The effects of FX-interventions on forecasters disagreement: A mixed data sampling view

We analyse the heterogeneity of exchange rate forecasts by a panel of professional forecasters. Adopting the view that forecasters’ economic behaviour is such that they constantly collect, process and analyse relevant information when producing forecasts, we apply a Mixed-Data Sampling (MIDAS) regression approach. This enables us to explore the roles played by key drivers for which available data are at different frequencies from forecast disagreement. Examining the Colombian peso/U.S. dollar exchange rate, we find that central bank intervention is most effective in reducing heterogeneity in the very short-run, and when conducted against a background of high exchange rate volatility.     

Forecasting economic activity with mixed frequency BVARs

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.     

Informational efficiency tests of quarterly macroeconometric GNP forecasts from 1976 to 1985

The objective of the paper is to compare the informational efficiency of five macroeconometric and one statistical quarterly forecasting models. The results suggest that the forecasters inefficiently utilize readily available economic information. The qualitative effect for a particular information variable is the same across all forecasters exhibiting inefficiency. Further, the magnitude on coefficients of significant information variables are quite close. In particular, real GNP forecasts appear to not fully incorporate information about lagged M 1 growth and lagged changes in housing starts. Deflator forecasts can be improved by more fully specifying the degree of slackness in the economy as captured by capacity utilization and changes in the labor market.     

Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7

In this paper, we use survey data to analyze the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not previously been analyzed in the literature. We provide evidence on the properties of forecasts for all G7-countries and for four different macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large differences in the performances of forecasters, not only across countries but also across different macroeconomic variables. In general, the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.     

Forecast revisions of Mexican inflation and GDP growth

We analyze the forecasts of inflation and GDP growth contained in the Banco de México’s Survey of Professional Forecasters for the period 1995–2009. The forecasts are for the current and the following year, and comprise an unbalanced three-dimensional panel with multiple individual forecasters, target years, and forecast horizons. The fixed-event nature of the forecasts enables us to examine their efficiency by looking at the revision process. The panel structure allows us to control for aggregate shocks and to construct a measure of the news that impacted expectations in the period under study. We find that respondents anchor to their initial forecasts, updating their revisions smoothly as they receive more information. In addition, they do not seem to use publicly-known information in an efficient manner. These inefficiencies suggest clear areas of opportunity for improving the accuracy of the forecasts, for instance by taking into account the positive autocorrelation found in forecast revisions.     

How likely is a UK recession after Brexit?

• In the wake of the UK Brexit vote, forecasters have rushed to downgrade their growth forecasts for the UK, with some now expecting a recession. Using the Oxford Economics' Global Economic Model, we examine how likely a recession is by looking at the shocks the UK economy faces and the policy responses. We conclude that while a sharp slowdown is likely – in line with our own new forecasts – a recession is unlikely.
• Many UK forecasters are now predicting a recession in 2017, even though ‘stand‐alone’ recessions in industrial countries are rare. Our forecast is less downbeat. The UK faces a series of negative shocks including to consumers and business confidence, but growth will be supported by the weaker sterling and likely policy responses.
• Using the Oxford Economics' Global Economic Model, we show that to shift our baseline forecast of growth of 1.1% next year to zero would require a very severe negative confidence shock. Our new baseline already assumes a shock equivalent to one‐third of that seen in the global financial crisis (GFC). All else being equal, the shock would have to be around two‐thirds of that in the GFC to cut GDP growth to zero in 2017.
• Our new baseline also does not incorporate all the possible policy levers the UK can employ. We currently assume the Bank Rate drops to zero, but if a ‘rescue package’ of £75 billion of QE and a fiscal stimulus equal to 1% of GDP was also added, then the shock to confidence needed to get zero GDP growth would have to be similar to that seen in the GFC. We do not consider this likely given the scale of financial stress and credit restriction that occurred globally at the time of the GFC.

     

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.     

Use and misuse of information in supply chain forecasting of promotion effects

Demand forecasting is critical to sales and operations planning (S&OP), but the effects of sales promotions can be difficult to forecast. Typically, a baseline statistical forecast is judgmentally adjusted on receipt of information from different departments. However, much of this information either has no predictive value or its value is unknown. Research into base rate discounting has suggested that such information may distract forecasters from the average uplift and reduce accuracy. This has been investigated in situations in which forecasters were able to adjust the statistical forecasts for promotions via a forecasting support system (FSS). In two ecologically valid experiments, forecasters were provided with the mean level of promotion uplift, a baseline statistical forecast, and quantitative and qualitative information. However, the forecasters were distracted from the base rate and misinterpreted the information available to them. These findings have important implications for the design of organizational S&OP processes, and for the implementation of FSSs.     

