James M. Nason

Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research

This paper studies the effects of applying the Hodrick-Prescott filter to trend and difference stationary time series. Applying the Hodrick-Prescott filter to an integrated process is similar to detrending a random walk. When the data are difference stationary, the Hodrick-Prescott filter can generate business cycle dynamics even if none are present in the original data. We study the implications for interpreting stylized facts about business cycles and for analyzing data generated by real business cycle models.

Testing for structural breaks in cointegrated relationships

Abstract The purpose of this paper is to investigate the tests of Hansen (1992) to detect structural breaks in cointegrated relations using Monte Carlo methods. The evaluation takes place within the linear quadratic model. We study models that generate cointegrated relations with single and multiple regressors. The evidence with multiple regressors suggests that the tests have proper size but the power is low when the cost of adjustment is high. In addition to the tests of Hansen, we consider the sensitivity of the augmented Dickey-Fuller (ADF) test for cointegration in the presence of a structural break. Given a break, our Monte Carlo experiments show that the rejection frequency of the ADF test decreases substantially. Thus the ADF test correctly indicates that the constant parameter cointegrating relationship is not appropriate.

Choosing the best volatility models: the model confidence set approach

This paper applies the model confidence set (MCS) procedure of Hansen, Lunde and Nason (2003) to a set of volatility models. An MCS is analogous to the confidence interval of a parameter in the sense that it contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is based on 55 volatility models and the MCS includes about a third of these when evaluated by mean square error, whereas the MCS contains only a VGARCH model when mean absolute deviation criterion is used. We conduct a simulation study which shows that the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, the latter delivers inferior performance.

Testing the Significance of Calendar Effects

This paper studies tests of calendar effects in equity returns. It is necessary to control for all possible calendar effects to avoid spurious results. The authors contribute to the calendar effects literature and its significance with a test for calendar-specific anomalies that conditions on the nuisance of possible calendar effects. Thus, their approach to test for calendar effects produces robust data-mining results. Unfortunately, attempts to control for a large number of possible calendar effects have the downside of diminishing the power of the test, making it more difficult to detect actual anomalies. The authors show that our test achieves good power properties because it exploits the correlation structure of (excess) returns specific to the calendar effect being studied. We implement the test with bootstrap methods and apply it to stock indices from Denmark, France, Germany, Hong Kong, Italy, Japan, Norway, Sweden, the United Kingdom, and the United States. Bootstrap p-values reveal that calendar effects are significant for returns in most of these equity markets, but end-of-the-year effects are predominant. It also appears that, beginning in the late 1980s, calendar effects have diminished except in small-cap stock indices.

Investment and the Current Account in the Short Run and the Long Run

Theoretical models of the relationship between investment and the current account impose restrictions on the joint dynamic behavior of these variables. These restrictions come in two forms. One imposes causal orderings on investment and the current account. The other restriction concerns the permanent responses of these variables to different shocks. We use these restrictions to identify empirically structural shocks from vector autoregressions of investment and the current account for Canada. Under certain identifications, our results support the implications of the intertemporal, small open economy model. However, these results are sensitive to perturbations of the identifications.

Impulse dynamics and propagation mechanisms in a real business cycle model

Abstract In a typical real business cycle model, we find that output dynamics are determined primarily by impulse dynamics and that endogenous propagation mechanisms are weak. Consequently, the model must rely on external sources of dynamics in order to replicate the univariate dynamics of U.S. per capita GNP.

The long-horizon regression approach to monetary neutrality: how should the evidence be interpreted?

Abstract Fisher and Seater [American Economic Review, 83 (1993) 402] develop a long-horizon regression test of long-run monetary neutrality and reject it in a long-annual U.S. sample. This test often fails to be rejected elsewhere. We can resolve the conflicting results.

Business Cycles and Financial Crises: The Roles of Credit Supply and Demand Shocks

This paper explores the hypothesis that the sources of economic and financial crises differ from non-crisis business cycle fluctuations. We employ Markov-switching Bayesian vector autoregressions (MS-BVARs) to gather evidence about the hypothesis on a long annual U.S. sample running from 1890 to 2010. The sample covers several episodes useful for understanding U.S. economic and financial history, which generate variation in the data that aids in identifying credit supply and demand shocks. We identify these shocks within MS-BVARs by tying credit supply and demand movements to inside money and its intertemporal price. The model space is limited to stochastic volatility (SV) in the errors of the MS-BVARs. Of the 15 MS-BVARs estimated, the data favor a MS-BVAR in which economic and financial crises and non-crisis business cycle regimes recur throughout the long annual sample. The best-fitting MS-BVAR also isolates SV regimes in which shocks to inside money dominate aggregate fluctuations.

Simple versus optimal rules as guides to policy

This paper contributes to the policy evaluation literature by developing new strategies to study alternative policy rules. We compare optimal rules to simple rules within canonical monetary policy models. In our context, an optimal rule represents the solution to an intertemporal optimization problem in which a loss function for the policymaker and an explicit model of the macroeconomy are specified. We define a simple rule to be a summary of the intuition policymakers and economists have about how a central bank should react to aggregate disturbances. The policy rules are evaluated under minimax and minimax regret criteria. These criteria force the policymaker to guard against a worst-case scenario, but in different ways. Minimax makes the worst possible model the benchmark for the policymaker, while minimax regret confronts the policymaker with uncertainty about the true model. Our results indicate that the case for a model-specific optimal rule can break down when uncertainty exists about which of several models is true. Further, we show that the assumption that the policymaker’s loss function is known can obscure policy trade-offs that exist in the short, medium, and long run. Thus, policy evaluation is more difficult once it is recognized that model and preference uncertainty can interact.

Bringing Financial Stability into Monetary Policy

This paper arms central bank policy makers with ways to think about interactions between financial stability and monetary policy. We frame the issue of whether to integrate financial stability into monetary policy operating rules by appealing to the observation that in actual economies financial markets are incomplete. Incomplete markets create financial market frictions that prevent economic agents from perfectly sharing risk; in the absence of frictions, financial (in)stability would be of no concern. Overcoming these frictions to improve risk sharing across economic agents is, in our view, the intent of policies geared toward ensuring financial stability. There are many definitions of financial stability. Although the definitions share the notion that financial stability becomes an issue for policy makers when a breakdown in risk-sharing arrangements in financial markets has a negative effect on real economic activity, we give several examples that show this notion is too general for thinking about the role that monetary policy might have in smoothing shocks to financial stability. Examples include statistical models that seek to separate “good” from “bad” changes in private-sector debt aggregates, new Keynesian policy prescriptions grounded in neo-Wicksellian natural rate rules, and a historical episode involving the 1920s Federal Reserve. These examples raise a cautionary flag for policy attempts to control both the growth and the composition of debt that financial markets produce. We conclude with some advice for revising central banks’ Monetary Policy Reports.