Time series

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

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 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

How to create an EcOS database? In EcOS, data and metadata are stored in time series database. An EcOS database has some key characteristics: • An EcOS database acts as a container for data, metadata, and objects. • The database structure is governed by its attributes. • Since it is a time-series database, each series stored is tied to a time dimension. • Each time series has a default scale. This video shows how to create an EcOS database. Step 1. Planning. Before constructing a database in EcOS

However, demand management is when decisions made affect the amounts of one or more products that are a part of the supply chain. (4) How do lead times and forecast errors affect supply chain performance? Lead time is the time between the initiation and completion of a process. It affects supply chain performance because the longer the lead time the longer it will take for materials to move through the supply chain. Forecasting errors affect the supply chain because the calculations formulated

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 minus forecast sales using alpha 0.15 totaled -33.404. The mean percentage error (MPE) average utilizing alpha 0.15 was -0.105 and -0.061 utilizing alpha 0.9. The MAPE

had been found by the authors. Ahmed & Shakur( 2011) performed a research to highlight the problems created by the debt (external debt) to economic growth of Pakistan. They have used the unit root test and Johansen co-integration to analyze time series data from FY 1981 to FY 2008. The Granger Causality Vector Error Correction (GCVEC) method proved unidirectional relationship between external debt and growth rate of GDP per capita. Wijeweera, Dollery & Pathberya (2005), investigated the connections

0.85084 34.27454 0.00000 0.933336 0.919543 67.66915 0.00000 0.889215 0.866294 38.79462 0.00000 0.924163 0.908473 58.89987 0.00000 0.739903 0.68609 13.74949 0.00000 Serial Correlation and Heteroskedasticity: Normally the possibilities for the time series data to have the Serial correlation or auto correlation are more. It can be tested with the

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

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