{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Wooldridge IE AISE SSM ch10

# Wooldridge IE AISE SSM ch10 - CHAPTER 10 SOLUTIONS TO...

This preview shows pages 1–3. Sign up to view the full content.

This edition is intended for use outside of the U.S. only, with content that may be different from the U.S. Edition. This may not be resold, copied, or distributed without the prior consent of the publisher. 52 CHAPTER 10 SOLUTIONS TO PROBLEMS 10.1 (i) Disagree. Most time series processes are correlated over time, and many of them strongly correlated. This means they cannot be independent across observations, which simply represent different time periods. Even series that do appear to be roughly uncorrelated – such as stock returns – do not appear to be independently distributed, as you will see in Chapter 12 under dynamic forms of heteroskedasticity. (ii) Agree. This follows immediately from Theorem 10.1. In particular, we do not need the homoskedasticity and no serial correlation assumptions. (iii) Disagree. Trending variables are used all the time as dependent variables in a regression model. We do need to be careful in interpreting the results because we may simply find a spurious association between y t and trending explanatory variables. Including a trend in the regression is a good idea with trending dependent or independent variables. As discussed in Section 10.5, the usual R -squared can be misleading when the dependent variable is trending. (iv) Agree. With annual data, each time period represents a year and is not associated with any season. 10.3 Write y* = α 0 + ( δ 0 + δ 1 + δ 2 )z* = α 0 + LRP z *, and take the change: Δ y * = LRP Δ z *. 10.5 The functional form was not specified, but a reasonable one is log( hsestrts t ) = α 0 + α 1 t + δ 1 Q2 t + δ 2 Q3 t + δ 3 Q3 t + β 1 int t + β 2 log( pcinc t ) + u t , Where Q2 t , Q3 t , and Q4 t are quarterly dummy variables (the omitted quarter is the first) and the other variables are self-explanatory. This inclusion of the linear time trend allows the dependent variable and log( pcinc t ) to trend over time ( int t probably does not contain a trend), and the quarterly dummies allow all variables to display seasonality. The parameter β 2 is an elasticity and 100 β 1 is a semi-elasticity. 10.7 (i) pe t -1 and pe t -2 must be increasing by the same amount as pe t . (ii) The long-run effect, by definition, should be the change in gfr when pe increases permanently. But a permanent increase means the level of pe increases and stays at the new level, and this is achieved by increasing pe t -2 , pe t -1 , and pe t by the same amount.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document