Real-time supervision of shifts in inflation expectations is an important issue for monetary policy makers, especially in the presence of economic uncertainty. In this paper, we elaborate tools for on-line monitoring of such shifts by extracting valuable information from noisy daily financial market data. For this purpose, first, we suggest a new risk adjustment for observable proxies of medium and long run inflation expectations assuming that the latter are well-anchored. Second, we propose an econometric methodology for sequential monitoring of level changes in the associated proxies at daily frequency. Our empirical evidence shows that the on-line surveillance of risk adjusted US forward breakeven inflation rates by means of the cumulative sum (CUSUM) detector appears to be helpful to extract timely signals on potential shifts. In particular, the obtained signals indicate important turning points in market-based measures of inflation expectations, which also tend to materialize in lower frequency experts' surveys.

This paper explores the interplay between credit market development and human capital accumulation in a two-period overlapping-generations economy with asymmetric information under the assumption that young lenders channel credits to young borrowers and acquire education. We find that, at the self-selection equilibrium, lenders will allocate more time to acquire education if the cost of screening borrowers falls. Furthermore, a longer duration of lenders' schooling time suppresses borrowers' incentive to cheat thereby enabling lenders to screen less frequently. Our preliminary cross-country empirical analysis appears to support these findings.

This paper explores single-equation nonlinear error correction (NEC) models with linear and nonlinear cointegrated variables. Within the class of semiparametric NEC models, we use smoothing splines. Within the class of parametric models, we discuss the interesting properties of cubic polynomial NEC models and we show how they can be used to identify unknown threshold points in asymmetric models and to check the stability properties of the long-run equilibrium. A new class of rational polynomial NEC models is also introduced. We found multiple long-run money demand equilibria. The stability observed in the money-demand parameter estimates during more than a century, 1878 to 2000, is remarkable.

The Forward Premium Puzzle is widely considered to indicate inefficiency in the foreign exchange market. This paper proposes a resolution of the puzzle using recursive least squares learning. Risk-neutral agents learn the unknown parameters, underlying the exchange rate generation process, using constant-gain recursive least squares. Simulations using plausible model parameter values replicate the anomaly along with other observed empirical features of the forward and spot exchange rate data. Estimates of parameter values from data support the model assumptions and justify the use of constant-gain learning. The conclusion is that the puzzle is not necessarily a reflection of inefficiency.

The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econometric protocols to take such changes into consideration. The introduction of ARMA (autoregressive moving average models) by Box and Jenkins (1970) led to the development of time-series econometrics, which had a major impact on the conceptual analysis of economic and financial data. This type of modeling offered a transition from a static setup to a new modeling process that reproduces the time-varying features of macroeconomic and financial series. However, the ARMA modeling system retains the constancy of the first and second moments, limits the phases of a cycle to symmetrical instances, and only reproduces the dynamics of stationary variables. It thus fails to adequately reproduce the nonstationary relationships between major economic and financial variables. Abrupt changes in economies and financial systems have given evidence of nonstationary series whose statistical properties are also time-varying, making it necessary to develop new econometric tools to capture the time variation of economic and financial series in the mean and in the variance, and to apprehend their dynamics in the short and long term. Among the most important and influential studies in the 1980s' econometrics literature were therefore those that dealt with the introduction of the ARCH (autoregressive conditional heteroskedasticity) model by Engle (1982) and the cointegration theory by Engle and Granger (1987). The ARCH model, which focuses on the time-varying features of volatility structure, was a major breakthrough, as it highlighted the importance of the second moment of time series, while the cointegration framework enabled the short- and long-term dynamics of nonstationary variables to be modeled.

This paper investigates the potential sources of the mixed evidence found in the empirical literature studying asymmetries in the response of output to monetary policy shocks of different magnitudes. Further, it argues that such mixed evidence is a consequence of the exogenous imposition of the threshold that classifies monetary shocks as small or large. To address this issue, I propose an unobserved-components model of output, augmented by a monetary policy variable, which allows the threshold to be endogenously estimated. The results show strong statistical evidence that the effect of monetary policy on output varies disproportionately with the size of the monetary shock once the threshold is estimated. Meanwhile, the estimates of the model are consistent with a key implication of menu-cost models: smaller monetary shocks trigger a larger response on output.

