nep-for New Economics Papers
on Forecasting
Issue of 2013‒08‒31
seventeen papers chosen by
Rob J Hyndman
Monash University

  1. An empirical comparison of alternate schemes for combining electricity spot price forecasts By Jakub Nowotarski; Eran Raviv; Stefan Trueck; Rafal Weron
  2. Forecasting Stock Returns under Economic Constraints By Davide Pettenuzzo; Allan Timmermann; Rossen Valkanov
  3. Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach By Christiane Baumeister; Lutz Kilian
  4. Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks By Chen, Shiu-Sheng
  5. Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions By M. Mayer; R. Patuelli
  6. Announcements of Interest Rate Forecasts: Do Policymakers Stick to Them? By Mirkov, Nikola; Natvik, Gisle James
  7. Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach By Ahmet Goncu; Mehmet Oguz Karahan; Tolga Umut Kuzubas
  8. A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory By Nima Nonejad
  9. Investor Overreaction to Analyst Reference Points By Jean-Sébastien Michel
  10. Forecasting Chinese Households’ Demand from Home Production By Yabin Wang
  11. Assessing the historical role of credit: business cycles, financial crises and the legacy of Charles S. Peirce By Oscar Jordà
  12. The Great Recession and the Two Dimensions of European Central Bank Credibility By Timo Henckel; Gordon Menzies; Daniel J. Zizzo
  13. How Predictable are Environmental Compliance Inspections? By Sarah L. Stafford
  14. The impact of jumps and thin trading on realized hedge ratios By Dungey, Mardi; Henry, Olan T; Hvodzdyk, Lyudmyla
  15. Can we still benefit from portfolio diversification in the post-crisis years? The case of the Czech and German stock markets By Krenar Avdulaj; Jozef Barunik
  16. Are we in a bubble? A simple time-series-based diagnostic By Franses, Ph.H.B.F.
  17. Nonparametric estimation of the conditional distribution in a discrete-time stochastic volatility model By Roland Langrock; Th\'eo Michelot; Alexander Sohn; Thomas Kneib

  1. By: Jakub Nowotarski; Eran Raviv; Stefan Trueck; Rafal Weron
    Abstract: In this paper we investigate the use of forecast averaging for electricity spot prices. While there is an increasing body of literature on the use of forecast combinations, there is only a small number of applications of these techniques in the area of electricity markets. In this comprehensive empirical study we apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our findings support the additional benefit of combining forecasts for deriving more accurate predictions, however, the performance is not uniform across the considered markets. Interestingly, equally weighted pooling of forecasts emerges as a viable robust alternative compared with other schemes that rely on estimated combination weights. Overall, we provide empirical evidence that also for the extremely volatile electricity markets, it is beneficial to combine forecasts from various models for the prediction of day-ahead electricity prices. In addition, we empirically demonstrate that not all forecast combination schemes are recommended.
    Keywords: Electricity price forecasting; Forecasts combination; ARX model; Day-ahead market;
    JEL: C22 C24 C53 Q47
    Date: 2013–08–21
  2. By: Davide Pettenuzzo (Economics Department, Brandeis University); Allan Timmermann (University of California, San Diego); Rossen Valkanov (University of California, San Diego)
    Abstract: We propose a new approach to imposing economic constraints on time-series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: Non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates timevarying volatility in the predictive regression framework. Empirically, we Önd that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance. The Sharpe ratio constraint, in particular, results in considerable economic gains.
    Keywords: Economic constraints; Sharpe ratio, Equity premium predictions; Bayesian analysis
    JEL: C11 C22 G11 G12
    Date: 2013–05
  3. By: Christiane Baumeister; Lutz Kilian
    Abstract: The U.S. Energy Information Administration regularly publishes short-term forecasts of the price of crude oil. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify, and not particularly successful when compared with naïve no-change forecasts, as documented in Alquist, Kilian and Vigfusson (2013). Recently, a number of alternative econometric oil price forecasting models have been introduced in the literature and shown to be more accurate than the nochange forecast of the real price of oil. We investigate the merits of constructing realtime forecast combinations of six such models with weights that reflect the recent forecasting success of each model. Forecast combinations are promising for four reasons. First, even the most accurate forecasting models do not work equally well at all times. Second, some forecasting models work better at short horizons and others at longer horizons. Third, even the forecasting model with the lowest mean-squared prediction error (MSPE) may potentially be improved by incorporating information from other models with higher MSPEs. Fourth, one can think of forecast combinations as providing insurance against possible model misspecification and smooth structural change. We demonstrate that over the past 20 years suitably constructed real-time forecast combinations would have been more accurate than the no-change forecast at every horizon up to two years. Relative to the no-change forecast, forecast combinations reduce the MSPE by up to 18 per cent. They also have statistically significant directional accuracy as high as 77 per cent. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil.
