184 research outputs found
Understanding and forecasting aggregate and disaggregate price dynamics
The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is generally accepted that forecast aggregation is an empirical issue. Empirical results in the literature often go unexplained. This leaves forecasters in the dark when confronted with the option of forecast aggregation. We take our empirical exercise a step further by considering the underlying issues in more detail. We analyse two price datasets, one for the United States and one for the Euro Area, which have distinctive dynamics and provide a guide to model choice. We also consider multiple levels of aggregation for each dataset. The models include an autoregressive model, a factor augmented autoregressive model, a large Bayesian VAR and a time-varying model with stochastic volatility. We find that once the appropriate model has been found, forecast aggregation can significantly improve forecast performance. These results are robust to the choice of data transformation. JEL Classification: E17, E31, C11, C38Aggregation, forecasting, inflation
Understanding and Forecasting Aggregate and Disaggregate Price Dynamics
The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is gener- ally accepted that forecast aggregation is an empirical issue. Empirical results in the literature often go unexplained. This leaves forecasters in the dark when confronted with the option of forecast aggregation. We take our empirical exercise a step further by considering the underlying issues in more detail. We analyse two price datasets, one for the United States and one for the Euro Area, which have distinctive dynamics and provide a guide to model choice. We also consider multiple levels of aggregation for each dataset. The models include an autoregressive model, a factor augmented autoregressive model, a large Bayesian VAR and a time-varying model with stochastic volatility. We find that once the appropriate model has been found, forecast aggrega- tion can significantly improve forecast performance. These results are robust to the choice of data transformation.
Algorithmic Robust Forecast Aggregation
Forecast aggregation combines the predictions of multiple forecasters to
improve accuracy. However, the lack of knowledge about forecasters' information
structure hinders optimal aggregation. Given a family of information
structures, robust forecast aggregation aims to find the aggregator with
minimal worst-case regret compared to the omniscient aggregator. Previous
approaches for robust forecast aggregation rely on heuristic observations and
parameter tuning. We propose an algorithmic framework for robust forecast
aggregation. Our framework provides efficient approximation schemes for general
information aggregation with a finite family of possible information
structures. In the setting considered by Arieli et al. (2018) where two agents
receive independent signals conditioned on a binary state, our framework also
provides efficient approximation schemes by imposing Lipschitz conditions on
the aggregator or discrete conditions on agents' reports. Numerical experiments
demonstrate the effectiveness of our method by providing a nearly optimal
aggregator in the setting considered by Arieli et al. (2018)
Sample Complexity of Forecast Aggregation
We consider a Bayesian forecast aggregation model where experts, after
observing private signals about an unknown binary event, report their posterior
beliefs about the event to a principal, who then aggregates the reports into a
single prediction for the event. The signals of the experts and the outcome of
the event follow a joint distribution that is unknown to the principal, but the
principal has access to i.i.d. "samples" from the distribution, where each
sample is a tuple of the experts' reports (not signals) and the realization of
the event. Using these samples, the principal aims to find an
-approximately optimal aggregator, where optimality is measured in
terms of the expected squared distance between the aggregated prediction and
the realization of the event. We show that the sample complexity of this
problem is at least for arbitrary
discrete distributions, where is the size of each expert's signal space.
This sample complexity grows exponentially in the number of experts . But,
if the experts' signals are independent conditioned on the realization of the
event, then the sample complexity is significantly reduced, to , which does not depend on . Our results can be generalized
to non-binary events. The proof of our results uses a reduction from the
distribution learning problem and reveals the fact that forecast aggregation is
almost as difficult as distribution learning.Comment: Update related works. Add new results and discussion
Multi-resolution forecast aggregation for time series in agri datasets
A wide variety of phenomena are characterized by time dependent dynamics that can be analyzed using time series methods. Various time series analysis techniques have been presented, each addressing certain aspects of the data. In time series analysis, forecasting is a challenging problem when attempting to estimate extended time horizons which effectively encapsulate multi-step-ahead (MSA) predictions. Two original solutions to MSA are the direct and the recursive approaches. Recent studies have mainly focused on combining previous methods as an attempt to overcome the problem of discarding sequential correlation in the direct strategy or accumulation of error in the recursive strategy. This paper introduces a technique known as Multi-Resolution Forecast Aggregation (MRFA) which incorporates an additional concept known as Resolutions of Impact. MRFA is shown to have favourable prediction capabilities in comparison to a number of state of the art methods
Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?
Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECB's monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of subindices of the Harmonized Index of Consumer Prices (HICP) as opposed to forecasting the aggregate HICP directly. The analysis includes univariate and multivariate linear time series models and distinguishes between different forecast horizons, HICP components and inflation measures. Various model selection procedures are employed to select models for the aggregate and the disaggregate components. The results indicate that aggregating forecasts by component does not necessarily help forecast year-on-year inflation twelve months ahead. JEL Classification: E31, E37, C53, C32Euro Area Inflation, HICP subindex forecast aggregation, linear time series models
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