An Inflation‐Predicting Measure of the Output Gap in the Euro Area

Using a small Bayesian dynamic factor model of the euro area, we estimate the deviations of output from its trend that are consistent with the behavior of inflation. We label these deviations the output gap. In order to pin down the features of the model, we evaluate the accuracy of real‐time inflation forecasts from different model specifications. The version that forecasts inflation best implies that after the 2011 sovereign debt crisis, the output gap in the euro area has been much larger than the official estimates. Versions featuring a secular stagnation‐like slowdown in trend growth, and hence a small output gap after 2011, do not adequately capture the inflation developments.     

The term structure of inflation expectations

We use information in the term structure of survey-based forecasts of inflation to estimate a factor hidden in the nominal yield curve. We construct a model that accommodates forecasts over multiple horizons from multiple surveys and Treasury real and nominal yields by allowing for differences between risk-neutral, subjective, and objective probability measures. We establish that model-based inflation expectations are driven by inflation, output, and one latent factor. We find that this factor affects inflation expectations at all horizons but has almost no effect on the nominal yields; that is, the latent factor is hidden. We show that this hidden factor is not related to either current and past inflation or the standard set of macro variables studied in the literature. Consistent with the theoretical property of a hidden factor, our model outperforms a standard macro-finance model in its forecasting of inflation and yields.     

A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations

We study the evolution of U.S. inflation by means of a new noncausal autoregressive model with time‐varying parameters that outperforms the corresponding causal and constant‐parameter noncausal models in terms of fit and forecast accuracy. Our model also beats the unobserved component stochastic volatility (UCSV) model, one of the best‐performing univariate inflation forecasting models, in terms of both point and density forecasts. We also show how the new Keynesian Phillips curve can be estimated based on our noncausal model. Both expected and lagged inflation turn out important, but the former dominates in determining the current inflation.     

Does Global Liquidity Help to Forecast U.S. Inflation?

We construct a measure of global liquidity using the growth rates of broad money for the G7 economies. Global liquidity produces forecasts of U.S. inflation that are significantly more accurate than the forecasts based on U.S. money growth, Phillips curve, and autoregressive and moving average models. The marginal predictive power of global liquidity is strong at 3-year horizons. Results are robust to alternative measures of inflation.     

预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。     

Asymmetry,Complementarities, and State Dependence in Federal Reserve Forecasts

Forecasts are a central component of policymaking; the Federal Reserve's forecasts are published in a document called the Greenbook. Previous studies of the Greenbook's inflation forecasts have found them to be rationalizable but asymmetric if considering particular subperiods, for example, before and after the Volcker appointment. In these papers, forecasts are analyzed in isolation, assuming policymakers value them independently. We analyze the Greenbook forecasts in a framework in which the forecast errors for different variables are allowed to interact. We find that allowing the losses to interact makes the unemployment forecasts virtually symmetric, the output forecasts symmetric prior to the Volcker appointment, and the inflation forecasts symmetric after the onset of the Great Moderation.     

Term structure dynamics with macro-factors using high frequency data

This paper empirically studies the role of macro-factors in explaining and predicting daily bond yields. In general, macro-finance models use low-frequency data to match with macroeconomic variables available only at low frequencies. To deal with this, we construct and estimate a tractable no-arbitrage affine model with both conventional latent factors and macro-factors by imposing cross-equation restrictions on the daily yields of bonds with different maturities, credit risks, and inflation indexation. The estimation results using both the US and the UK data show that the estimated macro-factors significantly predict actual inflation and the output gap. In addition, our daily macro-term structure model forecasts better than no-arbitrage models with only latent factors as well as other statistical models.     

A comparison of inflation forecasts

Interest rate models provide slightly better monthly forecasts and substantially better eight- and fourteen-month forecasts of inflation than a univariate time series model. The Livingston surveys underestimate eight- and fourteen-month inflation rates, especially during the high inflation period of 1978–81. In contrast, eight- and fourteen-month inflation forecasts extrapolated from one-month interest rates show little bias and track ex post eight- and fourteen-month inflation rates better than the survey forecasts.     

Inflation dynamics: A cross-country investigation

We document that “persistent and lagged” inflation (with respect to output) is a world-wide phenomenon in that these short-run inflation dynamics are highly synchronized across countries. In particular, the average cross-country correlation of inflation is significantly and systematically stronger than that of output, while the cross-country correlation of money growth is essentially zero. We investigate whether standard monetary models driven by monetary shocks are consistent with the empirical facts. We find that neither the new Keynesian sticky-price model nor the sticky-information model can fully explain the data. An independent contribution of the paper is to provide a simple solution technique for solving general equilibrium models with sticky information.     

Monetary Pressures and Inflation Dynamics in Turkey: Evidence from P-Star Model

This paper uses the P-star model to explain inflation dynamics in Turkey. In P-star models, money determines the price gap, which is postulated to measure the pressure on prices in an economy. This pressure emerges when output is above the potential, the interest rate is lower than the natural rate, or there is pure excess money in the economy. The estimation results with the Turkish data show that the price gap contains considerable information for explaining inflation dynamics. Moreover, the model selection criterion that compares the empirical performance of the P-star model with the new classical Phillips curve relation favors the P-star model over the Phillips curve relationship. We conclude that money is efficacious in predicting risk in price stability in Turkey.     

