Given a text T, and several options, according to the question posed in the text T to choose the most appropriate option from the options as the answer, please directly output the most appropriate option, do not need to output the explanation.

Adele Chiesa is a money manager for the Bianco Fund. She is interested in recent findings showing that certain business condition variables predict excess US stock market returns (one-month market return minus one-month T-bill return). She is also familiar with evidence showing how US stock market returns differ by the political party affiliation of the US President. Chiesa estimates a multiple regression model to predict monthly excess stock market returns accounting for business conditions and the political party affiliation of the US President:Excess stock market returnt= a0+ a1Default spreadt–1+ a2Term spreadt–1+ a3Pres party dummyt–1+ etDefault spread is equal to the yield on Baa bonds minus the yield on Aaa bonds. Term spread is equal to the yield on a 10-year constant-maturity US Treasury index minus the yield on a 1-year constant-maturity US Treasury index. Pres party dummy is equal to 1 if the US President is a member of the Democratic Party and 0 if a member of the Republican Party.Chiesa collects 432 months of data (all data are in percent form, i.e., 0.01 = 1 percent). The regression is estimated with 431 observations because the independent variables are lagged one month. The regression output is in Exhibit 1. Exhibits 2 through 5 contain critical values for selected test statistics.
| Exhibit l.Multiple Regression Output(the Dependent Variable Is the One-Month |
| Market Return in Excess of the One-Month T-Bill Return) |
| Coefficient | t-Statistic | p-Value |
| Intercept | -4.60 | -4.36 | <0.01 |
| Default spread-i | 3.04 | 4.52 | <0.01 |
| Tem spread-i | 0.84 | 3.41 | <0.01 |
| Pres party dummy-1 | 3.17 | 4.97 | <0.01 |
| Number of observations | 431 |
| Test statistic from B reusch-Pagan(BP) test | 7.35 |
| R | 0.053 |
| Adjusted R | 0.046 |
| Durbin-Watson(DW) | 1.65 |
| Sum of square der ors(SSE) | 19，048 |
| Regression sum of squares(SSR) | 1，071 |


An intern working for Chiesa has a number of questions about the results in Exhibit 1:Question 1: How do you test to determine whether the overall regression model is significant?Question 2: Does the estimated model conform to standard regression assumptions? For instance, is the error term serially correlated, or is there conditional heteroskedasticity?Question 3: How do you interpret the coefficient for the Pres party dummy variable?Question 4: Default spread appears to be quite important. Is there some way to assess the precision of its estimated coefficient? What is the economic interpretation of this variable?After responding to her intern's questions, Chiesa concludes with the following statement: “Predictions from Exhibit 1 are subject to parameter estimate uncertainty, but not regression model uncertainty.”
| Exhibit 2.Critical Values for the Durbin-Watson Statistic(a=0.05) |
| K=3 |
|  | d | dx |
| 420 | 1.825 | 1.854 |
| 430 | 1.827 | 1.855 |
| 440 | 1.829 | 1.857 |



| Exhibit 3.Table of the Student'st-Distribution(One-Tailed Probabilities fordf=c) |
| P | t |
| 0.10 | 1.282 |
| 0.05 | 1.645 |
| 0.025 | 1960 |
| 0.01 | 2.326 |
| 0.005 | 2.576 |



| Exhibit 4.Values of y’ |
| Probability in Right Tail |
| df | 0.975 | 0.95 | 0.05 | 0.025 |
| 1 | 0.0001 | 0.0039 | 3.841 | 5.024 |
| 2 | 0.0506 | 0.1026 | 5.991 | 7.378 |
| 3 | 0.2158 | 0.3518 | 7.815 | 9.348 |
| 4 | 0.4840 | 0.7110 | 9.488 | 11.14 |



| Exhibit 5.Table of the F-Distribution(Critical Values for Right-Hand Tail Area Equal |
| dfl |
| df2 | 1 | 2 | 3 | 4 | 427 |
| 1 | 161 | 200 | 216 | 225 | 254 |
| 2 | 18.51 | 19.00 | 19.16 | 19.25 | 19.49 |
| 3 | 10.13 | 9.55 | 9.28 | 9.12 | 8.53 |
| 4 | 7.71 | 6.94 | 6.59 | 6.39 | 5.64 |
| 427 | 3.86 | 3.02 | 2.63 | 2.39 | 1.17 |


 
Which of the following is Chiesa's best response to Question 2 regarding serial correlation in the error term? At a 0.05 level of significance, the test for serial correlation indicates that there is:
Option A:no serial correlation in the error term.
Option B:positive serial correlation in the error term.
Option C:negative serial correlation in the error term.

Answer: B