Is There a Positive Intertemporal Tradeoff Between Risk and Return After All?

This paper develops an extended version of Turner, Startz, and Nelson's (1989) Markov-switching model of stock returns. The model is motivated as an alternative version of Campbell and Hentschel's (1992) volatility feedback model, with news about future dividends subject to a two-state Markov-switching variance. We are able to identify an endogenous volatility feedback effect by assuming that economic agents acquire information about market volatility that is not directly available to econometricians. Using this model, we find strong evidence for a positive tradeoff between volatility and the equity risk premium, especially for post-War stock returns.

Inventory Mistakes and the Great Moderation

Why did the volatility of U.S. real GDP decline by more than the volatility of final sales with the Great Moderation in the mid-1980s? One possible explanation is that firms shifted their inventory behaviour towards a greater emphasis on production smoothing. We investigate the role of inventories in the Great Moderation by estimating an unobserved components model that identifies inventory and sales shocks and their propagation in the aggregate data. Our findings suggest little evidence of increased production smoothing. Instead, a reduction in inventory mistakes explains the excess volatility reduction in output relative to sales. The inventory mistakes are informational errors related to production that must be set in advance and their reduction also helps to explain the changed forecasting role of inventories since the mid-1980s. Our findings provide an optimistic prognosis for the continuation of the Great Moderation despite the dramatic movements in output during the recent economic crisis.inventories; unobserved components model; inventory mistakes; production smoothing; Great Moderation

The Adjustment of Prices and the Adjustment of the Exchange Rate

The purchasing power parity puzzle relates to the adjustment of real exchange rates. Real exchange rates are extremely volatile, suggesting that temporary shocks emanate from the monetary sector. But the half-life of real exchange rate deviations is extremely large -- 2.5 to 5 years. This half-life seems too large to be explained by the slow adjustment of nominal prices. We offer a different interpretation. We maintain that nominal exchange rates and prices need not converge at the same rate, as is implicit in rational-expectations sticky-price models of the exchange rate. Evidence from an unobserved components model for nominal prices and nominal exchange rates that imposes relative purchasing power parity in the long run indicates that nominal exchange rates converge much more slowly than nominal prices. The real puzzle is why nominal exchange rates converge so slowly.

Likelihood-Based Confidence Sets for the Timing of Structural Breaks

In this paper, we propose a new approach to constructing confidence sets for the timing of structural breaks. This approach involves using Markov-chain Monte Carlo methods to simulate marginal “fiducial” distributions of break dates from the likelihood function. We compare our proposed approach to asymptotic and bootstrap confidence sets and find that it performs best in terms of producing short confidence sets with accurate coverage rates. Our approach also has the advantages of i) being broadly applicable to different patterns of structural breaks, ii) being computationally efficient, and iii) requiring only the ability to evaluate the likelihood function over parameter values, thus allowing for many possible distributional assumptions for the data. In our application, we investigate the nature and timing of structural breaks in postwar U.S. Real GDP. Based on marginal fiducial distributions, we find much tighter 95% confidence sets for the timing of the so-called “Great Moderation” than has been reported in previous studies.Fiducial Inference; Bootstrap Methods; Structural Breaks; Confidence Intervals and Sets; Coverage Accuracy and Expected Length; Markov-chain Monte Carlo;

A steady-state approach to trend/cycle decomposition of regime-switching processes

In this paper, we present a new approach to trend/cycle decomposition under the assumption that the trend is the permanent component and the cycle is the transitory component of an integrated time series. The permanent component is defined as the steady-state level of the series, a definition that has exploitable forecasting implications useful for identification. We operationalize the steady-state approach for regime-switching processes and we use generated data from such processes to demonstrate the advantages of the steady-state approach over alternative approaches to trend/cycle decomposition. We then apply the steady-state approach to estimate the trend and cycle of U.S. real GDP implied by a regime-switching forecasting model. Our findings portray a very different picture of the business cycle than implied by more traditional methods.Time-series analysis ; Business cycles

Shift Contagion in Asset Markets

The authors develop a new methodology to investigate how crises cause the relationship between financial variables to change. Two possible sources of increased co-movement between markets during high-variance episodes are considered: larger common shocks operating through standard market linkages, and a structural change in the propagation of shocks between markets, called “shift contagion.” The methodology has three key features: (i) high- and low-variance episodes are model-determined, rather than exogenously assigned; (ii) the markets where crises originate need not be known; and (iii) the approach provides an unambiguous test of shift contagion. Applications to bivariate returns in currency markets of developed countries and bond markets of emerging-market countries suggest that shift contagion occurs among the former but not the latter.Financial markets; Econometric and statistical methods

The Meta Taylor Rule

This paper provides a characterisation of U.S. monetary policy within a generalized Tay¬lor rule framework that accommodates uncertainties about the duration of policy regimes and the speciÞcation of the rule, in addition to the standard parameter and stochastic un¬certainties inherent in traditional Taylor rule analysis. Our approach involves estimation and inference based on Taylor rules obtained through standard linear regression methods, but combined using Bayesian model averaging techniques. Employing data that were available in real time, the estimated version of the ‘meta’ Taylor rule provides a ßexible but compelling characterisation of monetary policy in the United States over the last forty years.Taylor rule, real-time policy, model uncertainty, US interest rates.

Why Are Beveridge-Nelson and Unobserved-Component Decompositions of GDP So Different?

This paper reconciles two widely-used decompositions of GDP into trend and cycle that yield starkly different results. Beveridge-Nelson (BN) implies that a stochastic trend accounts for most of the variation in output, while Unobserved-Components (UC) implies cyclical variation is dominant. Which is correct has broad implications for the relative importance of real versus nominal shocks. We show the difference arises from the restriction imposed in UC that trend and cycle innovations are uncorrelated. When this restriction is relaxed, the UC decomposition is identical to the BN decomposition. Furthermore, the zero correlation restriction can be rejected for U.S. quarterly GDP, with the estimated correlation being –0.9.

Reproducing Business Cycle Features: How Important Is Nonlinearity Versus Multivariate Information?

In this paper, we consider the ability of time-series models to generate simulated data that display the same business cycle features found in U.S. real GDP. Our analysis of a range of popular time-series models allows us to investigate the extent to which multivariate information can account for the apparent univariate evidence of nonlinear dynamics in GDP. We find that certain nonlinear specifications yield an improvement over linear models in reproducing business cycle features, even when multivariate information inherent in the unemployment rate, inflation, interest rates, and the components of GDP is taken into account.