Econ2p91 Review Questions Multiple Choice Solutions Chapter 14 PDF

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ECON 2P91-...

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1)

Autoregressive distributed lag models include a. current and lagged values of the error term. b. lags of the dependent variable, and lagged values of additional predictor variables. c. current and lagged values of the residuals. d. lags and leads of the dependent variable.

2)

In order to make reliable forecasts with time series data, all of the following conditions are needed with the exception of a. coefficients having been estimated precisely. b. the regression having high explanatory power. c. the regression being stable. d. the presence of omitted variable bias.

3)

The first difference of the logarithm of Yt equals a. the first difference of Y. b. the difference between the lead and the lag of Y. c. approximately the growth rate of Y when the growth rate is small. d. the growth rate of Y exactly.

4)

The time interval between observations can be all of the following with the exception of data collected a. daily. b. by decade. c. bi-weekly. d. across firms.

5)

One reason for computing the logarithms (ln), or changes in logarithms, of economic time series is that a. numbers often get very large. b. economic variables are hardly ever negative. c. they often exhibit growth that is approximately exponential. d. natural logarithms are easier to work with than base 10 logarithms.

6)

The jth autocorrelation coefficient is defined as

a.

cov(Yt , Yt  1 ) var(Yt ) var(Yt  1)

.

cov(Yt , X t  j  1 ) b.

var(Yt ) var( X t  j )

c.

cov(Yt , ut ) var(Yt ) var(ut )

.

.

cov(Yt , Yt  j ) 7)

var(Yt ) var(Yt  j ) d. . An autoregression is a regression a. of a dependent variable on lags of regressors. b. that allows for the errors to be correlated. c. model that relates a time series variable to its past values. d. to predict sales in a certain industry.

8)

The root mean squared forecast error (RMSFE) is defined as E[| YT  YˆT |T  1 |] . a. ˆ E[( YT  1  YT  1|T ) 2 ] b. . (b) 2 (YT  YˆT| T 1 ) c. . E[(YT  YˆT |T  1)] . d.

9)

The forecast is a. made for some date beyond the data set used to estimate the regression. b. another word for the OLS predicted value. c. equal to the residual plus the OLS predicted value. d. close to 1.96 times the standard deviation of Y during the sample.

10)

The AR(p) model Y   0   pYt  p  u t a. is defined as t . b. represents Yt as a linear function of p of its lagged values. Y   0  1 X t   pY t  p  u t c. can be represented as follows: t . Yt   0  1Yt 1  u t p . d. can be written as

11)

The ADL(p,q) model is represented by the following equation a. b. c. d.

y t =β 0 + β q y t −q +δ p x t− p +ut y t =β 0 + β 1 y t−1 + β 2 y t−2 +. ..+β p y t−p +δ q u t−q y t =β 0 + β 1 y t−1 + β 2 y t−2 +. ..+β p y t−p +δ 0 +δ 1 x t−1 +ut−q y t =β 0 + β 1 y t−1 +β 2 y t−2 +...+β p y t−p +δ 1 x t−1 +δ 2 x t−2 +. ..+δ q xt−q +ut

(d)...