Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

This paper uses an infinite hidden Markov model (IIHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and attractive alternative to existing methods. We found a clear structural break during the recent financial crisis. Prior to that, inflation persistence was high and fairly constant.

Notes on Agents’ Behavioral Rules Under Adaptive Learning and Studies of Monetary Policy

These notes try to clarify some discussions on the formulation of individual intertemporal behavior under adaptive learning in representative agent models. First, we discuss two suggested approaches and related issues in the context of a simple consumption-saving model. Second, we show that the analysis of learning in the NewKeynesian monetary policy model based on “Euler equations” provides a consistent and valid approach.

Monetary Policy and Heterogeneous Expectations

This paper studies the implications for monetary policy of heterogeneous expectations in a New Keynesian model. The assumption of rational expectations is replaced with parsimonious forecasting models where agents select between predictors that are underparameterized. In a Misspecification Equilibrium agents only select the best-performing statistical models. We demonstrate that, even when monetary policy rules satisfy the Taylor principle by adjusting nominal interest rates more than one for one with inflation, there may exist equilibria with Intrinsic Heterogeneity. Under certain conditions, there may exist multiple misspecification equilibria. We show that these findings have important implications for business cycle dynamics and for the design of monetary policy.

Fiscal Policy and Learning

Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature (in sharp contrast to the implausibly low values under RE). Effectiveness of fiscal policy is demonstrated during times of economic stress like the recent Great Recession. Finally it is shown how learning can lead to dynamics empirically documented during episodes of 'fiscal consolidations.'

Finite Horizon Learning

Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step-ahead perceived Euler equation; and infinite-horizon learning, in which consumption today is determined optimally from an infinite-horizon optimization problem with given beliefs. In our approach, agents hold a finite forecasting/planning horizon. We find for the Ramsey model that the unique rational expectations equilibrium is E-stable at all horizons. However, transitional dynamics can differ significantly depending upon the horizon.

Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock’s return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents’ estimates of risk.

Does Ricardian Equivalence Hold When Expectations are not Rational?

This paper considers the Ricardian Equivalence proposition when expectations are not rational and are instead formed using adaptive learning rules. We show that Ricardian Equivalence continues to hold provided suitable additional conditions on learning dynamics are satisfied. However, new cases of failure can also emerge under learning. In particular, for Ricardian Equivalence to obtain, agents’ expectations must not depend on government’s financial variables under deficit financing.

The Stagnation Regime of the New Keynesian Model and Current US Policy

In Evans, Guse, and Honkapohja (2008) the intended steady state is locally but not globally stable under adaptive learning, and unstable deflationary paths can arise after large pessimistic shocks to expectations. In the current paper a modified model is presented that includes a locally stable stagnation regime as a possible outcome arising from large expectation shocks. Policy implications are examined. Sufficiently large temporary increases in government spending can dislodge the economy from the stagnation regime and restore the natural stabilizing dynamics. More specific policy proposals are presented and discussed.

Anticipation, Learning and Welfare: the Case of Distortionary Taxation

We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labour supply, which makes agents. decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behaviour that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of .scal reforms. (JEL: E32, E62, D84)

The Distributional Consequences of Supply-Side Reforms in General Equilibrium

This paper addresses the issue on whether tax reforms consisten with lower public debt-to-GDP in the long-run can lead to a more efficient and equitable economy. To this end we solve a heterogeneous agent model comprised of a government, a representative capitalist and representative skilled and unskilled workers, under both rational expectations and adaptive learning. Our main ndings are that (i) reductions in capital taxation, while bene cial at the aggregate level, lead to increased inequality mainly due to the substitutability of un- skilled labour and capital; (ii) a fall in taxation for skilled labour is Pareto improving, which is largely explained by its complementarity with the other factor inputs; (iii) all agents would prefer increasing the tax rate on capital to increasing the tax rate on skilled and un- skilled labour since it leads to relatively lower welfare losses; and (iv) heterogeneity in initial beliefs under adaptive learning quantitatively matters for welfare.

The Dynamics of UK and US Inflation Expectations

This paper investigates the relationship between short term and long term in ation expectations in the US and the UK with a focus on iflation pass through (i.e. how changes in short term expectations affect long term expectations). An econometric methodology is used which allows us to uncover the relationship between in ation pass through and various explanatory variables. We relate our empirical results to theoretical models of anchored, contained and unmoored inflation expectations. For neither country do we find anchored or unmoored inflation expectations. For the US, contained inflation expectations are found. For the UK, our ndings are not consistent with the specifi =c model of contained inflation expectations presented here, but are consistent with a more broad view of expectations being constrained by the existence of an inflation target.

What is the Causal Effect of Information and Learning about a Public Good on Willingness to Pay?

In this study we elicit agents’ prior information set regarding a public good, exogenously give information treatments to survey respondents and subsequently elicit willingness to pay for the good and posterior information sets. The design of this field experiment allows us to perform theoretically motivated hypothesis testing between different updating rules: non-informative updating, Bayesian updating, and incomplete updating. We find causal evidence that agents imperfectly update their information sets. We also field causal evidence that the amount of additional information provided to subjects relative to their pre-existing information levels can affect stated WTP in ways consistent overload from too much learning. This result raises important (though familiar) issues for the use of stated preference methods in policy analysis.

Seigniorage-maximizing inflation

What is the seigniorage-maximizing level of inflation? Four models formulae for the seigniorage maximizing inflation rate (SMIR) are compared. Two sticky-price models arrive at very different quantitative recommendations although both predict somewhat lower SMIRs than Cagan’s formula and a variant of a .ex-price model due to Kimbrough (2006). The models differ markedly in how inflation distorts the labour market: The Calvo model implies that inflation and output are negatively related and that output is falling in price stickiness whilst the Rotemberg cost-of-price-adjustment model implies exactly the opposite. Interestingly, if our version of the Calvo model is to be believed, the level of inflation experienced recently in advanced economies such as the USA and the UK may be quite close to the SMIR.

When Do We Learn to Cooperate? The Role of Social Learning in Social Dilemmas

In this paper, I look at the interaction between social learning and cooperative behavior. I model this using a social dilemma game with publicly observed sequential actions and asymmetric information about pay offs. I find that some informed agents in this model act, individually and without collusion, to conceal the privately optimal action. Because the privately optimal action is socially costly the behavior of informed agents can lead to a Pareto improvement in a social dilemma. In my model I show that it is possible to get cooperative behavior if information is restricted to a small but non-zero proportion of the population. Moreover, such cooperative behavior occurs in a finite setting where it is public knowledge which agent will act last. The proportion of cooperative agents within the population can be made arbitrarily close to 1 by increasing the finite number of agents playing the game. Finally, I show that under a broad set of conditions that it is a Pareto improvement on a corner value, in the ex-ante welfare sense, for an interior proportion of the population to be informed.

Baysian Model Averaging, Learning and Model Selection

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.