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Appendix D: Global Growth Data and Projections
Pages 229-242

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From page 229...
... DATA As described below one could construct two time series for economic growth based on the Maddison Project Database.1 The Maddison Project provides lengthy time series of per capita income for virtually all countries. Its starting point was the seminal work of Summers and Heston (1984)
From page 230...
... To apply the MW approach to the Maddison data, one must construct a univariate series for global growth rates. World GDP per capita is already aggregated and provided directly by the Maddison Project for 1950-2010.
From page 231...
... Summary statistics for this distribution are given in Table D-2. TABLE D-1 Growth Rates of Aggregate GDP per Capita, in percent Year 25 Countries 1870-2010 Entire World 1950-2010 1871 1.58 NA 1872 2.93 NA 1873 1.04 NA 1874 2.40 NA 1875 1.94 NA 1876 –2.04 NA 1877 1.10 NA 1878 0.80 NA 1879 0.64 NA 1880 4.66 NA 1881 1.98 NA 1882 2.34 NA 1883 1.27 NA 1884 0.11 NA 1885 –0.24 NA 1886 1.04 NA 1887 2.34 NA 1888 0.50 NA 1889 2.36 NA 1890 0.97 NA 1891 0.42 NA 1892 2.48 NA 1893 –1.48 NA 1894 1.09 NA 1895 3.55 NA 1896 0.06 NA 1897 2.31 NA 1898 3.13 NA 1899 3.23 NA 1900 0.80 NA 1901 2.15 NA 1902 0.07 NA 1903 1.81 NA 1904 –0.20 NA 1905 2.50 NA 1906 5.13 NA 1907 1.45 NA 1908 –3.95 NA 1909 4.13 NA 1910 0.20 NA 1911 2.77 NA 1912 2.72 NA continued
From page 232...
... 232 VALUING CLIMATE DAMAGES Year 25 Countries 1870-2010 Entire World 1950-2010 1913 1.87 NA 1914 –7.78 NA 1915 0.69 NA 1916 6.61 NA 1917 –2.84 NA 1918 0.49 NA 1919 –1.13 NA 1920 0.71 NA 1921 –1.17 NA 1922 5.78 NA 1923 3.55 NA 1924 4.18 NA 1925 3.00 NA 1926 2.10 NA 1927 2.23 NA 1928 2.34 NA 1929 3.13 NA 1930 –6.07 NA 1931 –7.12 NA 1932 –6.81 NA 1933 1.24 NA 1934 4.33 NA 1935 3.88 NA 1936 6.04 NA 1937 3.98 NA 1938 –0.14 NA 1939 5.89 NA 1940 1.65 NA 1941 6.61 NA 1942 6.52 NA 1943 7.83 NA 1944 1.70 NA 1945 –10.27 NA 1946 –13.33 NA 1947 1.05 NA 1948 3.93 NA 1949 2.61 NA 1950 5.67 NA 1951 5.35 4.08 1952 2.72 2.65 1953 3.51 3.05 1954 1.27 1.42 1955 5.30 4.32 1956 2.26 2.70 1957 2.24 1.69 1958 0.10 1.12 1959 4.60 2.62 1960 3.70 3.65 1961 2.89 2.05 1962 4.09 2.84
From page 233...
... APPENDIX D 233 TABLE D-1  Continued Year 25 Countries 1870-2010 Entire World 1950-2010 1963 3.22 2.14 1964 4.83 5.02 1965 3.88 3.16 1966 4.27 3.30 1967 2.63 1.65 1968 4.56 3.28 1969 4.23 3.35 1970 2.67 3.07 1971 2.43 1.91 1972 4.09 2.69 1973 4.97 4.52 1974 0.17 0.39 1975 –0.59 –0.26 1976 3.85 3.06 1977 2.70 2.24 1978 3.28 2.60 1979 2.95 1.74 1980 0.47 0.25 1981 0.44 0.26 1982 –1.01 –0.50 1983 1.52 0.86 1984 3.81 2.79 1985 2.79 1.70 1986 2.45 1.77 1987 2.52 2.04 1988 3.29 2.49 1989 2.52 1.51 1990 0.73 0.32 1991 0.22 –0.16 1992 0.98 0.42 1993 0.49 0.74 1994 2.32 1.97 1995 1.84 2.58 1996 1.87 1.88 1997 2.70 2.51 1998 1.88 0.52 1999 2.27 2.30 2000 2.93 3.47 2001 0.62 1.70 2002 0.62 2.29 2003 1.09 3.47 2004 2.40 3.85 2005 1.78 3.18 2006 2.06 3.85 2007 1.87 3.08 2008 –0.48 1.62 2009 –4.31 –1.96 2010 NA 4.39 NOTES: NA, not available. See text for explanation of the calculation.
From page 234...
... IMPLEMENTATION The MATLAB code for implementing the MW approach is freely available from Mark Watson's website.2 Only a subset of the code is required to generate the results presented below: lr_main_annual.m, figure_1_2.m, Sigma_Compute.m, den_invariate.m, psi_compute.m, t_mixture.m, and lr_pred_set.m. It is possible to replicate our estimates using the nine-step procedure detailed in the rest of this section.3 Step One Alter the directory paths and file names in the code to point to the data.
From page 235...
... Truncating the set at q < T does involve some loss of information and thus some loss of econometric efficiency; a larger q would decrease the uncertainty in the predictions of growth rates. However, a larger q weakens the approximations utilized by this approach: the distribution of the transformed data would be further from the limiting normality and the shape of spectrum could exhibit greater deviations from the approximate shape near a frequency of 0 (the latter of which is not mitigated by a larger sample size T)
From page 236...
... Mean 1.37 1.44 2.14 2.18 Std. Deviation 1.02 1.34 1.03 1.40 1st Percentile –1.85 –3.08 –1.17 –2.42 5th Percentile –0.44 –0.80 0.56 0.29 10th Percentile 0.15 0.06 1.13 1.07 25th Percentile 0.90 0.99 1.75 1.78 33rd Percentile 1.13 1.23 1.91 1.95 50th Percentile 1.49 1.58 2.18 2.20 66th Percentile 1.78 1.85 2.42 2.45 75th Percentile 1.96 2.04 2.60 2.64 90th Percentile 2.42 2.63 3.13 3.31 95th Percentile 2.78 3.20 3.59 3.99 99th Percentile 3.71 4.89 4.95 6.26 The unobserved random variable YT is the average growth rate from time T + 1 to time T + h, relative to the observed average growth rate from t = 1 to T: YT = xT+1:T+h − x1:T .
From page 237...
... Projecting from World 1950-2010 Data to 2010-2300 (290 Years) FIGURE D-2 Predictive distribution for average annual growth rates.
From page 238...
... To compute the means of the terciles of average growth rate from time T + 1 to time T + h, shown in Table D-3, one would have to go beyond the MW analysis. Each tercile is defined as a range: from the 0th percentile (negative infinity)
From page 239...
... The expectation of the average growth rate from time T + 1 to T + h, conditional on the average growth rate falling in range R = (α, β)
From page 240...
... ⎟ ⎠ ⎝ s ( di ) ⎟⎥ ⎠ ⎣ ⎦ Notice that the bracketed term is just the expectation of a random variable ZT distributed as a standard Student's t with q = 12 degrees of freedom, falling in the range x1:T m ( di )
From page 241...
... . Improved international comparisons of real product and its composition: 1950–1980.


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