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.     

Investigating the inefficiency of the CBO’s budgetary projections

This paper evaluates the efficiency of budgetary projections issued by the Congressional Budget Office (CBO) from 1984 to 2016 and investigates the cause of their inefficiency. The efficiency of both its budgetary and macroeconomic projections is rejected in many cases, especially for revenue projections. A comparison of forecast evaluations suggests that the inefficiency of revenue projections is likely due to the inefficiency of the underlying macroeconomic projections. By adjusting budgetary projections by the CBO in real time using macroeconomic forecasts by the Survey of Professional Forecasters, the accuracy of the former projections can be significantly improved in some cases, by up to 26% in terms of the root-mean-square prediction error.     

Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters

Part of a prediction is the judgment applied by the forecaster. This judgmental input may be affected by the forecaster’s mood swings, which have been shown to affect, for example, stock market returns. The present paper analyzes the extent to which mood (approximated by the development in sentiment indicators) affects macroeconomic prediction errors; i.e., whether it explains part of the prediction bias. The evidence suggests that mood can explain part of the error in inflation and output growth predictions, and hence, that anomalies should be taken into account when trying to understand expectation formation and assess the uncertainty related to private forecasters’ point predictions.     

Five dimensions of the uncertainty–disagreement linkage

We analyze the relationship between forecaster disagreement and macroeconomic uncertainty in the Euro area using data from the European Central Bank’s Survey of Professional Forecasters for the period 1999Q1–2018Q4 and find that disagreement is generally a poor proxy for uncertainty. However, the strength of this link varies with the dispersion statistic employed, the choice of either the point forecasts or the histogram means for calculating disagreement, the outcome variable considered and the forecast horizon. In contrast, distributional assumptions do not appear to be very influential. The relationship is weaker in subsamples before and after the outbreak of the Great Recession. Accounting for the forecasters’ entry to and exit from the survey has little impact on the results. We also show that survey-based uncertainty is associated with overall policy uncertainty, whereas forecaster disagreement is related more closely to the expected fluctuations on financial markets.     

Economic policy uncertainty and industry risk on China’s stock market

We propose a dynamic mixture Copula with time-varying weight, which is endowed with generalized autoregressive score dynamics. Based on this model, we portray the lower-tail dependence between the return of WIND first-level industry and CSI-300 index as a proxy variable for the industry risk in China’s stock market, and use the VAR-GARCH-in-mean model based on BEKK-GARCH to deconstruct the different impact of the economic policy uncertainty (EPU) on industry risk of the first and second moments in terms of four policy categories, namely fiscal policy, monetary policy, trade policy, and foreign exchange rate and capital account policy. The results are followed. Firstly, the risk of Consumer Discretionary is averagely the highest, while the risk of Utilities remains the lowest. Secondly, category-specific EPU has no significant mean spillover to the risk of overall industries, while the variance spillover is significant for all the cases. Thirdly, except for Real Estate, the GARCH-in-mean effect is not significant of EPU on industry risks. Further more, all those three kinds of impact show industrial heterogeneities. To avoid systemic risks, we advise that the issue of economic policy should be forward-looking, consistent, and targeted, especially for sensitive industries.     

Density forecasting through disaggregation

In this paper, the revised expectations model (REM) is developed to incorporate economic agents’ price expectation formation effects. With this incorporation, two models, an aggregate one sector model and a disaggregated multi-sector model, are estimated and used in density forecasting of the US real GDP growth rate. The experiment shows that use of the disaggregated version of the model, which incorporates price expectation effects along with modern Bayesian MCMC estimation and prediction techniques, produces more precise density forecasts than those yielded by either an aggregate version or benchmark forecasting models.     

Connectedness of commodity,exchange rate and categorical economic policy uncertainties — Evidence from China

This paper provides a fresh perspective to explore the network correlations among commodity, exchange rate, and categorical economic policy uncertainties (EPU) in China. We try to contribute to the literature by examining the spillover mechanism with a relatively novel connectedness network using the monthly data over the period between June 2006 and January 2021. Our results suggest that prior to the recession, China’s commodity price is subject to greater spillovers from the exchange rate than recessions. The domestic commodity prices are more sensitive to monetary policy uncertainty and fiscal policy uncertainty. The occurrence of COVID-19 revises the dominance in the system from monetary policy uncertainty and fiscal policy uncertainty to trade policy uncertainty.     

The spillover effects of biofuel policy on participation in the conservation reserve program

This paper studies the spillover effects of rising biofuel production on participation in the Conservation Reserve Program. Landowner participation decisions are modeled using a real options framework. We develop a land use decision model that captures biofuel-driven structural changes in market demand and derive threshold conditions that trigger participation in the program. We then quantify the impacts of biofuel production on participation at both the national and state levels using Monte Carlo simulations. The model is also used to analyze how changes in the persistence of the biofuel production boom and in the volatility of farming returns affect conservation participation decisions. Policy implications of the results are discussed.     

Kernel-based calibration diagnostics for recession and inflation probability forecasts

A probabilistic forecast is the estimated probability with which a future event will occur. One interesting feature of such forecasts is their calibration, or the match between the predicted probabilities and the actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. We show here that we can do this without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating the predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on the calibration error in a number of economic applications, including the prediction of recessions and inflation, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. The outcomes are evaluated using both first-release outcome measures and subsequent revised data. We find substantial evidence of incorrect calibration in professional forecasts of recessions and inflation from the SPF, as well as in real-time inflation forecasts from a variety of output gap models.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.     

The effect of corruption on environmental performance: Does spatial dependence play a role?

Different from previous studies that mainly focused on conventional estimation techniques, this paper explores the role of spatial dependence in the effect of corruption on environmental performance by using a spatial panel data model. Our results show that the direct effects, indirect effects and total effects of corruption on environmental performance are negative and highly significant, implying that corruption is detrimental to environmental performance. More importantly, we find that there is a significant spatial spillover effect between corruption and environmental performance, namely being surrounded by highly corrupt countries has a negative impact on the local country’s environmental performance. Our results also suggest that a larger population density will worsen the environment, while urbanization has a significant positive impact. These results provide some important implications for policymakers seeking to improve their environmental performance.