nep-for New Economics Papers
on Forecasting
Issue of 2019‒01‒14
ten papers chosen by
Rob J Hyndman
Monash University

  1. Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors By Clark, Todd E.; McCracken, Michael W.; Mertens, Elmar
  2. Slicing up inflation: analysis and forecasting of Lithuanian inflation components By Julius Stakenas
  3. Model instability in predictive exchange rate regressions By Hauzenberger, Niko; Huber, Florian
  4. Forecasting FOMC Forecasts By S. Yanki Kalfa; Jaime Marquez
  5. Econometric modelling and forecasting of intraday electricity prices By Micha{\l} Narajewski; Florian Ziel
  6. Multimodal deep learning for short-term stock volatility prediction By Marcelo Sardelich; Suresh Manandhar
  7. New testing approaches for mean-variance predictability By Gabriele Fiorentini; Enrique Sentana
  8. Timing the market: the economic value of price extremes By Haibin Xie; Shouyang Wang
  9. Forecasting the production side of GDP By Gregor Bäurle; Elizabeth Steiner; Gabriel Züllig
  10. Central- versus Self-Dispatch in Electricity Markets By Holmberg, Pär; Tangerås, Thomas; Ahlqvist, Victor

  1. By: Clark, Todd E. (Federal Reserve Bank of Cleveland); McCracken, Michael W. (Federal Reserve Bank of St. Louis); Mertens, Elmar (Bank for International Settlements)
    Abstract: We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to other surveys like the Blue Chip Consensus, or the Federal Open Market Committee’s Summary of Economic Projections.
    Keywords: Stochastic volatility; survey forecasts; fan charts;
    JEL: C53 E37
    Date: 2017–09–01
  2. By: Julius Stakenas (Bank of Lithuania)
    Abstract: In this paper we model five Lithuanian HICP subcomponents in a medium scale Bayesian VAR framework. We deal with the parameter proliferation problem by setting the appropriate amount of shrinkage determined in the out-of-sample forecasting exercise. The main body of the paper consists of displaying the model’s performance in two applications: forecasting and analysis of inflation determinants. We find the model’s forecasts to be competitive against the univariate statistical models, particularly in the cases of predicting processed food and energy goods inflation. What is more, exercises based on conditional forecasting show that these two indices make the best use of accurate conditional information in terms of improving predicting accuracy. In the decomposition of the drivers of HICP components, we demonstrate that both, domestic and foreign factors can be prevalent inflation determinants in certain time periods. We also find some evidence on employees’ bargaining power playing a role in determining the Lithuanian consumer price inflation.
    Keywords: HICP subindices, Bayesian VAR, Bayesian shrinkage, inflation forecasting, structural decomposition
    JEL: C32 C53 E37
    Date: 2018–12–28
  3. By: Hauzenberger, Niko; Huber, Florian
    Abstract: In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered.
    Keywords: Empirical exchange rate models, exchange rate fundamentals, Markov switching
    Date: 2018–12
  4. By: S. Yanki Kalfa (International Monetary Fund); Jaime Marquez (Johns Hopkins School of Advanced International Studies (SAIS))
    Abstract: Summarizing Hendry’s forty years of work on taming uncertainty is "clear and distinct": Test, test, test. Sure - but test what? Test the maintained assumptions of the disturbances. Test the parameter restrictions of a given model. Test the explanatory power of a model against a rival model. In brief, test everything that is not clear and distinct. We implement Hendry’s view to forecast FOMC forecasts. Specifically, monetary policy is forward looking and, in its pursuit of transparency, it communicates its economic projections to the public at large. As a result, there is interest in whether these projections are credible. We argue that central to that credibility is the public’s ability to replicate FOMC’s projections using publicly available data only. In other words, is it possible to anticipate, reliably and independently, what the FOMC will anticipate for the federal funds rate? To address this question, we assemble FOMC projections from 1992 to 2017; examine their statistical properties; postulate models to predict FOMC projections; estimate the parameters of these models; and generate out-of-sample predictions for inflation, unemployment, and the federal funds rate for 2018. As the reader will soon realize, there is a lot more testing to be done.
    Keywords: Autometrics, Federal Funds Rate, FOMC, Survey of Professional Forecasters
    JEL: E5 C4
    Date: 2018–11
  5. By: Micha{\l} Narajewski; Florian Ziel
    Abstract: In the following paper we analyse the ID$_3$-Price on German Intraday Continuous Electricity Market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-sample very short-term forecasting study. The model's performance is compared with benchmark models and is discussed in detail. Forecasting results provide new insights to the German Intraday Continuous Electricity Market regarding its efficiency and to the ID$_3$-Price behaviour. The supplementary materials are available online.
    Date: 2018–12
  6. By: Marcelo Sardelich; Suresh Manandhar
    Abstract: Stock market volatility forecasting is a task relevant to assessing market risk. We investigate the interaction between news and prices for the one-day-ahead volatility prediction using state-of-the-art deep learning approaches. The proposed models are trained either end-to-end or using sentence encoders transfered from other tasks. We evaluate a broad range of stock market sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials. Our experimental results show that adding news improves the volatility forecasting as compared to the mainstream models that rely only on price data. In particular, our model outperforms the widely-recognized GARCH(1,1) model for all sectors in terms of coefficient of determination $R^2$, $MSE$ and $MAE$, achieving the best performance when training from both news and price data.
    Date: 2018–12
  7. By: Gabriele Fiorentini (Università di Firenze, Italy; Rimini Centre for Economic Analysis); Enrique Sentana (CEMFI, Spain)
    Abstract: We propose tests for smooth but persistent serial correlation in risk premia and volatilities that exploit the non-normality of financial returns. Our parametric tests are robust to distributional misspecification, while our semiparametric tests are as powerful as if we knew the true return distribution. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama-French factors for international stocks, whose distributions are mostly symmetric and fat-tailed. Our results highlight noticeable differences across regions and factors and confirm the fragility of Gaussian tests.
    Keywords: financial forecasting, moment tests, misspecification, robustness, volatility
    JEL: C12 C22 G17
    Date: 2019–01
  8. By: Haibin Xie; Shouyang Wang
    Abstract: By decomposing asset returns into potential maximum gain (PMG) and potential maximum loss (PML) with price extremes, this study empirically investigated the relationships between PMG and PML. We found significant asymmetry between PMG and PML. PML significantly contributed to forecasting PMG but not vice versa. We further explored the power of this asymmetry for predicting asset returns and found it could significantly improve asset return predictability in both in-sample and out-of-sample forecasting. Investors who incorporate this asymmetry into their investment decisions can get substantial utility gains. This asymmetry remains significant even when controlling for macroeconomic variables, technical indicators, market sentiment, and skewness. Moreover, this asymmetry was found to be quite general across different countries.
    Date: 2019–01
  9. By: Gregor Bäurle; Elizabeth Steiner; Gabriel Züllig
    Keywords: Forecasting, GDP, Sectoral heterogeneity, Bayesian vector auto regression, Dynamic Factor Model
    JEL: C11 C32 C38 E32 E37
    Date: 2018
  10. By: Holmberg, Pär (Research Institute of Industrial Economics (IFN)); Tangerås, Thomas (Research Institute of Industrial Economics (IFN)); Ahlqvist, Victor (Research Institute of Industrial Economics (IFN))
    Abstract: In centralized markets, producers submit detailed cost data to the day-ahead 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: 2018–12–17

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