Illustration 7.3 (p. 262-4) describes time-series forecasting of new home sales,
ID: 1106722 • Letter: I
Question
Illustration 7.3 (p. 262-4) describes time-series forecasting of new home sales, but you can see that the data is old. Click here (https://www.census.gov/construction/nrs/historical_data/index.html) to see the newest data in the first table: Houses Sold - Seasonal Factors, Total (Excel file is sold_cust.xls). Look at the monthly data on the "Reg Sold" tab. If you have trouble with the link, I have recreated the data in moodle in the CSV file "A3Q3 Census Housing Data."
Only keep the dates beginning in January 2010, so delete the earlier observations, and use the data through Sept. 2017. Keep only the US data, both the seasonally unadjusted monthly (column B) and the seasonally adjusted annual (column G). Make a new column of seasonally adjusted monthly by dividing the annual data by 12. Make a column called "t" similar to the book's column 4 on page 262 (t will go from 1 to 93 through Sept. 2017); make a t2 column too (since, if you look at the data, you can see sales are slightly U-shaped; hence the quadratic). Also make a column "D" that is a dummy variable equal to one during the spring and summer months, similar to the book's column 5.
Determine the correlation between the unadjusted and the adjusted monthly data (=CORREL(unadjust., adjust.) in Excel), and produce scatterplots (with connectors) of both.
Q1. Do you think making a seasonal adjustment will be useful, given what you observe at this point?
Select one:
a. Yes, since the seasonally unadjusted data traces a smoother path (graphically speaking) than the seasonally adjusted data.
b. No; even though the unadjusted is more volatile than the adjusted, it is expected to be and thus making the adjustment will not improve the analysis.
c. Yes, since even though they follow the same general trend, the seasonally unadjusted data is predictably more volatile than the seasonally adjusted data.
d. No, since there is no discernible difference between the two data series, as far as is evident in the graph.
using the attached file; answer the following question
Q2. In comparing the regression results between model 1 and 2 (the unadjusted sales), it is notable that including the extra variable D
D in model 2
Select one:
a. increases the R2, but it is insignificant and has an unexpected sign.
b. makes the t2 variables statistically insignificant in model 2, whereas they were significant in model 1.
c. increases the R2 as expected but reduces the adjusted R2, suggesting that D does not contribute to the explanatory power of the model.
d. dramatically improves the explanatory power of the model.
Q3. In comparing the regression results between models 2 and 3, it is notable that
Select one:
a. including the D variable in model 2 results in a much larger adjusted R2, suggesting that the inclusion of the dummy variable is necessary to boost predictive power.
b. the D variable in model 2 does a decent job of capturing the seasonal effect, since the results between the two models are not hugely different and D has the expected sign and is statistically significant.
c. the coefficient estimates for t and t2 change dramatically, even though the models are very comparable (unadjusted with a seasonal dummy is pretty close to seasonally adjusted).
d. dropping the D variable in model 3 pulls the R2 down, which is unexpected since D in model 2 is statistically insignificant.
Q4. The regression results for model 4 are notable because
Select one:
a. the adjusted R2 is higher than in the comparable model 3 (without the D).
b. adding the redundant D variable to the seasonally adjusted data causes the coefficient estimates for t and t2 to be dramatically different than they were in models 2 and 3.
c. making the seasonal adjustment in the dependent variable, in addition to adding the D dummy, yields the best results in terms of significant coefficients, explanatory power, and expected signs.
d. adding a redundant seasonal dummy to already seasonally-adjusted data results in the D variable being insignificant, as expected, and the model's explanatory power is essentially the same as model 3.
date US NSA month US SA year Jan-10 24 345 Feb-10 27 336 Mar-10 36 381 Apr-10 41 422 May-10 26 280 Jun-10 28 305 Jul-10 26 283 Aug-10 23 282 Sep-10 25 317 Oct-10 23 291 Nov-10 20 287 Dec-10 23 326 Jan-11 21 307 Feb-11 22 270 Mar-11 28 300 Apr-11 30 310 May-11 28 305 Jun-11 28 301 Jul-11 27 296 Aug-11 25 299 Sep-11 24 304 Oct-11 25 316 Nov-11 23 328 Dec-11 24 341 Jan-12 23 335 Feb-12 30 366 Mar-12 34 354 Apr-12 34 354 May-12 35 370 Jun-12 34 360 Jul-12 33 369 Aug-12 31 375 Sep-12 30 385 Oct-12 29 358 Nov-12 28 392 Dec-12 28 399 Jan-13 32 442 Feb-13 36 439 Mar-13 41 449 Apr-13 43 451 May-13 40 430 Jun-13 43 463 Jul-13 33 376 Aug-13 31 380 Sep-13 31 399 Oct-13 36 444 Nov-13 32 446 Dec-13 31 441 Jan-14 33 447 Feb-14 35 423 Mar-14 39 410 Apr-14 39 401 May-14 43 452 Jun-14 38 416 Jul-14 35 402 Aug-14 36 449 Sep-14 37 466 Oct-14 38 474 Nov-14 31 446 Dec-14 35 492 Jan-15 39 523 Feb-15 45 549 Mar-15 46 481 Apr-15 48 500 May-15 47 504 Jun-15 44 476 Jul-15 43 498 Aug-15 41 513 Sep-15 35 461 Oct-15 39 482 Nov-15 36 508 Dec-15 38 536 Jan-16 39 520 Feb-16 45 525 Mar-16 50 533 Apr-16 55 566 May-16 53 560 Jun-16 50 559 Jul-16 54 627 Aug-16 46 567 Sep-16 44 570 Oct-16 46 577 Nov-16 40 579 Dec-16 39 548 Jan-17 45 599 Feb-17 51 615 Mar-17 61 638 Apr-17 56 590 May-17 57 606 Jun-17 56 619 Jul-17 50 582 Aug-17 45 561 Sep-17 52 667Explanation / Answer
1)C)Yes since even though they follow the same general trend,the seasonally unadjusted data is predictably more volatile than the seasonally adjusted data.
2)C)increases the R2 as expected but reduces the adjusted R2,suggesting that D does not contribute to the explanatory power of the model.
3)C)the coefficient estimate for t and t2 change dramatically even though the models are very comparable.
4)C)making the seasonal adjustment in the dependent variable in addition to adding the D dummy yields the best results in terms of significant coefficient explanatory power and expected signs.