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.     

Financial nowcasts and their usefulness in macroeconomic forecasting

Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.     

Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.     

Asymmetry in unemployment rate forecast errors

Asymmetries in unemployment dynamics have been observed in the time series of a number of countries, including the United States. This paper studies asymmetries in unemployment rate forecast errors. We consider conditions under which optimal forecasts will display asymmetrically-distributed errors and how the degree of asymmetry might vary with the forecast horizon. Using data from the U.S. Survey of Professional Forecasters and the Federal Reserve Greenbook, we find substantial evidence of forecast error asymmetry, which tends to increase with the forecast horizon; we also find noteworthy differences in forecasts from these two sources. The results give insight into the abilities of professional forecasters to adapt their forecasts to asymmetry in underlying processes.     

Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information

How did DSGE model forecasts perform before, during and after the financial crisis, and what type of off-model information can improve the forecast accuracy? We tackle these questions by assessing the real-time forecast performance of a large DSGE model relative to statistical and judgmental benchmarks over the period from 2000 to 2013. The forecasting performances of all methods deteriorate substantially following the financial crisis. That is particularly evident for the DSGE model’s GDP forecasts, but augmenting the model with a measure of survey expectations made its GDP forecasts more accurate, which supports the idea that timely off-model information is particularly useful in times of financial distress.     

Central banks’ interest rate projections and forecast coordination

We apply a simple test to study the effect of the publication of central banks’ interest-rate projections on the coordination of private-sector interest-rate forecasts. Our results indicate that the publication of interest-rate projections does not lead private-sector forecasters to coordinate their forecasts. In fact, private-sector forecasters rather seem to anti-coordinate, that is, they scatter their forecasts around a consensus forecast or around a central bank's interest-rate projections.     

Information rigidity in growth forecasts: Some cross-country evidence

We document information rigidity in forecasts of real GDP growth in 46 countries over the past two decades. We also investigate: (i) whether rigidities differ across countries, particularly between advanced countries and emerging markets; (ii) whether rigidities are lower around turning points in the economy, such as in times of recessions and crises; and (iii) how quickly forecasters incorporate news about growth in other countries into their growth forecasts, with a focus on the way in which advanced countries’ growth forecasts incorporate news about emerging market growth, and vice versa.     

Do macroeconomic forecasters use macroeconomics to forecast?

Faced with an uncertain future, forecasters often rely on textbook relationships to build a coherent narrative for their macroeconomic forecasts. We focus on two cornerstones of modern macroeconomics – Okun’s law and the Phillips curve – and examine whether or not professionals forecast in a way that is consistent with these. Using microdata from the US, Euro Area, and UK surveys of professional forecasters, we examine forecasts over the period 1981-2017 at the level of the individual and across different time horizons. Our findings show that the majority of forecasters produce their forecasts in a manner that is consistent with macroeconomic theory.     

Differing behaviours of forecasters of UK GDP growth

The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus.     

Evaluating the conditionality of judgmental forecasts

We propose a framework for evaluating the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are incorporated faithfully into the remainder of the forecast. We consider whether the Greenbook, Blue Chip survey and Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be incorporated efficiently into the forecasts of other economic variables.     

How quickly do forecasters incorporate news? Evidence from cross‐country surveys

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the forecast data used are multi‐dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance–covariance matrix due to propagation of shocks across countries and economic linkages among them. Efficiency is rejected for all 18 countries: forecast revisions show a high degree of serial correlation. We then develop a framework for characterizing the nature of the inefficiency in forecasts. For a smaller set of countries, the G‐7, we estimate a VAR model on forecast revisions. The degree of inefficiency, as manifested in the serial correlation of forecast revisions, tends to be smaller in forecasts of the USA than in forecasts for European countries. Our framework also shows that one of the sources of the inefficiency in a country's forecasts is resistance to utilizing foreign news. Thus the quality of forecasts for many of these countries can be significantly improved if forecasters pay more attention to news originating from outside their respective countries. This is particularly the case for Canadian and French forecasts, which would gain by paying greater attention than they do to news from the USA and Germany, respectively. Copyright © 2006 John Wiley & Sons, Ltd.     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.