1. Taking the theory to the data: A proposal Romer: Advanced Macroeconomics, Chapter 9: Inflation and Monetary Policy ‘Specific to General’ versus ‘General to Specific’ The role of ceteris paribus in a theory model versus in an empirical model

2. What causes inflation? Many potential causes: Shocks shifting the AD curve to the right or the AS curve to the left leads to higher prices

3. Inflation and money growth Endogenous, exogenous variables? Ceteris paribus assumptions? Equlilibrium in the money market: Inverting the equilibrium money relation: Deviations from long-run benchmark levels:

4. Deviations from a long-run money demand relation Excess money measured as: m-p-y-13(Rm-Rb)

5. Examples of empirical questions

6. Time dependence of macro data Stationary variables with a short time dependence, i.e. a low degree of time persistence, transitory effcts Nonstationary variables with long time dependence, i.e. high degree of time persistence, permanent effects. Distinguish between:

7. What is the long run? Unit root as a local approximation of persistent behavior !

9. Stationary and nonstationary movements in the data stationary movements have a short memory (transitory components) nonstationary movements have a long memory (persistent components) cointegration between nonstationary variables eliminate the nonstationarity. A cointegration relation can often be interpreted as a long-run economic relation distinguish between short-run and long-run structures in the data

10. How do we measure long-term movements in the data? Deterministic trends (usually linear) Stochastic trends (persistent movements in the data A combination of the two

11. A stochastic formulation What is the meaning of a stochastic trend and a stochastic long cycle?

12. Illustrative example of how to measure a stochastic trend

13. Measuring a stochastic trend

14. The stochastic trend in inflation

15. The increments of a deterministic trend are constant over time The increments of a stochastic trend are random over time First and second order stochastic trends

18. Are prices I(1) or I(2)?

19. The I(2) Scenario Defining autonomous shocks Theoretically Empirically Shocks shifting the AD curve Shocks shifting the AS curve

20. Conditions for long-run price homogeneity

21. Assuming price homogeneity

24. A theory consistent scenario Inflation I(1) ?

25. Why does nonstationarity matter for the statistical modelling? Standard statistical inference is based on stationarity. Some new inferences is needed, but by transforming the data into differences and cointegration relations stationarity is recovered. Pulling and pushing forces

26. Why does nonstationarity matter for the economic modelling? Most economic models are developed for a stationary world The role of the ceteris paribus assumption The role of expectations –Model based rational expectations –Imperfect knowledge expectations

27. I had the great good fortune in the 1960s to initiate the profession’s work on plausible microfoundations for macroeconomic modeling taking into account the knowledge and the information that the micro actors could reasonably be supposed to have – truly a revolutionary movement. Unfortunately, the rational expectations models appearing in the 1970s sidestepped the problem of expectations formation under uncertainty by blithely supposing that the model’s actors (tellingly dubbed “agents”) knew the “correct” model and the correct model was the analyst’s model – whatever that model might be that day. The stampede toward “rational expectations,” referred to as a “revolution” though it was only a generalization of the neoclassical idea of equilibrium, derailed the movement I initiated. In the end, this way of modeling has not illuminated how the world economy works. Happily for me and I believe for the profession of economics, this book gives signs of bringing us back on track – on a road toward an economics possessing a genuine microfoundation and at the same time a capacity to illuminate some of the many aspects of the modern economy that the rational expectations approach cannot by its nature explain.

28. The I(1) scenario