Exam 12: Time Series Analysis and Forecasting

The seasonal component of a time series is more likely to exhibit the relatively steady growth of a variable,such as the population of Egypt from 35 million in 1960 to 75 million in 2005.

If we use a value close to 1 for the level smoothing constant $a$ and a value close to 0 for the trend smoothing constant $\beta$ in Holt's exponential smoothing model,then we expect the model to respond very quickly to changes in the level,but very slowly to changes in the trend.

Holt's model differs from simple exponential smoothing in that it includes a term for:

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

Winter's method is an exponential smoothing method,which is appropriate for a series with trend but no seasonality.

There are a variety of deseasonalizing methods,but they are typically variations of:

Suppose that a simple exponential smoothing model is used (with a = 0.30)to forecast monthly sandwich sales at a local sandwich shop.After June's demand is observed at 1520 sandwiches,the forecasted demand for July is 1600 sandwiches.At the beginning of July,what would be the forecasted demand for August?

Extrapolation methods attempt to:

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels,trends and cycles in the data.

Econometric models can also be called:

An exponential trend is appropriate when the time series changes by a constant percentage each period.

Every form of exponential smoothing model has at least one smoothing constant,which is always between 0 and 1.

A regression approach can also be used to deal with seasonality by using variables for the seasons.

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else,except possible time itself - to forecast values of the variable.

Seasonal variations will not be present in a deseasonalized time series.

A trend component of a time series is a long-term,relatively smooth pattern or direction exhibited by a series,and its duration is more than one year.

In exponential smoothing models,the forecast is based on the level at time t,L_t,which is not observable and can only be estimated.

In a regression model with seasonal dummy variables,the coefficients on the dummy variables represent the additive factor relative to the reference quarter value,not the multiplicative factor.

In a multiplicative seasonal model,we multiply a "base" forecast by an appropriate seasonal index.These indexes,one for each season,typically average to 1.

Related to the runs test,if you use a Z-statistic and you get a Z value greater than 2.0,this means that there is evidence of _____in the series