When studying the Federal Open Market Committee’s (FOMC’s) interest rate rule, some authors, such as Gonzalez-Astudillo [(2018) Journal of Monetary, Credit, and Banking 50(1), 115–154.], find evidence for changes in inflation and output gap responses. Others, such as Sims and Zha [(2006) American Economic Review 96(1), 54–81.], only find evidence for a change in the variance of the interest rate rule. In this paper, I develop a new two-regime Markov-switching model that probabilistically performs variable selection and identification of parameter change for each variable in the model. I find substantial evidence that there have been changes in the FOMC’s response to the unemployment gap and in the volatility of the rule. When the FOMC responds strongly to the unemployment gap, I find a bimodal density for the inflation response coefficient. Despite the bimodal density, there is a low probability that there have been changes in the FOMC’s response to inflation.

We consider a model-averaged forecast-based estimate of the output gap to measure economic slack in 10 industrialized economies. Our measure takes changes in the long-run growth rate into account and, by addressing model uncertainty using equal weights on different forecast-based estimates, is robust to different assumptions about the underlying structure of the economy. For all 10 countries in the sample, we find that the estimated output gap has much larger negative movements during recessions than positive movements in expansions, suggesting business cycle asymmetry is an intrinsic characteristic of industrialized economies. Furthermore, the estimated output gap is always strongly negatively correlated with future output growth and unemployment and positively correlated with capacity utilization. It also implies a convex Phillips Curve in many cases. The model-averaged output gap is reliable in real time in the sense of being subject to relatively small revisions.

We document the strong evidence of time variation in the volatility of Euro Area business cycles since 1970. Then we provide the quantitative sources of these changes using a medium-scale DSGE model allowing time variation in structural disturbance variances. We show that (1) the size of different types of shock oscillates, in a synchronized manner, between two regimes over time, with the high-volatility regime prevailing predominantly in the 1970s, sporadically in the 1980s and 1990s, and during the Great Recession; (2) their relative importance remains, however, unchanged across regimes, where neutral technology shocks and marginal efficiency of investment shocks are the dominant sources of business cycle fluctuations; and 3) these investment shocks, which affect the transformation of savings into productive capital, can be interpreted as an indicator of credit conditions.

This paper investigates patterns of variation in economic growth across and within countries using a time-varying transition matrix Markov-switching approach. The model developed here explains the dynamics of growth based on a collection of different states that countries pass into and out of over time; in addition, these states are characterized by their own submodels and growth patterns. The transition matrix among the different states varies over time—depending on the conditioning variables of each country—with a linear dynamic for each state. We develop a generalization of Diebold's EM algorithm and estimate a sample model in a panel with a transition matrix conditioned on institutional quality and the investment level. We find three states of growth: stable growth, miraculous growth, and stagnation. The results show that institutional quality is an important determinant of long-term growth, whereas the investment level plays a variety of roles: it contributes positively in countries with high-quality institutions but is of little relevance in countries with medium- or low-quality institutions.

Early research on the Taylor rule typically divided the data exogenously into pre-Volcker and Volcker–Greenspan subsamples. We contribute to the recent trend of endogenizing changes in monetary policy by estimating a real-time forward-looking Taylor rule with endogenous Markov switching coefficients and variance. The response of the interest rate to inflation is regime-dependent, with the pre- and post-Volcker samples containing monetary regimes where the Fed did and did not follow the Taylor principle. Although the Fed consistently adhered to the Taylor principle before 1973 and after 1984, it followed the Taylor principle from 1975 to 1979 and did not follow the Taylor principle from 1980 to 1984. We also find that the Fed only responded to real economic activity during the states in which the Taylor principle held. Our results are consistent with the idea that exogenously dividing postwar monetary policy into pre- and post-Volcker samples is misleading. The greatest qualitative difference between our results and recent research employing time-varying parameters is that we find that the Fed did not adhere to the Taylor principle during most of Paul Volcker's tenure, a finding that accords with the historical record of monetary policy.

This paper explores the hypothesis that the sources of economic and financial crises differ from those of noncrisis 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 noncrisis 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.

This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

This paper compares the different dynamics of the simple-sum monetary aggregates and the Divisia monetary aggregate indices over time, over the business cycle, and across high and low inflation and interest-rate phases. Although traditional comparisons of the series sometimes suggest that simple-sum and Divisia monetary aggregates share similar dynamics, there are important differences around turning points that cannot be evaluated by their average behavior. We use a factor model with a regime-switching model that separates the common movements underlying the monetary aggregate indices from idiosyncratic variations in each series. We find that the major differences between the simple-sum aggregates and Divisia indices occur around the beginnings and ends of recessions and during some high-interest-rate phases. We note the inferences' policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia [Journal of Political Economy, 104 (5) (1996), 1065–1083] has observed in this regard, “measurement matters.”

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.

Monetary policy should be guided by macroeconomic models with limited nominal rigidity; ‘New Classical’ or even for some issues just plain Classical (i.e. with no nominal rigidity at all) models are perfectly adequate for understanding various aspects of the economy that have previously led economists to believe in a high degree of nominal rigidity. On UK data these models account for the facts of inflation persistence and exchange rate ‘overshooting’; their impulse responses are in line with the data; and a typical example, the Liverpool Model, is marginally accepted in its entirety by the data since 1979. Such models suggest that no increased macro instability would result from taking the rigours of monetary policy one stage further from inflation targeting and ensuring that the price level itself is returned to its long-run preset target path — so that the value of money over long periods of time would be utterly predictable.

We first explore empirical evidence of parameter and shock uncertainties in a state-space model with Markov switching. The evidence indicates that uncertainties in the U.S. economy have been too great to accurately define monetary policy rules. We then explore monetary policy rules under uncertainty with two approaches: the RLS learning algorithm and robust control. The former allows the parameters to be learned for a given model. Yet, as our results of the RLS learning in a framework of optimal control indicate, the state variables do not necessarily converge even in a nonstochastic model. The latter, by permitting uncertainty with respect to model misspecification, allows for a broader framework. Our study on robust control shows that robust optimal monetary policy rules reveal a stronger response to fluctuations in inflation and output than when no uncertainty exists, implying that uncertainty does not necessarily require caution.

Let Y be a Ornstein–Uhlenbeck diffusion governed by astationary and ergodic process X : dYt = a(Xt)Yt dt + σ(Xt)dWt,Y0 = y0 . We establish that under the condition α = Eµ(a(X0)) < 0 with μ the stationary distribution of the regime process X, the diffusion Y is ergodic. We also consider conditions for the existence of moments for theinvariant law of Y when X is a Markov jump process having a finite number of states.Using results on random difference equationson one hand and the fact that conditionally toX, Y is Gaussian on the other hand, we give such a condition for the existence of the moment of order s ≥ 0. Actually we recover in this case a result that Basak et al. [J. Math. Anal. Appl. 202 (1996) 604–622] have established using the theory of stochastic control of linear systems.

We investigate the stability problem for a nonlinear autoregressive model with Markov switching. First we give conditions for the existence and the uniqueness of a stationary ergodic solution. The existence of moments of such a solution is then examined and we establish a strong law of large numbers for a wide class of unbounded functions, as well as a central limit theorem under an irreducibility condition.