Inflation Target Credibility: Do the Financial Markets Find the Targets Believable?

We investigate the credibility of inflation targeting (IT) central banks (CBs) by estimating perceived inflation targets of the financial markets. We calculate financial markets’ beliefs about the inflation targets of 24 IT countries. Then, we analyse whether the financial markets’ beliefs about inflation targets match the announced targets. We conclude that the perceived upper bound of the inflation target is significantly higher than the announced one in many countries. Additionally, the perceived target band is narrower and asymmetric around the mid‐point of the target for most CBs. We examine the implications of these findings and find that IT CBs are more likely to miss their targets when the perceptions of the financial markets are higher than the announced IT targets. These results indicate that IT CBs should pay attention to the perceptions of the announced targets when implementing policy actions.     

How money illusions and heterogeneous beliefs affect asset prices

We develop a dynamic asset pricing model with two investors with money illusions and heterogeneous beliefs about some aspects of the economy. The model is tractable and delivers closed forms for all equilibrium quantities. The study shows that money illusion leads the nominal shock risk to generate spillover effects on the real side of the economy and affects all equilibrium quantities, even without inflation disagreement. We find that bond yields increase, but the stock price decreases, as money illusion increases. Bond yield and stock price volatilities increase with fundamental disagreement, while the latter decreases with inflation disagreement. We also discover that the stock risk premium is inverse-U shaped as inflation disagreement increases. Moreover, we find that the optimistic investor holds positions in real bonds and stocks, and shorts the nominal bond to hedge against the risk of market changes, which is in line with the pessimistic investor’s beliefs.     

Learning,disagreement and inflation forecasting

This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.     

General Bayesian time-varying parameter vector autoregressions for modeling government bond yields

US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out model selection, we adopt Bayesian shrinkage priors. Our framework is applied to model the US yield curve. We show that the model forecasts well, and focus on selected in-sample features to analyze determinants of structural breaks in US yield curve dynamics.     

Inversion copulas from nonlinear state space models with an application to inflation forecasting

We propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation.     

Mind the Output Gap: The Disconnect of Growth and Inflation during Recessions and Convex Phillips Curves in the Euro Area

We develop a theoretical model that features a business cycle‐dependent relation between output, price inflation and inflation expectations, augmenting the model by Svensson (1997) with a nonlinear Phillips curve that reflects the rationale underlying the capacity constraint theory (Macklem, 1997). The theoretical model motivates our empirical assessment, based on a regime‐switching Phillips curve and a regime‐switching monetary structural VAR, employing different filter‐based, semi‐structural model‐based and Bayesian factor model‐implied output gaps. The analysis confirms the presence of a convex relationship between inflation and the output gap, meaning that the coefficient in the Phillips curve on the output gap recurringly increases during times of expansion and abates during recessions. Sign‐restricted monetary policy shocks based on a regime‐switching monetary SVAR reveal that expansionary monetary policy induces less pressure on inflation at times of weak as opposed to strong growth; thereby rationalizing relatively stronger expansionary policy, including unconventional volume‐based policy, during times of deep recession. A further augmented model shows that an effective euro exchange rate shock, too, implies business cycle state‐dependent responses, with more upward pressure on prices arising from unexpected currency depreciation at times of expansion than during recession phases.     

Market fundamentals versus rational bubbles in stock prices: a Bayesian perspective

Using Bayesian Markov chain Monte Carlo methods, we decompose the log price‐dividend ratio into a market fundamentals component and a bubble component. The market fundamentals component depends on expectations of future dividend growth and required returns, while the bubble component is assumed to follow a Markov switching model that allows for the possibility of exploding and collapsing regimes. If prior beliefs allow for the possibility of persistent shocks to dividend growth and/or required returns, the posterior distribution suggests the bubble component contributes virtually nothing to the stock price movements over our sample. On the other hand, if one's priors rule out the possibility of persistent shocks to dividend growth and required returns, the bubble component can have a much larger role to play in stock price movements. However, the regime switching behavior of the bubble bears little resemblance to infrequent switching from an exploding bubble regime to a collapsing or dormant bubble regime. Copyright © 2008 John Wiley & Sons, Ltd.     

Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major US macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long‐run volatility change as a recurrent structure change, while short‐run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure‐dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short‐run regime switches and long‐run structure changes in the US macroeconomic data. Copyright © 2010 John Wiley & Sons, Ltd.     

Evolving macroeconomic perceptions and the term structure of interest rates

We explore the role of evolving beliefs regarding the structure of the macroeconomy in improving our understanding of the term structure of interest rates within the context of a simple macro-finance model. Using quarterly vintages of real-time data and survey forecasts for the United States over the past 40 years, we show that a recursively estimated VAR on real GDP growth, inflation and the nominal short-term interest rate generates predictions that are more consistent with survey forecasts than a benchmark fixed-coefficient counterpart. We then estimate a simple term structure model under the assumption that investor risk attitude is driven by near-term expectations of the three state variables. When we allow for evolving beliefs about the macroeconomy, the resulting term structure model provides a better fit to the cross section of yields than the benchmark model, especially at longer maturities, and exhibits better performance in out-of-sample predictions of future yield movements.     

Bayesian stochastic search for VAR model restrictions

We propose a Bayesian stochastic search approach to selecting restrictions for vector autoregressive (VAR) models. For this purpose, we develop a Markov chain Monte Carlo (MCMC) algorithm that visits high posterior probability restrictions on the elements of both the VAR regression coefficients and the error variance matrix. Numerical simulations show that stochastic search based on this algorithm can be effective at both selecting a satisfactory model and improving forecasting performance. To illustrate the potential of our approach, we apply our stochastic search to VAR modeling of inflation transmission from producer price index (PPI) components to the consumer price index (CPI).     

Issue Information

Much research studies US inflation history with a trend‐cycle model with unobserved components, where the trend may be viewed as the Fed's evolving inflation target or long‐horizon expected inflation. We provide a novel way to measure the slowly evolving trend and the cycle (or inflation gap), by combining inflation predictions from the Survey of Professional Forecasters (SPF) with realized inflation. The SPF forecasts may be treated either as rational expectations (RE) or updating according to a sticky information (SI) law of motion. We estimate RE and SI state‐space models with stochastic volatility on samples of consumer price index and gross national product/gross domestic product deflator inflation and the associated SPF inflation predictions using a particle Metropolis–Markov chain Monte Carlo sampler. The trend converges to 2% and its volatility declines over time—two tendencies largely complete by the late 1990s.     

The Credibility of Hong Kong's Currency Board System: Looking Through the Prism of MS‐VAR Models with Time‐Varying Transition Probabilities

This paper employs a Markov regime‐switching VAR model to describe and analyse the time‐varying credibility of Hong Kong's currency board system. The endogenously estimated discrete regime shifts are made dependent on macroeconomic fundamentals. This enables us to determine which changes in macroeconomic variables can trigger switches between the low and high credibility regimes. We carry out extensive testing to search for the most appropriate specification of the Markov regime‐switching model. We find strong evidence of regime switching behaviour that portrays the time‐varying nature of credibility in the historical data.     

Estimating and Forecasting the Yield Curve Using A Markov Switching Dynamic Nelson and Siegel Model

We estimate versions of the Nelson–Siegel model of the yield curve of US government bonds using a Markov switching latent variable model that allows for discrete changes in the stochastic process followed by the interest rates. Our modeling approach is motivated by evidence suggesting the existence of breaks in the behavior of the US yield curve that depend, for example, on whether the economy is in a recession or a boom, or on the stance of monetary policy. Our model is parsimonious, relatively easy to estimate and flexible enough to match the changing shapes of the yield curve over time. We also derive the discrete time non‐arbitrage restrictions for the Markov switching model. We compare the forecasting performance of these models with that of the standard dynamic Nelson and Siegel model and an extension that allows the decay rate parameter to be time varying. We show that some parametrizations of our model with regime shifts outperform the single‐regime Nelson and Siegel model and other standard empirical models of the yield curve. Copyright © 2014 John Wiley & Sons, Ltd.     

