Lectures on Behavioral Macroeconomics - Hardcover

De Grauwe, Paul

Hardcover

ISBN 10: 0691147396 ISBN 13: 9780691147390

Publisher: Princeton University Press, 2012

Synopsis

In mainstream economics, and particularly in New Keynesian macroeconomics, the booms and busts that characterize capitalism arise because of large external shocks. The combination of these shocks and the slow adjustments of wages and prices by rational agents leads to cyclical movements. In this book, Paul De Grauwe argues for a different macroeconomics model--one that works with an internal explanation of the business cycle and factors in agents' limited cognitive abilities. By creating a behavioral model that is not dependent on the prevailing concept of rationality, De Grauwe is better able to explain the fluctuations of economic activity that are an endemic feature of market economies. This new approach illustrates a richer macroeconomic dynamic that provides for a better understanding of fluctuations in output and inflation. De Grauwe shows that the behavioral model is driven by self-fulfilling waves of optimism and pessimism, or animal spirits. Booms and busts in economic activity are therefore natural outcomes of a behavioral model. The author uses this to analyze central issues in monetary policies, such as output stabilization, before extending his investigation into asset markets and more sophisticated forecasting rules. He also examines how well the theoretical predictions of the behavioral model perform when confronted with empirical data. * Develops a behavioral macroeconomic model that assumes agents have limited cognitive abilities * Shows how booms and busts are characteristic of market economies * Explores the larger role of the central bank in the behavioral model * Examines the destabilizing aspects of asset markets

About this title

About the Author

Paul De Grauwe is professor of international economics at the London School of Economics and Political Science. He is the author or coauthor of several books, including The Exchange Rate in a Behavioral Finance Framework (Princeton) and Economics of Monetary Union.

From the Back Cover

"De Grauwe voices the concerns of many macroeconomists regarding the empirical plausibility of the rational expectations assumption. He shows how a parsimonious, boundedly rational approach can improve the fit of sticky price macro models to the data in a number of important dimensions."--John Duffy, University of Pittsburgh

From the Inside Flap

"De Grauwe voices the concerns of many macroeconomists regarding the empirical plausibility of the rational expectations assumption. He shows how a parsimonious, boundedly rational approach can improve the fit of sticky price macro models to the data in a number of important dimensions."--John Duffy, University of Pittsburgh

Excerpt. © Reprinted by permission. All rights reserved.

Lectures on Behavioral Macroeconomics

By Paul De Grauwe

Princeton University Press

Copyright © 2012 Princeton University Press
All right reserved.
ISBN: 978-0-691-14739-0

Contents

Preface....................................................................................vii1 A Behavioral Macroeconomic Model.........................................................11.1 Introduction...........................................................................11.2 The Model..............................................................................31.3 Introducing Heuristics in Forecasting Output...........................................51.4 Heuristics and Selection Mechanism in Forecasting Inflation............................101.5 Solving the Model......................................................................111.6 Animal Spirits, Learning, and Forgetfulness............................................121.7 Conditions for Animal Spirits to Arise.................................................161.8 Two Different Business Cycle Theories: Behavioral Model................................191.9 Two Different Business Cycle Theories: New Keynesian Model.............................201.10 Uncertainty and Risk..................................................................251.11 Credibility of Inflation Targeting and Animal Spirits.................................271.12 Different Types of Inertia............................................................311.13 Animal Spirits in the Macroeconomic Literature........................................331.14 Conclusion............................................................................34Appendix 1: Parameter Values of the Calibrated Model.......................................36Appendix 2: Matlab Code for the Behavioral Model...........................................37Appendix 3: Some Thoughts on Methodology in Mainstream Macroeconomics......................402 The Transmission of Shocks...............................................................452.1 Introduction...........................................................................452.2 The Transmission of a Positive Productivity Shock......................................452.3 The Transmission of Interest Rate Shocks...............................................492.4 Fiscal Policy Multipliers: How Much DoWe Know?.........................................522.5 Transmission under Perfect Credibility of Inflation Target.............................553 Trade-offs between Output and Inflation Variability......................................593.1 Introduction...........................................................................593.2 Constructing Trade-offs................................................................603.3 Trade-offs in the New Keynesian Rational Expectations (DSGE) Model.....................643.4 The Merits of Strict Inflation Targeting...............................................654 Flexibility, Animal Spirits, and Stabilization...........................................714.1 Introduction...........................................................................714.2 Flexibility and Neutrality of Money....................................................714.3 Flexibility and Stabilization..........................................................745 Animal Spirits and the Nature of Macroeconomic Shocks....................................795.1 Introduction...........................................................................795.2 The Model with Only Supply or Demand Shocks............................................795.3 Trade-offs in the Supply-Shocks-Only Scenario..........................................835.4 Trade-offs in the Demand-Shocks-Only Scenario..........................................855.5 Conclusion.............................................................................886 Stock Prices and Monetary Policy.........................................................916.1 Introduction...........................................................................916.2 Introducing Asset Prices in the Behavioral Model.......................................916.3 Simulating the Model...................................................................946.4 Should the Central Bank Care about Stock Prices?.......................................966.5 Inflation Targeting and Macroeconomic Stability........................................996.6 The Trade-off between Output and Inflation Variability.................................1016.7 Conclusion.............................................................................1057 Extensions of the Basic Model............................................................1077.1 Fundamentalists Are Biased.............................................................1077.2 Shocks and Trade-offs..................................................................1117.3 Further Extensions of the Basic Model..................................................1147.4 Conclusion.............................................................................1148 Empirical Issues.........................................................................1178.1 Introduction...........................................................................1178.2 The Correlation of Output Movements and Animal Spirits.................................1188.3 Model Predictions: Higher Moments......................................................1208.4 Transmission of Monetary Policy Shocks.................................................1228.5 Conclusion.............................................................................124References.................................................................................127Index......................................................................................133

