We hope to be able to provide answers to the following questions: 1) Has there been a structural break in postwar U.S. real GDP growth toward more stabilization? 2) If so, when would it have been? 3) What's the nature of the structural break? For this purpose, we employ a Bayesian approach to dealing with structural break at an unknown changepoint in a Markov-switching model of business cycle. Empirical results suggest that there has been a structural break in U.S. real GDP growth toward more stabilization, with the posterior mode of the break date around 1984:1. Furthermore, we #nd a narrowing gap between growth rates during recessions and booms is at least as important as a decline in the volatility of shocks. Key Words: Bayes Factor, Gibbs sampling, Marginal Likelihood, Markov-Switching, Stabilization, Structural Break. JEL Classi#cations: C11, C12, C22, E32. 1. Introduction In the literature, the issue of postwar stabilization of the U.S. economy relative to the prewar period has...

Abstract: In the first half of this century, special attention was given to two features of the business cycle: the comovement of many individual economic series and the different behavior of the economy during expansions and contractions. Recent theoretical and empirical research has revived interest in each attribute separately, and we survey this work. Notable empirical contributions are dynamic factor models that have a single common macroeconomic factor and nonlinear regime-switching models of a macroeconomic aggregate. We conduct an empirical synthesis that incorporates both of these features. It is desirable to know the facts before attempting to explain them; hence, the attractiveness of organizing business-cycle regularities within a model-free framework. During the first half of this century, much research was devoted to obtaining just such an empirical characterization of the business cycle. The most prominent example of this work

Recent imperfect capital market theories predict the presence of asymmetries in the variation of small and large firms' risk over the economic cycle. Small firms with little collateral should be more strongly affected by tighter credit market conditions in a recession state than large, better collateralized ones. This paper adopts a flexible econometric model to analyse these implications empirically. Consistent with theory, small firms display the highest degree of asymmetry in their risk across recession and expansion states and this translates into a higher sensitivity of these firms' expected stock returns with respect to variables that measure credit market conditions. Recent imperfect capital market theories (e.g., Bernanke and Gertler (1989), Gertler and Gilchrist (1994), Kiyotaki and Moore (1997)) predict that changing credit market conditions can have very different effects on small and large firms' risk. Agency costs induced by asymmetry in the information held by firms and their creditors make...

We develop a term structure model where the short interest rate and the market price of risks are subject to discrete regime shifts. Empirical evidence from efficient method of moments estimation provides considerable support for the regime shifts model. Standard models, which include affine specifications with up to three factors, are sharply rejected in the data. Our diagnostics show that only the regime shifts model can account for the well-documented violations of the expectations hypothesis, the observed conditional volatility, and the conditional correlation across yields. We find that regimes are intimately related to business cycles. MANY PAPERS DOCUMENT THAT THE UNIVARIATE short interest rate process can be reasonably well modeled in the time series as a regime switching process ~see Hamilton ~1988!, Garcia and Perron ~1996!!. In addition to this statistical evidence, there are economic reasons as well to believe that regime shifts are important to understanding the behavior of the entire yield curve. For example, business cycle expansion and contraction “regimes ” potentially

We study efficient insurance arrangements when there is complete information but limited commitment because only limited penalties can be imposed if households renege on their promises. Insurance arrangements must therefore take into account the fact that households will renege if benefits from doing so outweigh costs. Using a general dynamic model with MarkovJan aggregate and idiosyncratic uncertainty, we show that efficient arrangements are characterised by a simple updating rule, similar to a simple debt contract with occasional forgiveness. We use Indian village data to test thetheory against three alternatives: autarky, full insurance, and a static model of limited commitment.

Abstract. That information surprises result in discontinuous interest rates is no surprise to participants in the bond markets. We develop a class of Poisson-Gaussian models of the Fed Funds rate to capture surprise effects, and show that these models offer a good statistical description of short rate behavior, and are useful in understanding many empirical phenomena. Estimators are used based on analytical derivations of the characteristic functions and moments of jump-diffusion stochastic processes for a range of jump distributions, and are extended to discrete-time models. Jump (Poisson) processes capture empirical features of the data which would not be captured by Gaussian models, and there is strong evidence that existing models would be well-enhanced by jump and ARCH-type processes. The analytical and empirical methods in the paper support many applications, such as testing for Fed intervention effects, which are shown to be an important source of surprise jumps in interest rates. The jump model is shown to mitigate the non-linearity of interest rate drifts, so prevalent in pure-diffusion models. Day-of-week effects are modelled explicitly, and the jump model provides evidence of bond market overreaction, rejecting the martingale hypothesis for interest rates. Jump models mixed with Markov switching processes predicate that conditioning on regime is important in determining short rate behavior.

This paper investigates to what extent globalization and regional integration lead to increasing equity market interdependence. I focus on the case of Western Europe, as this region has gone through a unique period of economic, financial, and monetary integration. More specifically, I quantify the magnitude and time-varying nature of volatility spillovers from the aggregate European (EU) and US market to 13 local European equity markets. To account for time-varying integration, I allow the shock sensitivities to change through time by means of a regime-switching model. I find that these regime switches are both statistically and economically important. While both the EU and US shock spillover intensity has increased over the 1980s and 1990s, the rise is more pronounced for EU spillovers. In most countries, shock spillover intensities increased most strongly in the second half of 1980s and the first half of the 1990s. Increased trade integration, equity market development, and low inflation are shown to have contributed to the increase in EU shock spillover intensity. Finally, I find some evidence for contagion from the US market to a number of local European equity markets during periods of high world market volatility.

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of the two models, and a Monte Carlo study. The latter allows us to control for factors that may otherwise undermine the performance of the non-linear models. 1 Introduction In recent years there has been a great deal of interest in the modelling of non-linearities in economic time series. While the usefulness of linear time-series models in the tradition of Box and Jenkins (1970) is usually gauged by their predictive ability, there does not appear to be a clear consensus as to whether allowing for non-linearities has l...

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We examine dynamic asymmetries in U.S unemployment using nonlinear time series models and Bayesian methods. We#ndstrong statistical evidence in favor of a two-regime threshold autoregressive model. Empirical results indicate that, once wetakeinto account both parameter and model uncertainty, there are economically interesting asymmetries in the unemployment rate. One #nding of particular interest is that shocks whichlower the unemployment rate tend to have a smaller e#ect than shocks which raise the unemployment rate. This #nding is consistent with unemploymentrises being sudden and falls gradual. Keywords: Nonlinearity, Threshold Autoregression, Bayesian, Unemployment. JEL: C11,C22,C52,E24. # Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. y Financial support from the NSF under grant SES 9211726 and the Center for Computable Economics at UCLA is gratefully acknowledged. 1 Introduction The vast majority of reduced fo...