    Keywords: Econometric and statistical methods; International topics
    JEL: Q43 C53 E32
    Date: 2013
  4. By: Chen, Shiu-Sheng
    Abstract: This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is ignificantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.
    Keywords: oil-sensitive stock prices; oil prices; out-of-sample prediction
    JEL: C53 G17 Q43 Q47
    Date: 2013–08–22
  5. By: M. Mayer; R. Patuelli
    Abstract: Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of a single estimator, the empirical set-ups of simulation studies are often based on strong assumptions regarding the shape and regularity of the statistical distribution of the variables involved. It is then valuable to have, next to simulation studies, empirical assessments of competing econometric models based on real data. In this paper, we evaluate competing spatial (dynamic) panel methods, selecting a number of data sets characterized by a range of different crosssectional and temporal dimensions, as well as different levels of spatial autocorrelation. We carry out our empirical exercise on regional unemployment data for France, Spain and Switzerland. Additionally, we test different forecasting horizons, in order to investigate the speed of deterioration of forecasting quality. We compare two classes of methods: spatial vector autoregressive (SVAR) models and dynamic panel models making use of eigenvector spatial filtering (SF). We find that, as it could be expected, the unbalance between the temporal and cross-sectional dimension (T >> n) does play in favour of the SVAR model. On the other hand, the advantage of the SVAR model over the SF model appears to diminish as the forecasting horizon widens, eventually leading the SF model to being preferred for more distant forecasts.
    JEL: C53 E24 E27 R12 R15 R23
    Date: 2013–08
  6. By: Mirkov, Nikola; Natvik, Gisle James
    Abstract: If central banks value the ex-post accuracy of their forecasts, previously announced interest rate paths might affect the current policy rate. We explore whether this “forecast adherence” has influenced the monetary policies of the Reserve Bank of New Zealand and the Norges Bank, the two central banks with the longest history of publishing interest rate paths. We derive and estimate a policy rule for a central bank that is reluctant to deviate from its forecasts. The rule can nest a variety of interest rate rules. We find that policymakers appear to be constrained by their most recently announced forecasts.
    Keywords: Interest rates, forecasts, Taylor rule, adherence.
    JEL: E43 E52 E58
    Date: 2013–04
  7. By: Ahmet Goncu; Mehmet Oguz Karahan; Tolga Umut Kuzubas
    Date: 2013–11
  8. By: Nima Nonejad (Aarhus University and CREATES)
    Abstract: We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications.
    Keywords: Mixture innovation models, Markov chain Monte Carlo, Realized volatility
    JEL: C11 C22 C51 C53
    Date: 2013–08–13
  9. By: Jean-Sébastien Michel
    Abstract: In this paper, I document analysts’ reliance on the company issued guidance range as a frame of reference in making their EPS forecasts. Analysts who use the guidance range as a reference may limit information diffusion to market participants by keeping their true beliefs private. I therefore analyze the stock market’s reaction to analyst forecasting decisions, and find that investors overreact to forecasts that are exactly equal to the minimum or maximum of the guidance range, but do not overreact to other types of forecasts. The evidence presented is most consistent with overreaction driven by overconfident investors who trade too much in the face of information uncertainty.
    Keywords: Overreaction, Stock Returns, Reference Point, Analyst Earnings Forecasts
    JEL: G12 G14 G24
    Date: 2013
  10. By: Yabin Wang (University of California, Santa Cruz)
    Abstract: As the Chinese market economy expands and market institutions become stronger, there will be more incentives for Chinese households to substitute market activity for home production.The goal of this paper is to provide a quantitative analysis of the potential Chinese consumer demand for household services. Using dataset from the American Time Use Survey and the China Health and Nutrition Survey, I compare households’ demand for services and home production between US and China. A standard choice-theoretic model of the household is used to estimate the structural parameters and to quantitatively forecast Chinese households’ service demand.
    Date: 2013–06
  11. By: Oscar Jordà
    Abstract: This paper provides a historical overview on financial crises and their origins. The objective is to discuss a few of the modern statistical methods that can be used to evaluate predictors of these rare events. The problem involves prediction of binary events and therefore fits modern statistical learning, signal processing theory, and classification methods. The discussion also emphasizes the need to supplement statistics and computational techniques with economics. A forecast’s success in this environment hinges on the economic consequences of the actions taken as a result of the forecast, rather than on typical statistical metrics of prediction accuracy.
    Keywords: Financial crises ; Statistical methods ; Credit ; Business cycles
    Date: 2013
  12. By: Timo Henckel (Centre for Applied Macroeconomic Analysis, Australian National University); Gordon Menzies (Economics Discipline Group, University of Technology, Sydney); Daniel J. Zizzo (School of Economics and CBESS, University of East Anglia)
    Abstract: A puzzle from the Great Recession is an apparent mismatch between a fall in the persistence of European inflation rates, and the increased variability of expert forecasts of inflation. We explain this puzzle and show how country specific beliefs about inflation are still quite close to the European Central Bank target of 2% (what we call official target credibility) but the degree of anchoring to this target has gone down, implying an erosion of what we call anchoring credibility. A decline in anchoring credibility can explain increased forecast variance independently of any changes in inflation persistence, contrary to standard time series models.
