONTROL Student: Hugo Costa Campbel Student number: z5007692 Due date: 25/08/2014 Assignment 1 Question 1: The customer order decoupling point is described as the point at which demand changes from independent to dependant. What does this mean and why is this important to managers? The demand can be divided in two different types: - The independent demand depends on the market conditions and it is difficult to control and must be forecast. Even though

TYPES OF FORECASTING METHODS Qualitative methods: These types of forecasting methods are based on judgments or opinions, and are subjective in nature. They do not rely on any mathematical computations. Quantitative methods: These types of forecasting methods are based on quantitative models, and are objective in nature. They rely heavily on mathematical computations. QUALITATIVE FORECASTING METHODS Qualitative Methods Executive Opinion Market Research Delphi