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# We Helped With This Statistics with R Economics Homework: Have A Similar One?

SOLVED

Category | Economics |
---|---|

Subject | R | R Studio |

Difficulty | Undergraduate |

Status | Solved |

More Info | Help With Macroeconomics In R |

## Short Assignment Requirements

The question is in Exercise 3.png, only question 3. The data is in excel. The data in excel is not in quarter format, you first need to convert the year to Q1, Q2, Q3, Q4. I also attached the solution in the zip.file. Just for reference. The result should be like the solution. I require Rmd file only.

## Assignment Image

EXERCISES
1. Update the data set on annual house prices and interest rates analyzed in
Section 4.1.1. Compute the ACF and PACF functions of house prices, interest
rates, house price growth, and interest rate changes. Comment on the differ-
ences across autocorrelation functions. In which series do you find stronger time
dependence?
2. With the new data from Exercise 1, replicate models (i) and (ii) of Section 4.1.1.
Run different regression models by adding two more lags for price and interest
rate movements. Compare your results with models (i) and model (ii). Is a uni-
variate information set more valuable than a multivariate information set to
explain housing prices? Are the new models better than models (i) and (ii) at
explaining price growth?
3. Download the same data as in Exercise 1, but at the quarterly frequency.
Compute the ACF and PACF functions of quarterly house prices, interest rates,
house price growth, and interest rates changes. Comment on the differences
across autocorrelation functions. In which series do you find stronger time
dependence? Examine the differences in the autocorrelation functions of quar-
terly data versus annual data.
4. Using the same data set as in Exercise 3 and for house price growth, run several
regression models with one, two, three, and four lags of price growth in the right-
hand side of the model. Analyze the regression results. Compare the regression
models at the quarterly frequency with the models at the annual frequency that
you estimated in Exercise 2. Choose your favorite model and implement a recur-
sive and a rolling estimation scheme. Plot the time series of the estimates of the
regression coefficients and observe how much they change over time.
5. Consider the manager of a large department store. Among other responsibili-
ties, she is in charge of inventory control so that she needs good forecasts of
department sales. Think about the costs of overstocking and understocking
merchandise, and recommend a loss function that she should use to produce
her forecast.
6. The Board of Governors of the Federal Reserve publishes the 1-quarter-ahead
forecasts of real GDP growth. These are known as the Greenbook forecasts.
Download the realized values of GDP growth (from the website of the Federal

## Assignment Image

Sample: 1975Q1 201104
Included observations: 148
Autocorrelation Partial Correlation
AC PAC Q-Stat Prob
1
2
3
0.988 0.988 147.51 0.000
0.975 -0.094 291.92 0.000
0.960 -0.042 432.95 0.000
4 0.944 -0.050 570.31 0.000
5 0.926 -0.101 703.35 0.000
0.906 -0.066 831.62 0.000
0.885 -0.027 954.97 0.000
6
7
8 0.864 -0.021 1073.3 0.000
9 0.841 -0.063 1186.3 0.000
10 0.818 -0.020 1293.8 0.000
0.794 0.019 1396.1 0.000
0.771 -0.009 1493.2 0.000
13 0.746 -0.077 1584.8 0.000
0.720 -0.049 1670.8 0.000
11
12
14
15
0.695 0.002 1751.3 0.000
16
0.669 -0.017 1826.5 0.000
17 0.641 -0.060 1896.2 0.000
18 0.613 -0.028 1960.5 0.000
19 0.586 0.007 2019.6 0.000
20 0.559 0.008 2073.7 0.000
Figure 5: ACF, PACF and Q-Statistic of P

## Assignment Image

Sample: 1975Q1 201104
Included observations: 148
Autocorrelation Partial Correlation
1
2
3
4
5
0.976 0.976 143.99 0.000
0.946 -0.157 280.10 0.000
0.915 -0.018 408.23 0.000
0.882 -0.043 528.19 0.000
0.847 -0.066 639.52 0.000
0.814 0.056 743.20 0.000
0.785 0.036 840.23 0.000
0.757 0.000 931.15 0.000
0.728 -0.065 1015.7 0.000
0.696 -0.051 1093.7 0.000
0.665 -0.015 1165.3 0.000
0.631 -0.059 1230.4 0.000
0.602 0.093 1289.9 0.000
0.574 0.005 1344.6 0.000
0.545 -0.076 1394.2 0.000
0.515 -0.023 1438.8 0.000
0.489 0.034 1479.3 0.000
18 0.463 -0.003 1516.0 0.000
17
19 0.441 0.076 1549.5 0.000
20 0.422 0.039 1580.4 0.000
6
7
8
9
10
11
12
13
AC PAC Q-Stat Prob
14
15
16
Figure 6: ACF, PACF and Q-Statistic of R

## Assignment Image

Sample: 1975Q1 201104
Included observations: 147
Autocorrelation Partial Correlation
Lollo-al
I
PAC Q-Stat Prob
1
6
7
8
0.634 0.634 60.317 0.000
2 0.285 -0.196 72.562 0.000
3 0.508 0.725 111.85 0.000
4 0.724 0.068 192.07 0.000
5 0.383 -0.413 214.68 0.000
0.080 -0.003 215.69 0.000
0.295 0.081 229.32 0.000
0.473 -0.031 264.53 0.000
9 0.167 -0.078 268.95 0.000
10 -0.084 -0.027 270.08 0.000
11 0.112 -0.054 272.11 0.000
12 0.257 -0.020 282.84 0.000
13 -0.037 -0.148 283.06 0.000
14 -0.260 -0.041 294.22 0.000
15 -0.075 -0.050 295.16 0.000
16 0.091 0.159 296.55 0.000
17 -0.128 0.001 299.29 0.000
18 -0.321 -0.078 316.77 0.000
19 -0.158 -0.064 321.03 0.000
20 -0.009 -0.053 321.04 0.000
AC
Figure 7: ACF, PACF and Q-Statistic of DP

## Assignment Image

Sample: 1975Q1 201104
Included observations: 147
Autocorrelation Partial Correlation
AC
0.264 0.264 10.435 0.001
0.074 0.004 11.255 0.004
3 0.064 0.047 11.881 0.008
4 0.031 0.003 12.029 0.017
5 -0.092 -0.112 13.336 0.020
6 -0.058 -0.010 13.853 0.031
7 -0.042 -0.022 14.129 0.049
8 0.051 0.085 14.543 0.069
9 0.078 0.060 15.510 0.078
10 -0.016 -0.067 15.552 0.113
11 0.072 0.084 16.390 0.127
12 -0.039 -0.107 16.642 0.164
13 -0.023 0.022 16.726 0.212
14 0.027 0.052 16.844 0.265
15 -0.016 -0.041 16.889 0.326
16 -0.069 -0.038 17.683 0.343
17 -0.004 -0.001 17.686 0.409
18 -0.148 -0.165 21.391 0.260
19 -0.140 -0.058 24.755 0.169
20 -0.050 0.007 25.182 0.195
1
2
PAC Q-Stat Prob
Figure 8: ACF, PACF and Q-Statistic of DR