    Keywords: Central bank credibility; excess volatility; euro; inferential expectations; inflation
    JEL: C51 D84 E31 E52
    Date: 2013–08–01
  13. By: Sarah L. Stafford (Department of Economics, College of William and Mary)
    Abstract: The goal of this paper is to examine the timing of environmental compliance inspections and determine the extent to which such inspections can be predicted. The paper focuses on modeling the inspections at hazardous waste facilities in the U.S. using detailed data on individual inspections and facilities. The paper uses a number of parametric and semi-parametric duration models to predict the timing of inspections and finds that the Exponential model provides the best balance in terms of the explanatory power and simplicity of the model. However, even with this model it is difficult to accurately predict the timing of most compliance inspections. The paper also examines the extent to which using data on individual inspections can improve empirical predictions about aggregate inspections. If the goal is to estimate the annual number of inspections at hazardous waste facilities, neither the Exponential model or a Poisson model is clearly superior. Which model is more appropriate depends on the question the researcher wants to answer. Similarly, if the focus is on whether any inspection occurred in a given time period, the benefits of using the Exponential model depend on the nature of the questions to be answered. While the Exponential model performs better than a Probit model in predicting which entities will be inspected, it also results in a higher number of "false positives," that is predicting an inspection when no inspection actually occurs.
    Keywords: Hazardous Waste, Duration Model, Inspection Timing
    Date: 2013–08–21
  14. By: Dungey, Mardi; Henry, Olan T; Hvodzdyk, Lyudmyla (School of Economics and Finance, University of Tasmania)
    Abstract: The use of intradaily data to produce daily variance measures has resulted in increased forecast accuracy and better hedging for many markets. However, this paper shows that improved hedging ratios can depend on the behavior of price disruptions in the assets. When spot and future prices for the same asset do not jump simultaneously inferior hedging outcomes can be observed. This problem dominates potential bias from thin trading. Using US Treasury data we demonstrate how the extent of non-synchronized jumping leads to the ?nding that optimal hedging ratios are not improved with intradaily data in this market.
    Keywords: US US Treasury bonds; Futures; Realized hedge ratios; Jumps; Thin trading
    JEL: C01 C32 G11 G17
    Date: 2013–03–28
  15. By: Krenar Avdulaj; Jozef Barunik
    Abstract: One of the findings of the recent literature is that the 2008 financial crisis caused reduction in international diversification benefits. To fully understand the possible potential from diversification, we build an empirical model which combines generalised autoregressive score copula functions with high frequency data, and allows us to capture and forecast the conditional time-varying joint distribution of stock returns. Using this novel methodology and fresh data covering five years after the crisis, we compute the conditional diversification benefits to answer the question, whether it is still interesting for an international investor to diversify. As a diversification tools, we consider the Czech PX and the German DAX broad stock indices, and we find that the diversification benefits strongly vary over the 2008-2013 post-crisis years.
    Date: 2013–08
  16. By: Franses, Ph.H.B.F.
    Abstract: Time series with bubble-like patterns display an unbalance between growth and acceleration, in the sense that growth in the upswing is “too fast†and then there is a collapse. In fact, such time series show periods where both the first differences (1-L) and the second differences (1-L)2 of the data are positive-valued, after which period there is a collapse. For a time series without such bubbles, it can be shown that 1-L2 differenced data should be stable. A simple test based on one-step-ahead forecast errors can now be used to timely monitor whether a series experiences a bubble and also whether a collapse is near. Illustration on simulated data and on two housing prices and the Nikkei index illustrates the practical relevance of the new diagnostic. Monte Carlo simulations indicate that the empirical power of the test is high.
    Keywords: growth;test;acceleration;C22;speculative bubbles
  17. By: Roland Langrock; Th\'eo Michelot; Alexander Sohn; Thomas Kneib
    Abstract: Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. A substantial body of research deals with various techniques for fitting relatively basic SV models, which assume the returns to be conditionally normally distributed or Student-t-distributed, given the volatility. In this manuscript, we consider a frequentist framework for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. More specifically, we suggest to represent the density of the conditional distribution as a linear combination of standardized B-spline basis functions, imposing a penalty term in order to arrive at a good balance between goodness of fit and smoothness. This allows us to employ the efficient hidden Markov model machinery in order to fit the model and to assess its predictive performance. We demonstrate the feasibility of the approach in a simulation study before applying it to three series of returns on stocks and one series of stock index returns. The nonparametric approach leads to an improved predictive capacity in some cases, and we find evidence for the conditional distributions being leptokurtic and negatively skewed.
    Date: 2013–08

This nep-for issue is ©2013 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.