We propose a new approach to test the full-information rational expectations hypothesis which can identify whether rejections of the null arise from information rigidities. This approach quantifies ...the economic significance of departures from the null and the underlying degree of information rigidity. Applying this approach to US and international data of professional forecasters and other agents yields pervasive evidence consistent with the presence of information rigidities. These results therefore provide a set of stylized facts which can be used to calibrate imperfect information models. Finally, we document evidence of state-dependence in the expectations formation process.
Exchange Rate Predictability Rossi, Barbara
Journal of economic literature,
12/2013, Letnik:
51, Številka:
4
Journal Article
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The main goal of this article is to provide an answer to the question: does anything forecast exchange rates, and if so, which variables? It is well known that exchange rate fluctuations are very ...difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: "Are exchange rates predictable?" is, "It depends"—on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift.
We propose new indices to measure macroeconomic uncertainty. The indices measure how unexpected a realization of a representative macroeconomic variable is relative to the unconditional forecast ...error distribution. We use forecast error distributions based on the nowcasts and forecasts of the Survey of Professional Forecasters. We further compare the new indices with those proposed in the literature and assess their macroeconomic impact.
Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the ...historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy.
An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer ...seasonality, and dual-calendar effects. The new framework incorporates Box-Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. A key feature of the framework is that it relies on a new method that greatly reduces the computational burden in the maximum likelihood estimation. The modeling framework is useful for a broad range of applications, its versatility being illustrated in three empirical studies. In addition, the proposed trigonometric formulation is presented as a means of decomposing complex seasonal time series, and it is shown that this decomposition leads to the identification and extraction of seasonal components which are otherwise not apparent in the time series plot itself.
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change ...over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
This article proposes new methodologies for evaluating economic models' out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve ...evaluating the predictive ability of forecasting models over a wide range of window sizes. The study shows that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models' forecasting ability.
Forecasting with temporal hierarchies Athanasopoulos, George; Hyndman, Rob J.; Kourentzes, Nikolaos ...
European journal of operational research,
10/2017, Letnik:
262, Številka:
1
Journal Article
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•Temporal hierarchies can be used for any time series to improve forecasting.•The proposed methodology is independent of forecasting models.•It results in temporally reconciled, accurate and robust ...forecasts.•The implied combination mitigates modelling uncertainty.•We demonstrate empirically that it offers superior accuracy and decision making.
This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly ...developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less persistent than the continuous component, and that separating the rough jump moves from the smooth continuous moves results in significant out-of-sample volatility forecast improvements. Moreover, many of the significant jumps are associated with specific macroeconomic news announcements.
•eDemand, a data driven energy consumption forecasting model is developed.•The proposed model is designed for accurate short, mid, and long term forecasting.•Improved version of SCOA is used to ...identify the optimal hyperparameters of LSTM.•A case study using KReSIT power consumption data is presented.•Impact of hidden layers, hidden units and dropout on model accuracy was analysed.
Data driven building energy consumption forecasting models play a significant role in enhancing the energy efficiency of the buildings through building energy management, energy operations, and control strategies. The multi-source and heterogeneous energy consumption data necessitates the integration of evolutionary algorithms and data-driven models for better forecast accuracy and robustness. We present eDemand, an energy consumption forecasting model which employs long short term memory networks and improved sine cosine optimization algorithm for accurate and robust building energy consumption forecasting. A novel Haar wavelet based mutation operator was introduced to enhance the divergence nature of sine cosine optimization algorithm towards the global optimal solution. Further, the hyperparameters (learning rate, weight decay, momentum, and number of hidden layers) of the LSTM were optimized using the improved sine cosine optimization algorithm. A case study on the real-time energy consumption data obtained from Kanwal Rekhi building, an academic building at Indian Institute of Technology, Bombay for short, mid, and long-term forecasting. Experiments reveal that the proposed model outperforms the state-of-the-art energy consumption forecast models in terms of mean absolute error, mean absolute percentage error, mean square error, root mean square error, and Theil statistics. It is shown that stable and accurate forecast results are produced by ISCOA-LSTM and hence it can be used as an efficient tool for solving energy consumption forecast problems.
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