1 / 36

Econ 314: Project 1

Econ 314: Project 1. Answers and Questions. Examining the Growth Data. Trends, Cycles, and Turning Points. The Growth Experience. Trend Growth Rates. Cycle Turning Points. Peaks. Troughs. Measuring Growth Rates. Compounding and Growth Rate Formulas. Product growth formula.

leonard
Download Presentation

Econ 314: Project 1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Econ 314: Project 1 Answers and Questions

  2. Examining the Growth Data Trends, Cycles, and Turning Points

  3. The Growth Experience

  4. Trend Growth Rates

  5. Cycle Turning Points Peaks Troughs

  6. Measuring Growth Rates Compounding and Growth Rate Formulas

  7. Product growth formula Continuously compounded: Formula holds exactly.

  8. Product growth formula Annually compounded: Formula holds approximately. Close when ab is small.

  9. Trend growth vs. average growth • Trend rate is slope of best-fit line • What is average growth rate? From period 0 to 2:

  10. Trend growth vs. average growth • Trend rate is slope of best-fit line • What is average growth rate? From period 0 to T:

  11. Trend growth vs. average growth Actual Log GDP - Egypt Fitted values 18.5 18 lnGDPT – lnGDP0 17.5 17 T 16.5 1950 1960 1970 1980 1990 Year

  12. Is Trend Growth Stable? Examining the Record

  13. Is the trend stable? Single trend for Japan

  14. Is the trend stable? Stability Test for Japan Source | SS df MS Number of obs = 51 -------------+------------------------------ F( 3, 47) = 5988.24 Model | 39.488173 3 13.1627243 Prob > F = 0.0000 Residual | .103310446 47 .002198095 R-squared = 0.9974 -------------+------------------------------ Adj R-squared = 0.9972 Total | 39.5914834 50 .791829668 Root MSE = .04688 ------------------------------------------------------------------------------ lgdp_jpn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- year | .0908236 .0013825 65.69 0.000 .0880424 .0936049 d | 115.4399 3.557021 32.45 0.000 108.2841 122.5957 dyear | -.0585122 .0018037 -32.44 0.000 -.0621408 -.0548836 _cons | -171.915 2.711848 -63.39 0.000 -177.3706 -166.4595 ------------------------------------------------------------------------------

  15. Is the trend stable?

  16. Cyclical GDP: Single trend

  17. Cyclical GDP: Split trend

  18. Are there two breaks?

  19. Cyclical series with two breaks

  20. Stationarity and Trends Is Log-Linear Trend Appropriate?

  21. “Definition” of stationarity • Stationary variable: • Same mean, variance, etc. at all times • Nonstationary variable: • Different level, variability, etc. over time • Includes trended or drifting variables • ln GDP is nonstationary for all countries

  22. Kinds of nonstationary series • Trend stationary • Deviations from a fixed trend line are stationary • Shocks from trend line are temporary • Difference stationary • Difference (yt - yt -1) is stationary, but may have nonzero mean (drift) • Shocks are permanent

  23. Difference stationary series • Random walk: • Random walk with drift:

  24. Fitting a trend to random walk with drift

  25. Fitting a trend to random walk with drift

  26. Fitting a trend to random walk with drift?

  27. Barely stationary time series • Consider first-order autoregressive process: • Stationary as long as  < 1. • Random walk (nonstationary) if  = 1. • How much difference is there between  = 1 and  = 0.998? • Not much! • Very hard to tell the difference with small samples

  28. Detecting non-stationarity • Examine behavior of three series: • E = “White noise” process • AUTO = Stationary autoregressive process with  = 0.998 based on E • WALK = Random-walk process ( = 1) based on E

  29. 3 series: 100 observations

  30. 3 series: 1000 observations

  31. 3 series: 10,000 observations

  32. Testing for stationarity • Complex econometric task • Low power with small samples • Difficult to tell  = 1 from  = 0.998 • Macroeconomists rarely have more than a few dozen observations that can be assumed to follow the same model

  33. Is the Business Cycle Global? Cross-Country Correlation in GDP and Growth

  34. GDP Correlation across Countries (partial sample) | lgdpARG lgdpAUS lgdpBEL lgdpBGD lgdpBRA lgdpBWA lgdpCHE -------------+--------------------------------------------------------------- lgdpAUS | 0.9731 1.0000 lgdpBEL | 0.9721 0.9952 1.0000 lgdpBGD | 0.8779 0.9606 0.9258 1.0000 lgdpBRA | 0.9670 0.9860 0.9945 0.8967 1.0000 lgdpBWA | 0.8986 0.9796 0.9774 0.9555 0.9765 1.0000 lgdpCHE | 0.9517 0.9695 0.9766 0.8902 0.9709 0.9368 1.0000 lgdpCHN | 0.9166 0.9614 0.9403 0.9926 0.9221 0.9694 0.8765 lgdpCRI | 0.9780 0.9930 0.9957 0.9277 0.9935 0.9770 0.9753 lgdpDOM | 0.9682 0.9928 0.9901 0.9566 0.9867 0.9901 0.9536 lgdpESP | 0.9707 0.9854 0.9936 0.8939 0.9899 0.9541 0.9899 lgdpGBR | 0.9667 0.9978 0.9913 0.9683 0.9807 0.9795 0.9637 lgdpHKG | 0.9148 0.9892 0.9889 0.9521 0.9807 0.9891 0.9641 lgdpIRL | 0.9415 0.9731 0.9609 0.9786 0.9448 0.9810 0.8957 lgdpITA | 0.9662 0.9896 0.9950 0.9243 0.9943 0.9817 0.9876 lgdpJAM | 0.9266 0.9373 0.9508 0.8260 0.9439 0.8819 0.9859 lgdpJPN | 0.9649 0.9861 0.9943 0.8979 0.9931 0.9642 0.9888 lgdpLUX | 0.9348 0.9674 0.9490 0.9799 0.9254 0.9481 0.8966 lgdpNOR | 0.9654 0.9939 0.9906 0.9606 0.9865 0.9928 0.9477 lgdpNPL | 0.9041 0.9784 0.9542 0.9917 0.9289 0.9777 0.9188 lgdpNZL | 0.9721 0.9832 0.9842 0.9246 0.9790 0.9544 0.9873 lgdpSWE | 0.9651 0.9924 0.9955 0.9287 0.9903 0.9702 0.9879 lgdpZAF | 0.9670 0.9905 0.9965 0.9129 0.9965 0.9750 0.9813 lgdpZWE | 0.9502 0.9834 0.9929 0.9025 0.9932 0.9710 0.9693 Red indicates statistical significance at 0.05 level.

  35. Growth Correlation across Countries (partial sample) | dlgdpARG dlgdpAUS dlgdpBEL dlgdpBGD dlgdpBRA dlgdpBWA dlgdpCHE -------------+--------------------------------------------------------------- dlgdpAUS | 0.1564 1.0000 dlgdpBEL | -0.0214 0.2282 1.0000 dlgdpBGD | -0.0453 0.0373 -0.1525 1.0000 dlgdpBRA | 0.1719 -0.0229 0.4139 -0.4083 1.0000 dlgdpBWA | -0.1491 0.1170 0.2482 -0.2898 0.2515 1.0000 dlgdpCHE | 0.0725 0.2017 0.6910 -0.0291 0.2503 0.0247 1.0000 dlgdpCHN | 0.3598 -0.1534 -0.3292 0.1350 -0.3923 -0.3808 -0.3173 dlgdpCRI | 0.2731 0.2673 0.0947 -0.0729 0.2426 0.0975 -0.0294 dlgdpDOM | -0.0103 0.0936 0.2444 0.1274 0.1431 0.0857 0.1904 dlgdpESP | 0.0690 0.0177 0.5137 -0.1825 0.3269 0.0438 0.4256 dlgdpGBR | 0.0946 0.53470.3743 -0.1678 0.1470 0.0753 0.3704 dlgdpHKG | 0.1212 0.2218 0.3662 -0.0932 0.3083 -0.0885 0.2327 dlgdpIRL | -0.1584 0.0863 0.1344 -0.0318 -0.1917 0.1266 0.0116 dlgdpITA | 0.0040 0.2391 0.6121 -0.0027 0.4549 0.2880 0.6058 dlgdpJAM | 0.0233 0.0889 0.2823 -0.1468 0.1601 -0.1291 0.4663 dlgdpJPN | -0.0125 0.1004 0.5290 -0.2788 0.4306 0.0166 0.5597 dlgdpLUX | 0.0406 0.0288 0.2727 -0.0178 0.0014 0.2350 0.1008 dlgdpNOR | 0.3090 -0.0042 0.1593 -0.38600.4475 0.1658 -0.0861 dlgdpNPL | -0.1916 -0.1163 -0.2844 0.2797 -0.2934 -0.2608 -0.4133 dlgdpNZL | 0.1967 0.2395 0.3512 0.0937 0.2439 -0.1179 0.3190 dlgdpSWE | -0.0920 0.2621 0.5957 0.0078 0.3820 -0.0466 0.5004 dlgdpZAF | 0.0609 0.3794 0.4953 -0.0800 0.3445 0.0107 0.4709 dlgdpZWE | -0.0366 -0.1575 0.2970 -0.2195 0.1408 -0.0826 0.2658 Red indicates statistical significance at 0.05 level.

  36. Final Conclusion Econ 314 Students Do Good Work!!

More Related