Chapter One

A Behavioral Macroeconomic Model

1.1 Introduction

Capitalism is characterized by booms and busts, by periods of strong growth in output followed by periods of declines in economic growth. Every macroeconomic theory should attempt at explaining these endemic business cycle movements.

Before developing the behavioral model it is useful to present some stylized facts about the cyclical movements of output. In figure 1.1 I show the strong cyclical movements of the output gap in the United States since 1960. These cyclical movements imply that there is strong autocorrelation in the output gap numbers, i.e., the output gap in period t is strongly correlated with the output gap in period t - 1. The intuition is that if there are cyclical movements we will observe clustering of good and bad times. A positive (negative) output gap is likely to be followed by a positive (negative) output gap in the next period. This is what we find for the U.S. output gap over the period 1960–2009: the autocorrelation coefficient is 0.94. Similar autocorrelation coefficients are found in other countries.

A second stylized fact about the movements in the output gap is that these are not normally distributed. The evidence for the U.S. is shown in figure 1.2. We find, first, that there is excess kurtosis (kurtosis = 3.62), which means that there is too much concentration of observations around the mean to be consistent with a normal distribution. Second, we find that there are fat tails, i.e., there are more large movements in the output gap than is compatible with the normal distribution. This implies that the business cycle movements are characterized by periods of tranquility interrupted by large positive and negative movements in output, in other words, booms and busts. This also means that if we were basing our forecasts on the normal distribution we would underestimate the probability that in any one period a large increase or decrease in the output gap can occur. Finally, the Jarque–Bera test leads to a formal rejection of normality of the movements in the U.S. output gap series.

The same empirical features have been found in other OECD countries (see Fagiolo et al. 2008, 2009). These authors also confirm that output growth rates in most OECD countries are nonnormally distributed, with tails that are much fatter than those in a normal distribution. In the empirical chapter 8 additional evidence is provided for other industrialized countries illustrating the same empirical regularities observed for the United States.

One of the purposes of this chapter is to explain this boom and bust characteristic of movements of the business cycle. We will also want to contrast the explanation provided by the behavioral model with the one provided by the mainstream macroeconomic model, which is based on rational expectations.

1.2 The Model

I will use a standard macroeconomic model that in its basic structure is the same as the mainstream new Keynesian model as described in, for example, Galí (2008). In this section I describe this model. In the next I introduce the behavioral assumptions underlying the way agents make forecasts.

The model consists of an aggregate demand equation, an aggregate supply equation, and a Taylor rule.

The aggregate demand equation is specified in the standard way, i.e.,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.1)

where y_t is the output gap in period t, r_t is the nominal interest rate, π_t is the rate of inflation, and ε_t is a white noise disturbance term. [??]^t is the expectations operator, where the tilde symbol refers to expectations that are not formed rationally. This process will be specified subsequently. I follow the procedure introduced in new Keynesian macroeconomic models of adding a lagged output in the demand equation (see Galí 2008; Woodford 2003). This is usually justified by invoking habit formation. I keep this assumption here as I want to compare the behavioral model with the new Keynesian rational expectations model. However, I will later show that I do not really need this inertia-building device to generate inertia in the endogenous variables.

The aggregate demand equation has a very simple interpretation. Utility-maximizing agents will want to spend more on goods and services today when they expect future income (output gap) to increase and to spend less when the real interest rate increases.

