Key sources of disagreement among economic forecasters are identified by using data on cross-sectional dispersion in forecasters’ long- and short-run predictions of macroeconomic variables. ...Dispersion among forecasters is highest at long horizons where private information is of limited value and lower at short forecast horizons. Moreover, differences in views persist through time. Such differences in opinion cannot be explained by differences in information sets; our results indicate they stem from heterogeneity in priors or models. Differences in opinion move countercyclically, with heterogeneity being strongest during recessions where forecasters appear to place greater weight on their prior beliefs.
Macroeconomic forecasting in a multi‐country context Bai, Yu; Carriero, Andrea; Clark, Todd E. ...
Journal of applied econometrics (Chichester, England),
September/October 2022, Volume:
37, Issue:
6
Journal Article
Peer reviewed
In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical ...results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe prior, is very useful in forecasting inflation. It also has the best density forecast performance for output growth and the interest rate. Multi‐country models generally improve on the forecast accuracy of single‐country models.
Local prediction pools Oelrich, Oscar; Villani, Mattias; Ankargren, Sebastian
Journal of forecasting,
January 2024, 2024-01-00, 20240101, 2024, Volume:
43, Issue:
1
Journal Article
Peer reviewed
Open access
We propose local prediction pools as a method for combining the predictive distributions of a set of experts conditional on a set of variables believed to be related to the predictive accuracy of the ...experts. This is done in a two‐step process where we first estimate the conditional predictive accuracy of each expert given a vector of covariates—or pooling variables—and then combine the predictive distributions of the experts conditional on this local predictive accuracy. To estimate the local predictive accuracy of each expert, we introduce the simple, fast, and interpretable caliper method. Expert pooling weights from the local prediction pool approaches the equal weight solution whenever there is little data on local predictive performance, making the pools robust and adaptive. We also propose a local version of the widely used optimal prediction pools. Local prediction pools are shown to outperform the widely used optimal linear pools in a macroeconomic forecasting evaluation and in predicting daily bike usage for a bike rental company.
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.
Anthropogenic fragmentation of habitat is considered to be a critical factor contributing to the decline of species. However, a general consensus on the degree to which habitat loss and what has been ...called “habitat fragmentation per se” contribute to the loss of species diversity has not yet emerged. For empirical and theoretical reasons the topic has recently attracted renewed attention, thus reviving the “single large or several small” (SLOSS) debate. To study the effect of fragmentation per se, we use a spatially explicit and continuous, competitively neutral simulation model with immigration from a regional pool. The model accounts for the influence of ecological drift and intrafragment species clustering (due to limited dispersal) on local (plot) and global (landscape) diversity. We find that fragmentation increases global diversity but decreases local diversity, prominently so if fragments become more isolated. Cluster formation is a key mechanism reducing local diversity. By adding external disturbance events that lead to the occasional extinction of entire communities in habitat fragments, we show that the combined effect of such extinctions and cluster formation can create nonlinear interactive effects of fragmentation and fragment isolation on diversity patterns. We conclude that while in most cases fragmentation will decrease local and increase landscape diversity, universal predictions concerning the SLOSS debate should be taken with care.
High-frequency monitoring of growth at risk Ferrara, Laurent; Mogliani, Matteo; Sahuc, Jean-Guillaume
International journal of forecasting,
04/2022, Volume:
38, Issue:
2
Journal Article
Peer reviewed
Open access
Monitoring changes in financial conditions provides valuable information on the contribution of financial risks to future economic growth. For that purpose, central banks need real-time indicators to ...promptly adjust their policy stance. In this paper, we extend the quarterly growth-at-risk (GaR) approach of Adrian et al. (2019) by accounting for the high-frequency nature of financial conditions indicators. Specifically, we use Bayesian mixed-data sampling (MIDAS) quantile regressions to exploit the information content of both a financial stress index and a financial conditions index, leading to real-time high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) displays good GDP nowcasting properties, (ii) can provide an early signal of GDP downturns, and (iii) allows day-to-day assessment of the effects of monetary policies. During the first six months of the Covid-19 pandemic period, it has provided a timely measure of the tail risks to euro-area GDP.
Climate change and natural disturbances are catalysing forest transitions to different vegetation types, but whether these new communities are resilient alternate states that will persist for decades ...to centuries is not known. Here, we test how changing climate, disturbance and biotic interactions shape the long‐term fate of a deciduous broadleaf forest type that replaces black spruce after severe wildfires in interior Alaska, USA.
We simulated postfire deciduous forest that replaced black spruce after severe fires in 2004 for tens to hundreds of years under different climate scenarios (contemporary, mid 21st century, late 21st century), fire return intervals (11–250 years), distances to seed source (50–1,000 m) and browsing intensities (background, moderate, chronic). We identified combinations of conditions where deciduous forest remained the dominant vegetation type and combinations where it returned to black spruce forest, transitioned to mixed forest (where deciduous species and black spruce co‐dominate) or converted to nonforest.
Deciduous forest persisted in 86% of simulations and was most resilient if fire return intervals were short (≤50 years). When transitions to another vegetation type occurred, mixed forest was most common, particularly when fire return intervals were long (>50 years) and the nearest seed source was 500 m or farther. Moderate and chronic browsing also reduced deciduous sapling growth and survival, helping black spruce compete if fire return intervals were long and seed source was distant. Dry soils occasionally caused conversion to nonforest following short‐interval fire when simulations were forced with a late 21st‐century climate scenario that projects warming and increased vapor pressure deficit. Return to black spruce forest almost never occurred.
Synthesis. Conversion from black spruce to deciduous forest is already underway at regional scales in interior Alaska, and similar transitions have been widely observed throughout the North American boreal biome. We show that this boreal deciduous forest type is likely a resilient alternate state that will persist through the 21st century, which is important, because future vegetation outcomes will shape biophysical feedbacks to regional climate and influence subsequent disturbance regimes.
Conversion from black spruce to deciduous forest is already underway at regional scales in interior Alaska, and similar transitions have been widely observed throughout the North American boreal biome. We show that this boreal deciduous forest type is likely a resilient alternate state that will persist through the 21st century, which is important, because future vegetation outcomes will shape biophysical feedbacks to regional climate and influence subsequent disturbance regimes. Photo by Ann K. Olsson.
Abstract The heat generated by the spindle leads to thermal deformation, which can seriously affect machining accuracy. ANSYS software is used to establish the heat-fluid-solid coupling simulation ...model of the electric spindle. Temperature rise and thermal error experiments of the electric spindle are carried out. We modified the theoretical and empirical values used in the simulation according to the experimental data. Experimentally, it is demonstrated that the precision of the simulation increased by 27% after modification.
If the links between credit markets and real economy tighten in a crisis, financial indicators might be particularly useful in forecasting the macroeconomic outcomes associated with episodes of ...financial distress. We examine this conjecture by using a range of linear and nonlinear VAR models to generate predictive distributions for US inflation and industrial production growth. Financial variables display significant predictive power over the Great Recession period, particularly if used within a threshold model that captures the structural break associated to the crisis. However, the Great Recession is unique: financial information and thresholds make little difference for forecasting prior to 2008.
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