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ESRC Conference on Diversity in Macroeconomics Behavioral Macroeconomics

ESRC Conference on Diversity in Macroeconomics Behavioral Macroeconomics. Paul De Grauwe London School of Economics. Introduction: Some facts. Let us first look at some facts US output gap movements during last 50 years. Source: US Department of Commerce and Congressional Budget Office.

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ESRC Conference on Diversity in Macroeconomics Behavioral Macroeconomics

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  1. ESRC Conference on Diversity in Macroeconomics Behavioral Macroeconomics Paul De Grauwe London School of Economics

  2. Introduction: Some facts • Let us first look at some facts • US output gap movements during last 50 years

  3. Source: US Department of Commerce and Congressional Budget Office

  4. Frequency distribution of US Output gap (1960-2009)

  5. The same regularity for the output gap has been analysed by Fagiolo, et al. (2008) and (2009). • These authors also confirm that output growth rates in most OECD-countries are non-normally distributed, with tails that are much fatter than those in a Gaussian distribution. • Note also high autocorrelation coefficient in previous data: 0.94

  6. Non-normality of distribution output gap and output growth exhibiting excess kurtosis and fat tails is an important property of the dynamics of the business cycle. • It implies that business cycle movements are characterized by periods of relatively small changes in output interrupted by (infrequent) periods of large changes.

  7. Thus much of the time tranquillity reigns followed (unpredictably) by bursts of booms and busts. • The financial and economic crisis of 2007-09 was preceded by a period of tranquillity that was characterized as a period of “Great Moderation”.

  8. Mainstream DSGE-models have been struggling to provide a good explanation. • In these models the existence of occasionally large booms and busts is explained by the occurrence of large exogenous shocks (“hurricanes”). • This is not a very attractive theory.

  9. A satisfactory macroeconomic theory should try to explain the occurrence of non-normality in the movements in output from within the theory. • This is what I attempted to do using a behavioural macroeconomic model in which endogenously generated “animal spirits” take centre stage.

  10. The model: structure is the same in behavioral model and in DSGE Aggregate demand Forward and backward looking term (habit formation) ^ above E means: non rational expectation

  11. Aggregate supply: New Keynesian Phillips curve Taylor rule describes behavior of central bank when c2 = 0 there is strict inflation target

  12. Introducing heuristics: output forecasting I assume two possible forecasting rules A fundamentalist rule An extrapolative rule Fundamentalist rule: agents estimate equilibrium output gap and forecast output gap to return to steady state Extrapolative rule: agents extrapolate past output gap

  13. Introducing discipline Agents continuously evaluate their forecast performance. I apply notions of discrete choice theory (see Brock & Hommes(1997)) in specifying the procedure agents follow in this evaluation process They switch to the forecasting rule that performs better

  14. This switching mechanism is the disciplining device introduced in this model on the kind of rules of behaviour that are acceptable. Only those rules that pass the fitness test remain in place. The others are weeded out.

  15. Calibrating the model I calibrate the model by giving numerical values to the parameters that are often found in the literature And simulate it assuming i.i.d. shocks with std deviations of 0.5%

  16. Output gap • strong cyclical movements in the output gap. • the source of these cyclical movements is the fraction of those who forecast positive output gaps (optimists) • The model generates endogenous waves of optimism and pessimism • Keynes’“animal spirits” • Timing is unpredictable • Optimism and pessimism self-fulfilling • Correlation output gap and fraction optimists = 0.86

  17. Correlation animal spirits and output gap • I find a correlation coefficient between fraction of optimists and output gap in a range of 0.8-0.9 • This correlation depends on a number of parameters

  18. The World is non-normal • This behavioural model is capable of mimicking empirical regularities? • First finding: strong autocorrelation output gap, i.e. = 0.95 • Second finding: output gap non-normally distributed (despite the fact that shocks are normally distributed)

  19. Animal spirits produce non-normally distributed movements in output Kurtosis= 6.1

  20. Non-normality created by animal spirits

  21. Two different business cycle theories • In standard DSGE-model fat tails (booms and busts) are result of large exogenous shocks • Non-normality comes from outside the macroeconomy

  22. In behavioral model booms and busts are endogenously generated. • At irregular intervals the economy is gripped by either a wave of optimism or of pessimism. • The nature of these waves is that beliefs get correlated. Optimism breeds optimism; pessimism breeds pessimism. • These periods are characterized by large positive of negative movements in the output gap (booms and busts).

  23. Extensions: a banking sector • Banks intermediate between savers and investors • Investors demand loans and present collateral • Asset prices affect value of collateral and thus banks’ balance sheets • Predictions of asset prices driven by similar animal spirits

  24. In such a system banks amplify booms and busts by creating credit cycles • They do not create these booms and busts

  25. Monetary PolicyThe role of output stabilization • In order to analyze the role of stabilization in behavioral model I construct tradeoffs • The model was simulated 10,000 times and the average output and inflation variabilities were computed for different values of the Taylor rule parameters. • We first show how output variability and inflation variability change as we increase the output coefficient (c2) in the Taylor rule from 0 to 1. • Thus, when c2 increases central bank becomes increasingly active in stabilizing output (inflation targeting becomes less strict)

  26. Each line represents the outcome for different values of the inflation coefficient (c1) in the Taylor rule. Left panel exhibits the expected result, i.e. as the output coefficient increases (inflation targeting becomes less strict) output variability tends to decrease. Right panel is surprising. We observe that the relationship is non-linear. As the output parameter is increased from zero, inflation variability first declines and then increases.

  27. Thus the central bank can reduce both output and inflation variability when it moves away from strict inflation targeting (c2=0) and engages in some output stabilization. Too much stabilization is not good though. Too much output stabilization turns around the relationship and increases inflation variability.

  28. The trade-off Take the tradeoff AB. In point A, the output parameter c2=0 (strict inflation targeting). As output stabilization increases we first move downwards. Thus increased output stabilization by the central bank reduces output and inflation variability. The relation is non-linear, however. At some point, with too high an output stabilization parameter, the tradeoff curve starts increasing, becoming a “normal” tradeoff, A B B A

  29. How can we interpret these results? • When there is no attempt at stabilizing output at all we obtain large movements in output • These lead to stronger waves in optimism and pessimism • which in turn leads to high inflation variability • Thus some output stabilization is good because it also leads to less inflation variability • Not too much though

  30. Too much output stabilization reduces the stabilization bonus provided by a credible inflation target. • When the central banks attaches too much importance to output stabilization it creates more scope for better forecasting performance of the inflation extrapolators, leading to more inflation variability.

  31. Note that increasing the inflation parameter in the Taylor rule has the effect of shifting the tradeoffs downwards, • i.e. the central bank can improve the tradeoffs by reacting more strongly to changes in inflation • Reason: probability extrapolators take over is reduced • Credibility is enhanced • Credibility creates strong stability bonus

  32. The credibility of inflation targeting • There is relation between credibility of inflation targeting and the parameters c1 and c2 • Credibility can be given precise meaning in behavioral model • We define it as the fraction of agents using the announced inflation target to forecast inflation

  33. Some output stabilization enhances inflation credibility C2 should be between 0.5 and 1 Econometric evidence shows that this is the typical value central banks apply Central banks seem to apply a degree of output stabilization that is consistent with our theory of animal spirits

  34. Policy implications • Inflation targeting is necessary to stabilize the economy • It is not sufficient though • Central bank must also explicitly care for output stabilization • So as to reduce the ups and downs produced by excessive optimism and excessive pessimism

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