New neoclassical synthesis

The new neoclassical synthesis (NNS), which is now generally referred to as New Keynesian economics, and occasionally as the New Consensus, is the fusion of the major, modern macroeconomic schools of thought – new classical macroeconomics/real business cycle theory and early New Keynesian economics – into a consensus view on the best way to explain short-run fluctuations in the economy. This new synthesis is analogous to the neoclassical synthesis that combined neoclassical economics with Keynesian macroeconomics. The new synthesis provides the theoretical foundation for much of contemporary mainstream macroeconomics. It is an important part of the theoretical foundation for the work done by the Federal Reserve and many other central banks. Prior to the synthesis, macroeconomics was split between partial-equilibrium New Keynesian work on market imperfections demonstrated with small models and new classical work on real business cycle theory that used fully specified general equilibrium models and used changes in technology to explain fluctuations in economic output. The new synthesis has taken elements from both schools, and is characterised by a consensus on acceptable methodology, the importance of empirical validation of theoretical work, and the effectiveness of monetary policy. Ellen McGrattan proposed a list of four elements that are central to the new synthesis described by Goodfried and King: intertemporal optimization, rational expectations, imperfect competition, and costly price adjustment (menu costs). Goodfriend and King also find that the consensus models produce certain policy implications. In contradiction with some new classical thought, monetary policy can affect real output in the short-run, but there is no long-run trade-off: money is not neutral in the short-run but it is in the long-run. High inflation and fluctuations in the inflation rate, have negative welfare effects. It is important for central banks to maintain credibility through rules based policy like inflation targeting.