## Description

Q1 (1 points): For the Box and Cox power transformations, use calculus to show that, for any

fixed x > 0, as λ → 0, (x

λ − 1)/λ → log(x).

1

Q2 (1 points): Use two different smoothing techniques (e.g., Kernel, Lowess, spline, etc.) to

estimate the trend in the global temperature series globtemp. Comment!

Q3 (8 points for undergraduate students and 6 points for graduate students): Consider

the two weekly time series oil and gas. The oil series is in dollars per barrel, while the gas series is

in cents per gallon.

1. Plot the data on the same graph. Do you believe the series are stationary (explain your answer)?

2. In economics, it is often the percentage change in price (termed growth rate or return), rather

than the absolute price change, that is important. Argue that a transformation of the form

yt = ∇ log xt might be applied to the data, where xt

is the oil or gas price series.

3. Transform the data as described in part (2), plot the data on the same graph, look at the

sample ACFs and CCF of the transformed data, and comment.

4. Exhibit scatterplots of the oil and gas growth rate series for up to three weeks of lead time of

oil prices; include a nonparametric smoother in each plot and comment on the results (e.g.,

Are there outliers? Are the relationships linear?).

5. There have been a number of studies questioning whether gasoline prices respond more quickly

when oil prices are rising than when oil prices are falling (“asymmetry”). We will attempt to

explore this question here with simple lagged regression; we will ignore some obvious problems

such as outliers and autocorrelated errors, so this will not be a definitive analysis. Let Gt and

Ot denote the gas and oil growth rates.

(i) Fit the regression (and comment on the results)

Gt = β0 + β1It + β2Ot + β3Ot−1 + wt

where wt ∼ wn(0, σ2

w) and It = 1 if Ot ≥ 0 (i.e., It

is the indicator of increase in the oil

price) and 0 otherwise (no positive growth in the oil price).

(ii) What is the fitted model when there is negative growth in oil price at time t? What is

the fitted model when there is no or positive growth in oil price? Do these results support

the asymmetry hypothesis?

(iii) Fit the regression

Gt = β0 + β1Ot + wt

and decide (using AIC/BIC) whether it is better than the previous one or not?

(iv) Test the full model as specified in part (i) against the reduced model as as specified in

part (iii).

(v) Analyze the residuals from the best fit and comment.

Q4 (2 points): Quarterly earnings per share for the Johnson & Johnson Company are given in

the data file named jj. The earnings per share data, say yt

, covers the years from 1960 through

1980. In this problem, you are going to fit a special type of structural model, xt = log(yt), where

xt = Tt + St + Et

, where Tt

is a trend component, St

is a seasonal component, and Et

is error (noise)

term. Note that the time t is in quarters (1960.00, 1960.25, · · ·) so one unit of time is a year.

1. Fit the regression model

xt = βt

|{z}

trend

+ α1Q1(t) + α2Q2(t) + α3Q3(t) + α4Q4(t)

| {z }

seasonal

+ wt

|{z}

noise

where Qi(t) = 1 if time t corresponds to quarter i = 1, 2, 3, 4, and zero otherwise. The Qi(t)’s

are called indicator variables. We will assume for now that wt

is a Gaussian white noise

sequence.

2. If the model is correct, what is the estimated average annual increase in the logged earnings

per share?

3. If the model is correct, does the average logged earnings rate increase or decrease from the

third quarter to the fourth quarter? And, by what percentage does it increase or decrease?

4. What happens if you include an intercept term in the model in (a)? Explain why there was a

problem.

5. Graph the data, xt

, and superimpose the fitted values, say ˆxt

, on the graph. Examine the

residuals, xt − xˆt

, and state your conclusions. Does it appear that the model fits the data well

(do the residuals look white)?