Backtesting extreme value theory models of expected shortfall

We use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day horizon we combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.     

The impact of banking sector stability on the real economy

This article studies the relationship between the degree of banking sector stability and the subsequent evolution of real output growth and inflation. Adopting a panel VAR methodology for a sample of 18 OECD countries, we find a positive link between banking sector stability and real output growth. This finding is predominantly driven by periods of instability rather than by very stable periods. In addition, we show that an unstable banking sector increases uncertainty about future output growth. No clear link between banking sector stability and inflation seems to exist. We then argue that the link between banking stability and real output growth can be used to improve output growth forecasts. Using Fed forecast errors, we show that banking sector stability (instability) results in a significant underestimation (overestimation) of GDP growth in the subsequent quarters.     

Survey Measures of Expected Inflation and the Inflation Process

The accuracy of inflation forecasts obtained from household and professional surveys has deteriorated noticeably of late, to the extent that a simple autoregressive specification outperforms survey forecasts. The decline in (absolute and relative) accuracy has taken place at about the same time as an apparent change in the inflation process. Projections of household forecasts on realized inflation suggests that households have not recognized this change. For the professionals, projections of expected inflation on headline inflation have changed, but on core inflation have not. By contrast, projections of realized headline inflation on core have changed sharply.     

Are central banks' projections meaningful?

Central banks' projections – i.e. forecasts conditional on a given interest rate path – are often criticized on the grounds that their assumptions are inconsistent with the existence of a unique equilibrium in many forward-looking models. The present paper describes three alternative approaches to constructing projections that are not subject to the above criticism, using the New Keynesian model as a reference framework. The three approaches are shown to generate different projections for inflation and output, even though they imply an identical path for the interest rate. The latter result calls into question the meaning and usefulness of such projections.     

RECENT EVIDENCE ON THE ACCURACY AND RATIONALITY OF POPULAR INFLATION FORECASTS

This paper compares the accuracy and examines the rationality of inflation forecasts extracted from U.S. Treasury bill yields, the mean forecast of surveys conducted by the Institute for Social Research, and the predictions from a rolling time-series model. Measures of realized inflation include both the Consumer Price Index reported during the period and a revised CPI series (CPIX) that replaces the mortgage interest rate with the recently adopted “rental equivalence” component. Inferences about accuracy and rationality depend upon both the measure of inflation and the period studied. The findings suggest that survey forecasts should be used when turbulent variation in the unobservable expected real rate of interest obscures the measurement of the market's expectation of inflation.     

An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms

I consider the possibility that respondents to the Survey of Professional Forecasters round their probability forecasts of the event that real output will decline in the future, as well as their reported output growth probability distributions. I make various plausible assumptions about respondents’ rounding practices, and show how these impinge upon the apparent mismatch between probability forecasts of a decline in output and the probabilities of this event implied by the annual output growth histograms. I find that rounding accounts for about a quarter of the inconsistent pairs of forecasts.     

Do Macroforecasters Herd?

We show that typical tests of whether forecasters herd will falsely indicate herding behavior for a variety of types of behavior and forecasting environments that give rise to disagreement among forecasters. We establish that forecasters will appear to herd if differences between them reflect noise as opposed to private information, or if they arise from informational rigidities. Noise can have a behavioral interpretation and if so will depend on the behavioral model under consideration. An application of the herding tests to U.S. quarterly survey forecasts of inflation and output growth data 1981–2013 does not support herding behavior.     

Forecast uncertainty and the Taylor rule

In this paper, we derive a modification of a forward-looking Taylor rule by integrating two variables that measure the uncertainty of inflation and GDP growth forecasts into an otherwise standard New Keynesian model. We show that certainty-equivalence in New Keynesian models is a consequence of log-linearization and that a second-order Taylor approximation leads to a reaction function that includes the uncertainty of macroeconomic expectations. To test the model empirically, we use the standard deviation of individual forecasts around the median Consensus Forecast as a proxy for forecast uncertainty. Our sample covers the euro area, the United Kingdom, and the United States for the period 1990Q1–2016Q4. We find that the Bank of England and the European Central Bank have a significantly negative reaction to inflation forecast uncertainty. Our findings also reveal that the Federal Reserve (Bank of England) lowers (raises) its interest rate in response to higher GDP growth forecast uncertainty. We conclude by offering some implications for optimal monetary policy rules and central bank watchers.     

Long-Term Earnings Forecasts in the Electric Utility Industry: Accuracy and Valuation Implications

This paper integrates research on the accuracy of alternative long-term earnings forecasts, the gain in accuracy achievable from combining various forecasts, and the power of different long-term earnings forecasts to explain stock prices. The tests are performed on 82 electric utility firms because of the relative homogeneity of accounting data in that industry and because of the importance of the findings for the determination of the cost of capital in a regulatory proceeding. The results are consistent with earlier research findings that analyst forecasts of long-term earnings growth are more accurate than forecasts from extrapolative models. Combined forecasts applied to out-of-sample data, however, did not result in markedly improved forecasting accuracy. Finally, valuation tests of alternative forecasting techniques offered strong evidence that investors place the greatest weight on forecasts from Value Line.