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.     

On the loss structure of federal reserve forecasts of output growth

Utilizing recent techniques with comparable private forecasts as benchmarks, we test the rationality of the Federal Reserve forecasts of growth under flexible loss. Our findings for 1984–2006 indicate that these forecasts are rational (efficient) and directionally accurate under symmetric loss and are thus of value when similar cost is assigned to both incorrect upward and downward predictions. Over-predictions (under-predictions) are costly when the Fed responds by implementing an unnecessary contractionary (expansionary) monetary policy and thus causes lower growth (higher inflation). Symmetric loss thus indicates that the Fed is equally concerned about both low growth and high inflation. Further results reveal that the private sector closely replicates the Federal Reserve forecasts released to the public with a five-year lag, suggesting that one can utilize the readily available private data as proxies for the not-yet-released Federal Reserve forecasts.     

Raiders of the lost high-frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting

We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produce for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these data to study whether the staff forecasts efficiently. Prespecified regressions of forecast errors on forecast revisions show the staff's GDP forecasts exhibit time-varying inefficiency between FOMC meetings, and also show some evidence for inefficient inflation forecasts.     

Inflation forecasts and core inflation measures: Where is the information on future inflation?

This paper uses the forecast from a random walk model of inflation as a benchmark to test and compare the forecast performance of several alternatives of future inflation, including the Greenbook forecast by the Fed staff, the Survey of Professional Forecasters median forecast, CPI inflation minus food and energy, CPI weighted median inflation, and CPI trimmed mean inflation. The Greenbook forecast was found in previous literature to be a better forecast than other private sector forecasts. Our results indicate that both the Greenbook and the Survey of Professional Forecasters median forecasts of inflation and core inflation measures may contain better information than forecasts from a random walk model. The Greenbook's superiority appears to have declined against other forecasts and core inflation measures.     

EVALUATING MACROECONOMIC FORECASTS: A CONCISE REVIEW OF SOME RECENT DEVELOPMENTS

Macroeconomic forecasts are frequently produced, widely published, intensively discussed, and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyze some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC), and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes nonstandard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment, and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.     

Modelling multinational telecommunications demand with limited data

Forecasting the diffusion of innovations in the telecommunications sector is a constantly recurring problem for national providers. The problem is characterised by short data series making the estimation of model parameters unreliable. However, the same innovation will be diffusing simultaneously in other national markets, although with a different start date. The use of this cross-sectional data in constructing innovation diffusion models is investigated here. Four models for pooling the cross-sectional data are described and two diffusion models are discussed although only one, the Gompertz model is used throughout. Three innovation data sets are used in the evaluation of the models: digital cellular telephones, ISDN connections and fax connections. The pooled diffusion forecasts proved to be more accurate in several comparisons relative to a naïve benchmark and to individual forecasts when available.     

DSGE forecasts of the lost recovery

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.     

Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve regularly publishes participants’ qualitative assessments of forecast uncertainty, expressed relative to that seen on average in the past. The benchmarks used for these historical comparisons are the average root mean squared forecast errors (RMSEs) made by various private and government forecasters over the past twenty years. This paper documents how these benchmarks are constructed and discusses some of their properties. We draw several conclusions. First, if past performance is a reasonable guide to future accuracy, considerable uncertainty surrounds macroeconomic projections. Second, different forecasters have similar accuracy. Third, estimates of uncertainty about future real activity and interest rates are now considerably greater than prior to the financial crisis; in contrast, estimates of inflation accuracy have changed little. Finally, fan charts, constructed under certain assumptions and viewed in conjunction with the FOMC’s qualitative assessments, provide a reasonable approximation to future uncertainty.     

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.     

Central bank’s forecasts and lack of transparency: An assessment of the effect on private expectations in a large emerging economy

We analyze whether the Central Bank of Brazil’s Inflation Reports projections influences the private’s inflation expectations. Specifically, we investigate how the central bank’s inflation forecasts affect the private sector’s inflation expectations through a qualitative and quantitative examination of the disagreement measure between them. Furthermore, we appraise if the lack of transparency resulting from the difference between the central bank’s inflation forecasts and the realized inflation affects the private’s inflation expectations. Although the findings confirm the previous studies that point out that the central bank transparency can affect the readjustment of market expectations, the results do not rule out the possibility of the central bank’s forecast and private’s inflation expectations being affected reciprocally.     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

GDP forecasts: Informational asymmetry of the SPF and FOMC minutes

This paper emphasizes asymmetric information about the U.S. economy between the FOMC and SPF. Following Stekler and Symington (2016), it extends their text-based FOMC minutes index (FMI) of economic outlook to 1986-2016. Following Ericsson (2016), it employs truncation adjustment indicators and reinterprets the FMI calibrations as the policy-makers forecasts of the GDP growth, which carry information about the staff Greenbook forecasts prepared prior to the bi-quarterly FOMC meetings. Tests confirm unbiasedness and rationality of these forecasts. The encompassing tests indicate that both the FMI and SPF forecasts contain unique information beyond their alternative’s information set and can be weighted equally. The orthogonality tests suggest that the SPF efficiently use all their information set but could gain if the FOMC minutes were published without a lag, while the policy-makers rely mostly on their projections made earlier in the meetings, and could benefit from incorporating the already published SPF forecasts.     