This paper emphasizes the use of data-driven techniques to discern trends in commodity prices. Whereas many previous papers rely on parametric assumptions, we take as our departure point the view that such trends are inherently nonparametric, driven by complex forces of innovation and obsolescence, competition and strategic behavior, resource depletion and discovery, and supply and demand. Our reference specification is the partial linear model yt = f(t) + ztβ + ϵt where macroeconomic variables z enter parametrically, and f is nonparametric, to be discovered using cross-validation.

We analyze data on 11 commodities—3 hydrocarbons and 8 metals. For the majority of these commodities, our data-driven estimates of f bear close similarity to band pass estimates which include long term trends and super cycles. The OPEC effect is estimated to have increased oil prices by over 50% on average and coal prices by about 25%. U.S. coal mining legislation is estimated to have increased coal prices by 9% to 15%.

Most real business cycle models have a hard time jointly explaining the twin facts of strongly procyclical Solow residuals and extremely low correlations between wages and hours. We present a model that delivers both these results without using exogenous variation in total factor productivity (technology shocks). The key innovation of the paper is to add learning-by-doing to firms' technology. As a result, firms optimally vary their prices to control the amount of learning, which in turn influences future productivity. We show that exogenous variation in labor wedges (preference shocks) measured from aggregate data deliver around 50% of the standard deviation in the efficiency wedge (Solow residual) as well as realistic second moments for key aggregate variables, which is in sharp contrast to the model without learning-by-doing.

This note demonstrates analytically that a persistent catastrophic shock on endowment growth, even if moderate, yields negative equity premiums when a representative agent is relatively prudent. In particular, it derives the minimum persistence necessary to have zero equity premiums.

This paper studies how model uncertainty influences economic fluctuation when trend inflation is high. We introduce Hansen and Sargent’s [(2008) Robustness, Princeton University Press] robust control techniques into a New Keynesian model with non-zero trend inflation. We reveal the following three points. First, we find that robust monetary policy responds more aggressively. This aggressiveness increases with trend inflation. Second, as the trend inflation rises, the response of macroeconomic variables is larger under robust policy. Third, stronger robustness tends to lead to indeterminate equilibrium as trend inflation increases. Consequently, the economy might be volatile when trend inflation is high due to robustness from the view of both variance and determinacy. We interpret the results as indicating that the model uncertainty might be the one of the factors causing large macroeconomic fluctuations when trend inflation is high.

Models of the exchange process based on search theory can be used to analyze the features of objects that make them more or less likely to emerge as money in equilibrium. These models illustrate the trade-off between endogenous acceptability (an equilibrium property) and intrinsic characteristics of goods, such as storability or recognizability. We look at how the relative supply and demand for various goods affect their likelihood of becoming money. Intuitively, goods in high demand and/or low supply are more likely to appear as commodity money, subject to the qualification that which object ends up circulating as a medium of exchange depends at least partly on convention. Welfare properties and fiat money are discussed.

Seasonal fluctuations are as large as cyclical fluctuations. Monetary policy in the United States has dealt with seasonality by smoothing nominal rates of interest. The original motivation for this was that seasonality in nominal interest rates put recurring strain on the banking system. We build a model of monetary policy in the presence of seasonality that puts financial market conditions in the foreground. Our findings are as follows. Insulating nominal rates of interest or rates of inflation from seasonal variation are the basis for a formula for generating indeterminacy of equilibria and excess volatility. Preventing seasonality in the rate of monetary growth does not suffer from this problem. Moreover, under any method for conducting monetary policy, the setting of the target value of the monetary instrument will affect the degree of seasonal variability in most endogenous variables. This is a new channel by which monetary policy can have real effects. The case for eliminating seasonality in nominal rates of interest is strongest when seasonal impulses derive from shifts in money demand. It is weakest when seasonal impulses derive from the real sector.

We propose a unified identification scheme to identify monetary shocks and track their propagation through the economy. We combine three approaches dealing with the consequences of monetary shocks. First, we adjust a state space version of the P-star type model employing money overhang as the driving force of inflation. Second, we identify the contemporaneous impact of monetary policy shocks by applying a sign restriction identification scheme to the reduced form given by the state space signal equations. Third, to ensure that our results are not distorted by the measurement error exhibited by the official monetary data, we employ the Divisia M4 monetary aggregate provided by the Center for Financial Stability. Our approach overcomes one of the major difficulties of previous models by using a data-driven identification of equilibrium velocity. Thus, we are able to show that a P-star model can fit U.S. data and money did indeed matter in the United States.

