US macro data: Q1 2002 to Q4 2018
Year | Quarter | lgdp | lcons |
2002 | 1 | 8.090727405 | 7.693344439 |
2002 | 2 | 8.12074481 | 7.72093452 |
2002 | 3 | 8.132059982 | 7.729213877 |
2002 | 4 | 8.150086309 | 7.769002535 |
2003 | 1 | 8.114433022 | 7.717169235 |
2003 | 2 | 8.14116416 | 7.750348662 |
2003 | 3 | 8.160887893 | 7.764183474 |
2003 | 4 | 8.18940026 | 7.805388232 |
2004 | 1 | 8.156672577 | 7.761059352 |
2004 | 2 | 8.182812167 | 7.784881808 |
2004 | 3 | 8.193427879 | 7.794406261 |
2004 | 4 | 8.222503422 | 7.844228958 |
2005 | 1 | 8.190303099 | 7.792566489 |
2005 | 2 | 8.216992014 | 7.823049915 |
2005 | 3 | 8.234528909 | 7.834903257 |
2005 | 4 | 8.251790155 | 7.874189545 |
2006 | 1 | 8.226165529 | 7.827541096 |
2006 | 2 | 8.253569775 | 7.856307178 |
2006 | 3 | 8.252284232 | 7.859554843 |
2006 | 4 | 8.274634633 | 7.902287343 |
2007 | 1 | 8.240737203 | 7.855593126 |
2007 | 2 | 8.268126008 | 7.878880348 |
2007 | 3 | 8.274430946 | 7.880646358 |
2007 | 4 | 8.297485481 | 7.918891719 |
2008 | 1 | 8.26891079 | 7.87469882 |
2008 | 2 | 8.274636417 | 7.88642871 |
2008 | 3 | 8.26790354 | 7.87350833 |
2008 | 4 | 8.26465224 | 7.891799608 |
2009 | 1 | 8.215542004 | 7.842955691 |
2009 | 2 | 8.236355377 | 7.865876393 |
2009 | 3 | 8.24677615 | 7.870092223 |
2009 | 4 | 8.273794125 | 7.896420252 |
2010 | 1 | 8.234644322 | 7.852717041 |
2010 | 2 | 8.265770014 | 7.883548874 |
2010 | 3 | 8.276412771 | 7.887272797 |
2010 | 4 | 8.296775172 | 7.920745407 |
2011 | 1 | 8.258488263 | 7.876671098 |
2011 | 2 | 8.28025062 | 7.904861835 |
2011 | 3 | 8.286445048 | 7.903693071 |
2011 | 4 | 8.310299641 | 7.93417494 |
2012 | 1 | 8.28807206 | 7.896673974 |
2012 | 2 | 8.302246702 | 7.919464185 |
2012 | 3 | 8.310429505 | 7.914470457 |
2012 | 4 | 8.32406101 | 7.948595436 |
2013 | 1 | 8.295481864 | 7.901073716 |
2013 | 2 | 8.316074062 | 7.930162321 |
2013 | 3 | 8.331764171 | 7.9315535 |
2013 | 4 | 8.353926453 | 7.97351891 |
2014 | 1 | 8.313740987 | 7.920852162 |
2014 | 2 | 8.340411382 | 7.957549803 |
2014 | 3 | 8.360010119 | 7.96363646 |
2014 | 4 | 8.379707706 | 8.009244426 |
2015 | 1 | 8.346591093 | 7.960388898 |
2015 | 2 | 8.377084329 | 7.997674559 |
2015 | 3 | 8.386560907 | 8.000297515 |
2015 | 4 | 8.397710086 | 8.039418324 |
2016 | 1 | 8.36214651 | 7.989860404 |
2016 | 2 | 8.389897176 | 8.023711292 |
2016 | 3 | 8.400298861 | 8.026028355 |
2016 | 4 | 8.417715339 | 8.06629893 |
2017 | 1 | 8.375307455 | 8.011115611 |
2017 | 2 | 8.411973999 | 8.050637372 |
2017 | 3 | 8.425534596 | 8.050759821 |
2017 | 4 | 8.444487436 | 8.093293608 |
2018 | 1 | 8.40805399 | 8.03838322 |
2018 | 2 | 8.44206784 | 8.077521347 |
2018 | 3 | 8.452242212 | 8.079670073 |
2018 | 4 | 8.473009929 | 8.12039928 |
The dataset “US macro data”.xls contains quarterly time series data for the US for the period Q1 2002 to Q4 2018. Here Q1 20xx stands for the first quarter of the year 20xx, and so on. The variables are:
Year: 20xx
Quarter: 1 to 4
lgdp: logarithm of US Real Gross Domestic Product, Billions of Chained 2012 Dollars, Quarterly, Not Seasonally Adjusted
lcons: logarithm of US Real Personal Consumption Expenditures, Billions of Chained 2012 Dollars, Quarterly, Not Seasonally Adjusted
(Source: Federal Reserve Bank of St Louis)
In taking logarithms of output and consumption, a Federal Reserve economist Anne wrote:
“For several reasons, taking logarithms of macroeconomic aggregates is a good idea. First, it makes the distributions much closer to the normal distribution, and hence t‐ and F‐statistics are better applicable. Second, a log‐log regression allows interpreting the regression coefficient as an elasticity, which is a very useful and intuitive concept in economics and allied disciplines. Even a regression where the dependent variable is in logarithms but the regressor is not provides interpretation in terms of growth rates. Third, and finally, first difference in logarithms is very close to growth rates, and this offers nice interpretation as well.”
Critically discuss the above statement. If there’s excel formula, include them as well.