Learning from Inflation Experiences--论文代写范文精选

个人形式对未来通胀的预期如何?这个问题的答案是中央重要的货币政策和个人的财务决策。政策制定者希望更好地改善他们的通胀预期。下面的paper代写范文进行阐述。

Abstract 
How do individuals form expectations about future inflation? We propose that personal experiences play an important role. Individuals adapt their forecasts to new data but overweight inflation realized during their life-times. Young individuals update their expectations more strongly in the direction of recent surprises than older individuals since recent experiences make up a larger part of their lives so far. We find support for these predictions using 57 years of microdata on inflation expectations from the Reuters/Michigan Survey of Consumers. Differences in life-time experiences strongly predict differences in subjective inflation expectations. Learning from experience explains the substantial disagreement between young and old individuals in periods of high surprise inflation, such as the 1970s. It also explains household borrowing and lending behavior, including the choice of fixed versus variable-rate investments and mortgages. The loss of distant memory implied by learning from experience provides a natural microfoundation for models of perpetual learning, such as constant-gain learning models.

Introduction 
How do individuals form expectations about future inflation? The answer to this question is of central importance both for monetary policy and for the financial decision-making of individuals. Policy makers would like to better understand the formation of inflation expectations in order to improve their inflation forecasts and the resulting policy choices (Bernanke (2007)). The financial decisions of individuals, e.g., in the housing market, are sensitive to their perception of real interest rates, which in turn are influenced by their perception of in- flation. 

Hence, inflation expectations influence current real expenditure and macroeconomic outcomes (e.g., Woodford (2003)). Despite a large volume of research, there is still little convergence on the best model to predict inflation expectations (see Mankiw, Reis, and Wolfers (2003); Blanchflower and Kelly (2008)). The “stickiness” of inflation rates (Sims (1998), Mankiw and Reis (2006)) and the empirical heterogeneity in the formation of expectations remain hard to reconcile with existing models. Consider the simple time-series plot of inflation expectations from the Reuters/Michigan Survey of Consumers (MSC) in Figure 1.1 The figure plots the expectations of young individuals (age below 40), middle-aged individuals (age 40 to 60), and older individuals (age above 60), expressed as deviations from the cross-sectional mean expectation in each period. 

The figure shows that the dispersion in beliefs between young and old individuals can be large, reaching almost 3 percentage points during the high-inflation years of the 1970s and early 1980s. The graph also shows repeated reversals in relative beliefs, with the old having lower inflation expectations than the young in the 1970s and 1980s, but higher inflation expectations in the late 1960s, mid-1990s, and late 2000s. These patterns are unexplained in existing models. Similar concerns apply to the formation of beliefs about other macro-economic variables and their influence on aggregate dynamics, as discussed, for example, in Fuster, Laibson, and Mendel (2010) and Fuster, Hebert, and Laibson (2011). 

In this paper, we argue that individuals’ personal experiences play an important role in shaping expectations that is absent from existing models. When forming macroeconomic expectations, individuals put a higher weight on realizations of macroeconomic data experienced during their life-times compared with other available historical data. As a result, learning dynamics are perpetual. Beliefs keep fluctuating and do not converge in the long-run, as weights on historical data diminish when old generations disappear and new generations emerge. Such learning from experience carries two central implications. First, expectations are history-dependent. 

Cohorts that have lived through periods of high inflation for a substantial amount of time have higher inflation expectations than individuals who have mostly experienced low inflation. Second, beliefs are heterogeneous. Young individuals place more weight on recent data than older individuals since recent experiences make up a larger part of their life-times so far. As a result, different generations tend to disagree about the future. Both effects have been noticed in practice. During the high-inflation period of the late 1970s, the Chairman of the Federal Reserve Paul Volcker remarked: “An entire generation of young adults has grown up since the mid-1960’s knowing only inflation, indeed an inflation that has seemed to accelerate inexorably. 

In the circumstances, it is hardly surprising that many citizens have begun to wonder whether it is realistic to anticipate a return to general price stability, and have begun to change their behavior accordingly.”2 Both effects are also visible in Figure 1: Following years of high inflation, young people expect much higher inflation going forward than older people. We estimate a learning-from-experience model using 57 years of microdata on inflation expectations from the MSC. 

In our empirical framework, we assume that individuals employ regression-based forecasting rules as in the adaptive learning literature, in particular Marcet and Sargent (1989), but with the twist that we allow individuals to overweigh data realized during their life-times so far. Specifically, individuals use inflation rates experienced in the past to recursively estimate an AR(1) model of inflation. Learning from experience is implemented by allowing the gain, i.e., the strength of updating in response to surprise inflation, to depend on age. Young individuals react more strongly to an inflation surprise than older individuals who have more data accumulated in their life-time histories. A gain parameter determines how fast these gains decrease with age as more data accumulates. We estimate the gain parameter empirically by fitting the learning rule to the cohort-level inflation expectations from the MSC. The availability of microdata is crucial for our estimation. (paper代写)

Our identification strategy exploits time-variation in the cross-sectional differences of inflation experiences between cohorts and relates it to time-variation in the cross-sectional differences of inflation expectations. This identification from cross-sectional heterogeneity allows us to include time dummies in our specifications to separate the experience effect from time trends or any other time-specific determinants. For example, individuals might also draw on the full inflation history available at a given time, or on published forecasts of professional forecasters. With the inclusion of time dummies, the experience coefficient isolates the incremental explanatory power of individual life-time experiences over and above such common time-specific factors. In other words, our empirical approach allows us to rule out that omitted macroeconomic variables or any other unobserved effects common to all individuals bias the estimation results. This is a key distinction from other models of belief formation, such as adaptive learning models, where parameters are fit to aggregate time-series of (mean or median) expectations.(paper代写)

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