Considering macroeconomies as systems subject to stochastic forms of entropic equilibria, we shall consider how deviations driven by positive feedbacks as in a speculative bubble can drive such an economy into an anti-entropic state that can suddenly collapse back into an entropic state, with such a collapse taking the form of a Minsky moment. This can manifest itself as shifts in the boundary between the portion of the income distribution that is best modeled as Boltzmann–Gibbs and that best modeled as a Paretian power law.

We use a time-varying parameter dynamic factor model with stochastic volatility estimated using Bayesian methods to disentangle the relative importance of the common component in Federal Housing Finance Agency house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We find that the contribution of the national factor in explaining fluctuations in house prices is critical. We then use a Bayesian change-point vector autoregressive model that allows for different regimes throughout the sample period, to study the impact of aggregate supply, aggregate demand, (conventional) monetary policy, and term-spread shocks, identified based on sign restrictions on the national component of house price movements. While monetary policy and other shocks are found to be quite dominant early on, we find evidence that the national factor has been detached from the identified macroeconomic shocks since 2014, thus suggesting that a “national bubble” might be brewing again in the US housing market.

In a Markov switching framework, we show that the duration of recessions is significantly shorter than the duration of expansions in 11 manufacturing sectors, and in aggregate durables and manufacturing output. We find two leading indicators, consumer expectations and the term spread, act as important demand-driven forces behind asymmetry.

The choice between private and government provision of a productive public good like infrastructure (public capital) is examined in the context of an endogenously growing open economy. The accumulation of public capital need not require government provision, in contrast to the standard assumption in the literature. Even with an efficient government, the relative costs and benefits of government and private provision depend crucially on the economy's underlying structural conditions and borrowing constraints in international capital markets. Countries with limited substitution possibilities and large production externalities may benefit from governments encouraging private provision of public capital through targeted investment subsidies. By contrast, countries with flexible substitution possibilities and relatively smaller externalities may benefit either from governments directly providing public capital or from regulation of private providers. The transitional dynamics also are shown to depend on the underlying elasticity of substitution and the size of the production externality.

We use daily short-term interbank interest rates of France, the United Kingdom, and the United States to examine the dynamic links of international monetary markets from 2004 to 2009. Results from vector error-correction models and smooth-transition error-correction models show strong evidence of nonlinear and heterogeneous causalities between the three interest rates. We also find that changes in the U.S. interest rate deviations from the long-run equilibrium led those in France and in the United Kingdom by one to two days. Finally, the national interest rate nexus appears to converge in nonlinear fashion toward a steady state because it is subject to structural change beyond a certain rate threshold. Our findings have important implications for the actions of leading central banks (ECB, Bank of England, and U.S. Fed) because the joint behavior of short-term interest rates can be viewed as an indicator of the degree of central banks' policy interdependence.

Emerging economies that have adopted inflation targeting and that combine low credibility, high public debt, and a high interest rate suffer from a typical problem. Increases in the interest rate to reduce departures of inflation from the target imply that a higher primary surplus is required for stabilizing public debt/GDP ratio. This tricky situation is known as “unpleasant fiscal arithmetic” (UFA). This article develops a theoretical model showing how increased financial openness and capital account liberalization can mitigate UFA. Furthermore, empirical evidence from the Brazilian case through OLS, GMM, and GMM system methods is offered. The findings show that increases in capital mobility and financial openness work as a commitment technology, which contributes to the success of the inflation targeting and thus reduces the risk of UFA.

An asset market segmentation model is constructed to study the distributional effects of monetary policy when economic individuals can choose means of payment among alternatives. In equilibrium, monetary policy has two distributional effects: a direct effect and an indirect effect through the choice of means of payment. When the government injects money, some purchase a greater variety of goods with cash whereas others purchase a greater variety of goods with credit. Credit can dampen fluctuations in consumption arising from monetary policy. The optimal money growth rate can be positive or negative. The Friedman rule is not optimal in general.