The aggregate supply equation is derived from profit maximization of individual producers (see Galí 2008, chapter 3). In addition, it is assumed that producers cannot adjust their prices instantaneously. Instead, for institutional reasons, they have to wait to adjust their prices. The most popular specification of this price-adjustment mechanism is the Calvo pricing mechanism (Calvo 1983; for a criticism see McCallum 2005). This assumes that in period t a fraction of prices remains unchanged. Under those conditions the aggregate supply equation (which is often referred to as the new Keynesian Philips curve) can be derived as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (1.2)

The previous two equations determine the two endogenous variables—inflation and output gap—given the nominal interest rate. The model has to be closed by specifying the way the nominal interest rate is determined. The most popular way to do this has been to invoke the Taylor rule (see Taylor 1993). This rule describes the behavior of the central bank. It is usually written as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.3)

where π^* is the inflation target. Thus the central bank is assumed to raise the interest rate when the observed inflation rate increases relative to the announced inflation target. The intensity with which it does this is measured by the coefficient c₁. Similarly, when the output gap increases the central bank is assumed to raise the interest rate. The intensity with which it does this is measured by c₂. The latter parameter then also tells us something about the ambitions the central bank has to stabilize output. A central bank that does not care about output stabilization sets c₂ = 0. We say that this central bank applies strict inflation targeting. Finally, note that, as is commonly done, the central bank is assumed to smooth the interest rate. This smoothing behavior is represented by the lagged interest rate in equation (1.3).

The parameter c₁ is important. It has been shown (see Woodford 2003, chapter 4; Galí 2008) that it must exceed 1 for the model to be stable. This is also sometimes called the "Taylor principle."

Ideally, the Taylor rule should be formulated using a forward-looking inflation variable, i.e., central banks set the interest rate on the basis of their forecasts about the rate of inflation. This is not done here in order to maintain simplicity in the model (again see Woodford 2003, p. 257).

It should also be mentioned that another approach to describing the monetary policy of the central bank is to start from a minimization of the loss function and to derive the optimal response of the central bank from this minimization process (see Woodford 2003). This has not been attempted here.

We have added error terms in each of the three equations. These error terms describe the nature of the different shocks that can hit the economy. There are demand shocks, ε_t, supply shocks, η_t, and interest rate shocks, u_t. We will generally assume that these shocks are normally distributed with mean zero and a constant standard deviation. Agents with rational expectations are assumed to know the distribution of these shocks. It will turn out that this is quite a crucial assumption.

The model consisting of equations (1.1)–(1.3) can be solved under rational expectations. This will be done in Section 1.9. I will call this new Keynesian model with rational expectations the "mainstream model" to contrast it with our behavioral model. I will also occasionally refer to the DSGE model, i.e., the dynamic stochastic general equilibrium model, which has the same features, i.e., new Keynesian wage and price rigidities coupled to rational expectations. In the following sections I specify the assumptions that underlie the forecasting of output and inflation in the behavioral model.

1.3 Introducing Heuristics in Forecasting Output

In the world of rational expectations that forms the basis of the mainstream model, agents are assumed to understand the complexities of the world. In contrast, we take the view that agents have cognitive limitations. They only understand tiny little bits of the world. In such a world agents are likely to use simple rules, heuristics, to forecast the future (see, for example, Damasio 2003; Kahneman 2002; Camerer et al. 2005). In this chapter, a simple heuristic will be assumed. In a later chapter (chapter 5) other rules are introduced. This will be done to study how more complexity in the heuristics affects the results.

Agents who use simple rules of behavior are no fools. They use simple rules only because the real world is too complex to understand, but they are willing to learn from their mistakes, i.e., they regularly subject the rules they use to some criterion of success. There are essentially two ways this can be done. The first one is called statistical learning. It has been pioneered by Sargent (1993) and Evans and Honkapohja (2001). It consists in assuming that agents learn like econometricians do. They estimate a regression equation explaining the variable to be forecasted by a number of exogenous variables. This equation is then used to make forecasts. When new data become available the equation is re-estimated. Thus each time new information becomes available the forecasting rule is updated. The statistical learning literature leads to important new insights (see, for example, Bullard and Mitra 2002; Gaspar et al. 2006; Orphanides and Williams 2004; Milani 2007a; Branch and Evans 2011). However, this approach loads individual agents with a lot of cognitive skills that they may or may not have. I will instead use another learning strategy that can be called "trial-and-error" learning. It is also often labeled "adaptive learning." I will use both labels as synonyms.

Adaptive learning is a procedure whereby agents use simple forecasting rules and then subject these rules to a "fitness" test, i.e., agents endogenously select the forecasting rules that have delivered the highest performance (fitness) in the past. Thus, an agent will start using one particular rule. She will regularly evaluate this rule against the alternative rules. If the former rule performs well, she keeps it. If not, she switches to another rule. In this sense the rule can be called a trial-and-error rule.

This trial-and-error selection mechanism acts as a disciplining device on the kind of rules that are acceptable. Not every rule is acceptable. It has to perform well. What that means will be made clear later. It is important to have such a disciplining device, otherwise everything becomes possible. The need to discipline the forecasting rule was also one of the basic justifications underlying rational expectations. By imposing the condition that forecasts must be consistent with the underlying model, the model builder severely limits the rules that agents can use to make forecasts. The adaptive selection mechanism used here plays a similar disciplining role.

There is another important implication of using trial-and-error rules that contrasts a great deal with the rational expectations forecasting rule. Rational expectations implies that agents understand the complex structure of the underlying model. Since there is only one underlying model (there is only one "Truth"), agents understand the same "Truth." They all make exactly the same forecast. This allows builders of rational expectations models to focus on just one "representative agent." In the adaptive learning mechanism that will be used here, this will not be possible because agents can use different forecasting rules. Thus there will be heterogeneity among agents. This is an important feature of the model because, as will be seen, this heterogeneity creates interactions between agents. These interactions ensure that agents influence each other, leading to a dynamics that is absent from rational expectations models.

Agents are assumed to use simple rules (heuristics) to forecast the future output and inflation. The way I proceed is as follows. I assume two types of forecasting rules. A first rule can be called a "fundamentalist" one. Agents estimate the steady-state value of the output gap (which is normalized at 0) and use this to forecast the future output gap. (In a later extension in chapter 7, it will be assumed that agents do not know the steady-state output gap with certainty and only have biased estimates of it.) A second forecasting rule is an "extrapolative" one. This is a rule that does not presuppose that agents know the steady-state output gap. They are agnostic about it. Instead, they extrapolate the previous observed output gap into the future.

The two rules are specified as follows.

(i) The fundamentalist rule is defined by

[??]^f_t y_{t+1 = 0. (1.4)}

(ii) The extrapolative rule is defined by

[??]^e_t y_{t+1 = yt-1. (1.5)}

This kind of simple heuristic has often been used in the behavioral finance literature, where agents are assumed to use fundamentalist and chartist rules (see Brock and Hommes 1997; Branch and Evans 2006; De Grauwe and Grimaldi 2006). The rules are simple in the sense that they only require agents to use information they understand, and do not require them to understand the whole picture. Some experimental evidence in support of the two rules (1.4) and (1.5) for inflation forecasts in a new Keynesian model can be found in a paper by Pfajfar and Zakelj (2009).

Thus the specification of the heuristics in (1.4) and (1.5) should not be interpreted as a realistic representation of how agents forecast. Rather is it a parsimonious representation of aworld where agents do not knowthe "Truth" (i.e., the underlying model). The use of simple rules does not mean that the agents are dumb and that they do not want to learn from their errors. I will specify a learning mechanism later in this section in which these agents continually try to correct for their errors by switching from one rule to the other.

The market forecast is obtained as a weighted average of these two forecasts, i.e.,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.6)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.7)

and

α_f,t + α_e,t = 1, (1.8)

where α_f,t and α_e,t are the probabilities that agents use a fundamentalist and an extrapolative rule, respectively.

A methodological issue arises here. The forecasting rules (heuristics) introduced here are not derived at the micro-level and then aggregated. Instead, they are imposed ex post on the demand and supply equations. This has also been the approach in the learning literature pioneered by Evans and Honkapohja (2001). Ideally, one would like to derive the heuristics from the micro-level in an environment in which agents experience cognitive problems. Our knowledge about how to model this behavior at the micro-level and how to aggregate it is too sketchy, however. Psychologists and neuroscientists struggle to understand how our brains process information. There is as yet no generally accepted model we could use to model the micro-foundations of information processing in a world in which agents experience cognitive limitations. I have not tried to do so. In the appendix I return to some of the issues related to micro-founding of macroeconomic models.

Selecting the Forecasting Rules

As indicated earlier, agents in our model are not fools. They are willing to learn, i.e., they continually evaluate their forecast performance. This willingness to learn and to change one's behavior is the most fundamental definition of rational behavior. Thus our agents in the model are rational, just not in the sense of having rational expectations. We do not use this assumption here because it is an implausible assumption to make about the capacity of individuals to understand the world. Instead our agents are rational in the sense that they learn from their mistakes. The concept of "bounded rationality" is often used to characterize this behavior.

The first step in the analysis then consists in defining a criterion of success. This will be the forecast performance of a particular rule. Thus in this first step, agents compute the forecast performance of the two different forecasting rules as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.9)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1.10)

where U_f,t and U_e,t are the forecast performances (utilities) of the fundamentalist and extrapolating rules, respectively. These are defined as the mean squared forecasting errors (MSFEs) of the forecasting rules; ω_k are geometrically declining weights. We make these weights declining because we assume that agents tend to forget. Put differently, they give a lower weight to errors made far in the past as compared with errors made recently. The degree of forgetting will turn out to play a major role in our model. (Continues...)

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Edition details

• Publisher: Princeton University Press
• Publication date: 2012
• Language: English
• ISBN 10: 0691147396
• ISBN 13: 9780691147390
• Binding: Hardcover
• Number of pages: 152