Random Scenario Forecasts Versus Stochastic Forecasts

Probabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts. Another (we call it RS) began as a kind of randomized scenario: we consider its simplest variant, in which expert opinion is used to make probability distributions for terminal vital rates, and smooth trajectories are followed over time. We use analysis and examples to show several key differences between these methods: serial correlations in the forecast are much smaller in LT; the variance in LT models of vital rates (especially fertility)is much higher than in RS models that are based on official expert scenarios; trajectories in LT are much more irregular than in RS; probability intervals in LT tend to widen faster over forecast time. Newer versions of RS have been developed that reduce or eliminate some of these differences.

An Unsupervised Deep Learning Approach for Scenario Forecasts

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available at https://github.com/chennnnnyize/Scenario-Forecasts-GA

Structural Forecasts for the Danish Economy Using the Dynamic-AAGE Model

We describe how an applied dynamic general equilibrium model of the Danish economy has been developed to generate structural forecasts. The forecasts provide a microeconomic picture that is consistent with a macroeconomic scenario and the other inputs. We provide an overview of the inputs required to generate the forecasts and of the forecast methodology. Finally, we present aspects of the forecasting results.Research and Development/Tech Change/Emerging Technologies,

Forecasting in the Presence of Structural Breaks and Policy Regime Shifts

The value of selecting the best forecasting model as the basis for empirical economic policy analysis is questioned. When no model coincides with the data generation process, non-causal statistical devices may provide the best available forecasts: examples from recent work include intercept corrections and differenced-data VARs. However, the resulting models need have no policy implications. A 'paradox' may result if their forecasts induce policy changes which can be used to improve the statistical forecast. This suggests correcting statistical forecasts by using the econometric model's estimate of the 'scenario' change, and doing so yields reduced biases.

On selecting policy analysis models by forecast accuracy

The value of selecting the best forecasting model as the basis for empirical economic policy analysis is questioned. When no model coincides with the data generation process, non-causal statistical devices may provide the best available forecasts: examples from recent work include intercept corrections and differenced-data VARs. However, the resulting models need have no policy implications. A ‘paradox’ may result if their forecasts induce policy changes which can be used to improve the statistical forecast. This suggests correcting statistical forecasts by using the econometric model’s estimate of the ‘scenario’ change. An application to UK consumers expenditure illustrates the analysis.

Conditional probabilistic population forecasting

Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them to answer "what if" type questions properly when outcomes are uncertain. The second is a new category that we call "future jump-off date forecasts". Future jump-off date forecasts are valuable because they show policy-makers the likelihood that crucial features of today's forecasts will also be present in forecasts made in the future

Foreign exchange option and returns based correlation forecasts: evaluation and two applications

We compare option-implied correlation forecasts from a dataset consisting of over 10 years of daily data on over-the-counter (OTC) currency option prices to a set of return-based correlation measures and assess the relative quality of the correlation forecasts. We find that while the predictive power of implied correlation is not always superior to that of returns based correlations measures, it tends to provide the most consistent results across currencies. Predictions that use both implied and returns-based correlations generate the highest adjusted R2s, explaining up to 42 per cent of the realised correlations. We then apply the correlation forecasts to two policyrelevant topics, to produce scenario analyses for the euro effective exchange rate index, and to analyse the impact on cross-currency co-movement of interventions on the JPY/USD exchange rate. JEL Classification: F31, F37, G15Correlation forecasts, currency options data, effective exchange rate

Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts

We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants' private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts