nep-for New Economics Papers
on Forecasting
Issue of 2019‒02‒04
four papers chosen by
Rob J Hyndman
Monash University

  1. Assessing the External Demand of the Czech Economy: Nowcasting Foreign GDP Using Bridge Equations By Tomas Adam; Filip Novotny
  2. Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data By Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  3. Central- versus Self-Dispatch in Electricity Markets By Ahlqvist, V.; Holmberg, P; Tangeras, T.
  4. Volatility Models Applied to Geophysics and High Frequency Financial Market Data By Maria C Mariani; Md Al Masum Bhuiyan; Osei K Tweneboah; Hector Gonzalez-Huizar; Ionut Florescu

  1. By: Tomas Adam; Filip Novotny
    Abstract: We propose an approach to nowcasting foreign GDP growth rates for the Czech economy. For presentational purposes, we focus on three major trading partners: Germany, Slovakia and France. We opt for a simple method which is very general and which has proved successful in the literature: the method based on bridge equation models. A battery of models is evaluated based on a pseudo-real-time forecasting exercise. The results for Germany and France suggest that the models are more successful at backcasting, nowcasting and forecasting than the naive random walk benchmark model. At the same time, the various models considered are more or less successful depending on the forecast horizon. On the other hand, the results for Slovakia are less convincing, possibly due to the stability of the GDP growth rate over the evaluation period and the weak relationship between GDP growth rates and monthly indicators in the training sample.
    Keywords: Bayesian model averaging, bridge equations, nowcasting, short-term forecasting
    JEL: C53 E37
    Date: 2018–12
  2. By: Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach, that can be used to tackle these challenges, is proposed. The proposed method can be effectively combined with deep neural networks, leading to powerful deep learning models for time series analysis. However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process. The proposed method is capable of overcoming these limitations by a employing a novel adaptive scaling mechanism and replacing the classical Gaussian-based density estimation involved in the regular BoF model with a logistic kernel. The effectiveness of the proposed approach is demonstrated using extensive experiments on a large-scale financial time series dataset that consists of more than 4 million limit orders.
    Date: 2019–01
  3. By: Ahlqvist, V.; Holmberg, P; Tangeras, T.
    Abstract: In centralized markets, producers submit detailed cost data to the dayahead market, and the market operator decides how much should be produced in each plant. This differs from decentralized markets that rely on self-commitment and where producers send less detailed cost information to the operator of the day-ahead market. Ideally centralized electricity markets would be more effective, as they consider more detailed information, such as start-up costs and no-load costs. On the other hand, the bidding format is rather simplified and does not allow producers to express all details in their costs. Moreover, due to uplift payments, producers have incentives to exaggerate their costs. As of today, US has centralized wholesale electricity markets, while most of Europe has decentralized wholesale electricity markets. The main problem with centralized markets in US is that they do not provide intra-day prices which can be used to continuously up-date the dispatch when the forecast for renewable output changes. Intra-day markets are more flexible and better adapted to deal with renewable power in decentralized markets. Iterative intra-day trading in a decentralized market can also be used to sort out coordination problems related to non-convexities in the production. The downside of this is that increased possibilities to coordinate increase the risk of getting collusive outcomes. Decentralized day-ahead markets in Europe can mainly be improved by considering network constraints in more detail.
    Keywords: wholesale electricity markets, market clearing, centralization, decentralization, unit-commitment, self-dispatch
    JEL: D44 L13 L94
    Date: 2019–01–18
  4. By: Maria C Mariani; Md Al Masum Bhuiyan; Osei K Tweneboah; Hector Gonzalez-Huizar; Ionut Florescu
    Abstract: This work is devoted to the study of modeling geophysical and financial time series. A class of volatility models with time-varying parameters is presented to forecast the volatility of time series in a stationary environment. The modeling of stationary time series with consistent properties facilitates prediction with much certainty. Using the GARCH and stochastic volatility model, we forecast one-step-ahead suggested volatility with +/- 2 standard prediction errors, which is enacted via Maximum Likelihood Estimation. We compare the stochastic volatility model relying on the filtering technique as used in the conditional volatility with the GARCH model. We conclude that the stochastic volatility is a better forecasting tool than GARCH (1, 1), since it is less conditioned by autoregressive past information.
    Date: 2019–01

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