Exam 14: Introduction to Time Series Regression and Forecasting

To choose the number of lags in either an autoregression or in a time series regression model with multiple predictors, you can use any of the following test statistics with the exception of the

An autoregression is a regression

Autoregressive distributed lag models include

Pseudo out of sample forecasting can be used for the following reasons with the exception of

Time series variables fail to be stationary when

The root mean squared forecast error (RMSFE)is defined as

Stationarity means that the

The time interval between observations can be all of the following with the exception of data collected

In order to make reliable forecasts with time series data, all of the following conditions are needed with the exception of

The first difference of the logarithm of Y_t equals

The forecast is

Negative autocorrelation in the change of a variable implies that

The Granger Causality Test

The Times Series Regression with Multiple Predictors

One reason for computing the logarithms (ln), or changes in logarithms, of economic time series is that

One of the sources of error in the RMSFE in the AR(1)model is

The j^th autocorrelation coefficient is defined as

The ADL(p,q)model is represented by the following equation

The AR(p)model

Departures from stationarity