Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Asymmetric Quantile Persistence and Predictability: the Case of US Inflation

This article investigates the evidence of time‐variation and asymmetry in the persistence of US inflation. We compare the out‐of‐sample performance of different forecasting models and find that quantile forecasts from an Auto‐Regressive (AR) model with level‐dependent volatility are at least as accurate as the forecasts of the Quantile Auto‐Regressive model, in particular for the core inflation measures. Our results indicate that the persistence of core inflation has been relatively constant and high, but it declined for the headline inflation measures. We also find that the asymmetric persistence of inflation shocks can be mostly attributed to the positive relation between inflation level and its volatility.     

Modelling multi‐period inflation uncertainty using a panel of density forecasts

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often noted in the ARCH literature, is largely lost in a multi‐period context with varying forecast horizons. We propose a novel way of estimating ‘news’ and its variance using the Kullback‐Leibler information, and show that the latter is an important determinant of forecast uncertainty. Our evidence suggests a strong relationship of forecast uncertainty with level of inflation, but not with forecaster discord or with the volatility of a number of other macroeconomic indicators. Copyright © 2006 John Wiley & Sons, Ltd.     

Time-Varying Relationship between Inflation and Inflation Uncertainty

This paper investigates whether the relationship between inflation and inflation uncertainty has changed and whether the change in this relationship has been gradual or abrupt. We extend the time-varying parameter with stochastic volatility in mean model (TVP-SVM) to include a mixture innovation disturbance in the time-varying parameter process. The proposed model produces more reliable estimates and allows us to investigate the occurrence of breaks in the gradually evolving process of the time varying coefficients. Using data of US, Germany, Canada, New Zealand, UK, France, Italy, Spain and Australia, we find that: (i) the relationship between inflation and inflation uncertainty substantially varies over time; (ii) there is strong support for the existence of abrupt changes in the US inflation–inflation uncertainty relationship; (iii) our empirical results of Canada and New Zealand show that the correlation between inflation and inflation uncertainty has been much weaker since early 1990s, which coincides with the timing of the implementation of inflation targeting.     

Expert product reviews and conflict of interest

Many firms that produce expert product reviews benefit from increased sales of the products they review, resulting in a conflict of interest. We evaluate expert product reviews from a video game magazine owned by a game retailer. We find evidence of review inflation for lower‐quality games and in periods shortly following the release of a game's corresponding hardware. These results are consistent with theoretical predictions for a firm that optimizes the trade‐off between sales revenue and the reputational costs associated with biasing reviews.     

TIME‐VARYING DYNAMICS OF THE REAL EXCHANGE RATE: AN EMPIRICAL ANALYSIS

We use a time‐varying structural vector autoregression to investigate evolving dynamics of the real exchange rate for the UK, euro area and Canada. We show that demand and nominal shocks have a substantially larger impact on the real exchange rate after the mid 1980s. Real exchange rate volatility, relative to fundamentals, also shows a marked increase after this point in time. However, there is some evidence suggesting a closer business cycle co‐movement of the real exchange rate and fundamentals. Simulations from an open‐economy DSGE model show that these results are consistent with a decline in exchange rate pass‐through. Copyright © 2012 John Wiley & Sons, Ltd.     

LONG‐RUN RISKS IN THE TERM STRUCTURE OF INTEREST RATES: ESTIMATION

This paper estimates a model in which persistent fluctuations in expected consumption growth, expected inflation, and their time‐varying volatility determine asset price variation. The model features Epstein–Zin recursive preferences, which determine the market price of macro risk factors. Analysis of the US nominal term structure data from 1953 to 2006 shows that agents dislike high uncertainty and demand compensation for volatility risks. Also, the time variation of the term premium is driven by the compensation for inflation volatility risk, which is distinct from consumption volatility risk. The central role of inflation volatility risk in explaining the time‐varying term premium is consistent with other empirical evidence including survey data. In contrast, the existing long‐run risks literature emphasizes consumption volatility risk and ignores inflation‐specific time‐varying volatility. The estimation results of this paper suggest that inflation‐specific volatility risk is essential for fitting the time series of the US nominal term structure data. Copyright © 2012 John Wiley & Sons, Ltd.     

Trend growth and learning about monetary policy rules

The paper examines the effect of trend productivity growth on the determinacy and learnability of equilibria under alternative monetary policy rules. Under zero trend inflation we show that the economic structure is isomorphic to that of Bullard and Mitra (2002) and show that under a policy rule that responds to current period inflation and output a higher trend growth rate relaxes the conditions for determinacy and learnability. Results are mixed for other policy rules. Under the expectations-based rule, trend growth tightens the conditions for determinacy but it relaxes the conditions for learnability. Under the lagged-data-based rule, trend growth tightens the conditions for determinacy and learnability. Our analysis shows that lower (higher) trend growth has similar effects as higher (lower) trend inflation in the sense of making inflation more (less) forward-looking. Thus, our results complement previous studies on the role of high trend inflation as a cause of macroeconomic volatility in the U.S. in the 1970s, as this period was also characterized by productivity growth slowdown.     