Solving endogenous regime switching models

This paper solves rational expectations models in which structural parameters switch across multiple regimes according to state-dependent (endogenous) transition probabilities. Assuming small shocks and smooth transition probabilities, we apply a perturbation approach. We first provide for conditions under which a unique bounded equilibrium exists. We then compute first- and second-order approximations. In a new-Keynesian model with monetary policy switching, we document new effects of monetary policy switching when transition probabilities depend on inflation.     

Inflation illusion and the Taylor principle: An experimental study

We develop a simple experimental setting to evaluate the role of the Taylor principle, which holds that the nominal interest rate has to respond more than one-for-one to fluctuations in the inflation rate to exert a stabilizing effect. In our setting, the average inflation rate fluctuates around the inflation target if the computerized central bank obeys the Taylor principle. If the Taylor principle is violated, the average inflation rate persistently deviates from the target. These deviations from the target are less pronounced, if inflation rates cannot be as readily observed as nominal interest rates. This result is consistent with the interpretation that subjects underestimate the influence of inflation on the real return to savings if the inflation rate is only observed ex post.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

The Influence and Policy Signalling Role of FOMC Forecasts

Federal Open Market Committee (FOMC) policymakers have published macroeconomic forecasts since 1979 and we examine the effects of FOMC inflation forecasts using a structural VAR model. First, we assess whether they influence private inflation expectations. Second, we investigate the underlying mechanism at work and whether they convey policy signals. We provide original evidence that FOMC inflation forecasts influence private ones. We also find that the influencing effect of FOMC forecasts does not come through current Fed rate changes, that FOMC forecasts affect private expectations in a different way than current policy decisions, and that FOMC forecasts are informative about future Fed rate movements.     

Modelling Inflation Volatility

This paper discusses estimation of US inflation volatility using time‐varying parameter models, in particular whether it should be modelled as a stationary or random walk stochastic process. Specifying inflation volatility as an unbounded process, as implied by the random walk, conflicts with priors beliefs, yet a stationary process cannot capture the low‐frequency behaviour commonly observed in estimates of volatility. We therefore propose an alternative model with a change‐point process in the volatility that allows for switches between stationary models to capture changes in the level and dynamics over the past 40 years. To accommodate the stationarity restriction, we develop a new representation that is equivalent to our model but is computationally more efficient. All models produce effectively identical estimates of volatility, but the change‐point model provides more information on the level and persistence of volatility and the probabilities of changes. For example, we find a few well‐defined switches in the volatility process and, interestingly, these switches line up well with economic slowdowns or changes of the Federal Reserve Chair. Moreover, a decomposition of inflation shocks into permanent and transitory components shows that a spike in volatility in the late 2000s was entirely on the transitory side and characterized by a rise above its long‐run mean level during a period of higher persistence. Copyright © 2015 John Wiley & Sons, Ltd.     

Incorporating vintage differences and forecasts into Markov switching models

This paper incorporates vintage differences and forecasts into the Markov switching models described by Hamilton (1994). The vintage differences and forecasts induce parameter breaks close to the end of the sample, too close for standard maximum likelihood techniques to produce precise parameter estimates. A supplementary procedure estimates the statistical properties of the end-of-sample observations that behave differently from the rest, allowing inferred probabilities to reflect the breaks. Empirical results using real-time data show that these techniques improve the ability of a Markov switching model based on GDP and GDI to recognize the start of the 2001 recession.     

Testing the unbiased forward exchange rate hypothesis using a Markov switching model and instrumental variables

This paper develops a model for the forward and spot exchange rate which allows for the presence of a Markov switching risk premium in the forward market and considers the issue of testing the unbiased forward exchange rate (UFER) hypothesis. Using US/UK data, it is shown that the UFER hypothesis cannot be rejected, provided that instrumental variables are used to account for within‐regime correlation between explanatory variables and disturbances in the Markov switching model on which the test is based. Copyright © 2005 John Wiley & Sons, Ltd.