An evaluation of the forecasts of the federal reserve: a pooled approach

The Federal Reserve Greenbook forecasts of real GDP, inflation and unemployment are analysed for the period 1974–1997. We consider whether these forecasts exhibit systematic bias, and whether efficient use is made of information, that is, whether revisions to these forecasts over time are predictable. Rather than analyse the forecasts separately for each horizon of interest, we discuss and implement methods that pool information over horizons. We conclude that there is evidence of systematic bias and of forecast smoothing of the inflation forecasts. Copyright © 2007 John Wiley & Sons, Ltd.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.     

The evolution of U.S. monetary policy: 2000–2007

A vector autoregression with time-varying parameters is used to characterize changes in Federal Reserve policy that occurred from 2000 through 2007 and describe how they affected the performance of the U.S. economy. Declining coefficients in the model׳s estimated policy rule point to a shift in the Fed׳s emphasis away from stabilizing inflation over this period. More importantly, however, the Fed held the federal funds rate persistently below the values prescribed by this rule. Under this more discretionary policy, inflation overshot its target and the funds rate followed a path reminiscent of the “stop-go” pattern that characterized Fed behavior prior to 1979.     

Time‐varying yield curve dynamics and monetary policy

Monetary policy, the yield curve and the private sector behaviour of the US economy are modelled as a time‐varying structural vector autoregression. The monetary policy shocks of the early 1980s explain a large portion of the persistence of inflation and the level of the term structure. Changes in inflation expectations implied by the yield curve account for the persistence of the federal funds rate. Failures of the expectations hypothesis are rare, and coincided with the credibility building of Paul Volcker's Fed tenure at the beginning of the 1980s and the sequence of consecutive policy rate cuts around the time of the early 1990s recession. Copyright © 2009 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.     

Forecast Rationality Tests in the Presence of Instabilities,with Applications to Federal Reserve and Survey Forecasts

This paper proposes a framework to implement regression‐based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and efficiency tests, commonly referred to as tests of forecast rationality. Our framework is general: it can be applied to model‐based forecasts obtained either with recursive or rolling window estimation schemes, as well as to forecasts that are model free. The proposed tests provide more evidence against forecast rationality than previously found in the Federal Reserve's Greenbook forecasts as well as survey‐based private forecasts. It confirms, however, that the Federal Reserve has additional information about current and future states of the economy relative to market participants. Copyright © 2015 John Wiley & Sons, Ltd.     

Analyzing Fed behavior using a dynamic Taylor-type rule

This paper estimates a dynamic Taylor-type model in order to analyze the behavior of the Federal Reserve under the leadership of three different Fed chairs: Arthur Burns, Paul Volcker, and Alan Greenspan. The model proves useful in distinguishing the conduct of monetary policy under each. Burns is found to have paid little attention to inflation and inflationary pressures. Volcker focused on reducing actual inflation during his first term while turning his attention to the GDP gap and inflationary pressures in his second term. Greenspan emphasized preemptive strikes against inflation as indicated by high weights attributed to the GDP gap and expected inflation.     

How reliable are forecasts of GDP growth and inflation for countries with limited coverage?

This paper analyses the performance of GDP growth and inflation forecasts for 25 transition countries between 1994 and 2007, as provided by 13 international institutions, including multilateral, private and academic forecasters. The empirical results show that there is a positive correlation between the number of forecasters covering a given country and the forecast accuracy. Simple combined forecasts are shown to be unbiased and more accurate than most of the individual forecasters, although also inefficient. However, only a few institutions provide efficient and unbiased forecasts, with just one out of 13 forecasters providing both unbiased and efficient forecasts of both GDP growth and inflation in the observed period. The directional analysis shows a correct forecast of the change in the forecast indicator in over two thirds of cases. However, the eventual outcome is within the range of available forecasts in less than half of the cases, with more than 40% of outcomes for GDP growth above the highest forecast. Encouragingly, forecasts are shown to be improving over time and becoming more accurate with the increase in the number of forecasting institutions – forecast accuracy measured by mean absolute error improves by 0.3 percentage points for growth and by 0.2 percentage points for inflation for each additional institution providing forecasts.