The asymmetric effects of uncertainty on inflation and output growth

We study the effects of growth volatility and inflation volatility on average rates of output growth and inflation for post‐war US data. Our results suggest that increased growth uncertainty is associated with significantly lower average growth, while higher inflation uncertainty is significantly negatively correlated with lower output growth and lower average inflation. Both inflation and growth display evidence of significant asymmetric response to positive and negative shocks of equal magnitude. Copyright © 2004 John Wiley & Sons, Ltd.     

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.     

Is there a risk–return trade‐off? Evidence from high‐frequency data

This paper examines the intertemporal relation between risk and return for the aggregate stock market using high‐frequency data. We use daily realized, GARCH, implied, and range‐based volatility estimators to determine the existence and significance of a risk–return trade‐off for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macro‐economic variables associated with business cycle fluctuations. We also analyze the risk–return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high‐frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index). Copyright © 2006 John Wiley & Sons, Ltd.     

Optimal Control of Heteroscedastic Macroeconomic Models

This paper analyses the implications of heteroscedasticity for optimal macroeconomic policy and welfare. We find that changes in the variance structure driven by exogenous processes like generalized autoregressive conditional heteroscedasticity (GARCH) affect welfare but not the optimal feedback rule. However, changes in the variance structure driven by state‐dependent processes affect both. We also derive certainty‐equivalent transformations of state‐dependent volatility models that allow standard quadratic dynamic programming algorithms to be employed to study optimal policy. These results are illustrated numerically using a reduced‐form model of the US economy in which changes in volatility are driven by a GARCH process and the rate of inflation. Copyright © 2015 John Wiley & Sons, Ltd.     

UK Macroeconomic Volatility and the Term Structure of Interest Rates*

This study uses a macro‐finance model to examine the ability of the gilt market to predict fluctuations in macroeconomic volatility. The econometric model is a development of the standard ‘square root’ volatility model, but unlike the conventional term structure specification it allows for separate volatility and inflation trends. It finds that although volatility and inflation trends move independently in the short run, they are cointegrated. Bond yields provide useful information about macroeconomic volatility, but a better indicator can be developed by combining this with macroeconomic information.     

POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.     

Inflation Volatility with Regime Switching

This paper presents a new approach to model U.S. inflation dynamics by allowing regime switching in an unobserved components stochastic volatility framework. We use a modified particle filter to construct likelihood and estimate the model using MLE. The number of regimes is determined based on a bootstrap. We find that a model with three regimes and regime‐dependent constant volatilities has superior performance. In addition, we show that since 2000:II, U.S. inflation has entered a regime with moderate volatility where most of the volatility comes from transitory shocks.     

Are macroeconomic density forecasts informative?

We consider whether survey density forecasts (such as the inflation and output growth histograms of the US Survey of Professional Forecasters) are superior to unconditional density forecasts. The unconditional forecasts assume that the average level of uncertainty that has been experienced in the past will continue to prevail in the future, whereas the SPF projections ought to be adapted to the current conditions and the outlook at each forecast origin. The SPF forecasts might be expected to outperform the unconditional densities at the shortest horizons, but it transpires that such is not the case for the aggregate forecasts of either variable, or for the majority of the individual respondents for forecasting inflation.     

New Methods for Forecasting Inflation,Applied to the US*

Models for the 12‐month‐ahead US rate of inflation, measured by the chain‐weighted consumer expenditure deflator, are estimated for 1974–98 and subsequent pseudo out‐of‐sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared with benchmark univariate autoregressive models, and substantial out‐performance is demonstrated including against Stock and Watson's unobserved components‐stochastic volatility model. Three key ingredients to the out‐performance are: including equilibrium correction component terms in relative prices; introducing nonlinearities to proxy state‐dependence in the inflation process and replacing the information criterion, commonly used in VARs to select lag length, with a ‘parsimonious longer lags’ parameterization. Forecast pooling or averaging also improves forecast performance.     

Demographics and Business Cycle Volatility: A Spurious Relationship?

This paper replicates the estimation results of three studies on the impact of the age composition of the labor force on business cycle volatility and investigates whether they signal a meaningful long‐run relationship. We show that both the volatile‐age labor force share variable and the business cycle volatility measure exhibit non‐stationary behavior but find no robust evidence of cointegration. Hence the estimation results reported in the literature may be spurious. This conclusion is further supported by the finding that the strong relationship (i) disappears when cross‐sectional dependence is accounted for using the CCEP estimator and (ii) is highly sensitive to small changes in the composition of the sample, to data revisions, and to the exact definition of the volatile‐age labor share. Copyright © 2016 John Wiley & Sons, Ltd.