nep-for New Economics Papers
on Forecasting
Issue of 2013‒09‒13
eight papers chosen by
Rob J Hyndman
Monash University

  1. Modeling the impact of forecast-based regime switches on macroeconomic time series By Bel, K.; Paap, R.
  2. The Economic Valuation of Variance Forecasts: An Artificial Option Market Approach By Radovan Parrák
  3. Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict U.S. Equity Premium? By Rangan Gupta; Shawkat Hammoudeh; Mampho P. Modise; Duc Khuong Nguyen
  4. Assessing the forecasting power of the leading composite index in Macedonia By Petreski, Marjan
  5. FOMC forecasts as a focal point for private expectations By Paul Hubert
  6. Inflation fan charts and different dimensions of uncertainty. What if macroeconomic uncertainty is high? By Halina Kowalczyk
  7. Covariates and causal effects: the problem of context By Dionissi Aliprantis
  8. Predicting Patterns of Customer Usage, with Niftecash By Bargary, N.; Berkery, E.M.; Cellai, D.; Dobrzynski, M.; Faqeeh, A.; Krzyzanowski, G.; Melnik, S.; Nyamundanda, G.; O'Callaghan, R.; Sheikhi, A.; Sikora, M.; Vylegzhanin, E.; Wardell, O.; Wilson, R. E.

  1. By: Bel, K.; Paap, R.
    Abstract: Forecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition autoregressive model where the regime switches depend on the forecast of the time series of interest. This forecast can either be an exogenous expert forecast or an endogenous forecast generated by the model. Results of an application of the model to US inflation shows that (i) forecasts lead to regime changes and have an impact on the level of inflation; (ii) a relatively large forecast results in actions which in the end lower the inflation rate; (iii) a counterfactual scenario where forecasts during the oil crises in the 1970s are assumed to be correct leads to lower inflation than observed.
    Keywords: forecasting;nonlinear time series;inflation;regime switching
    Date: 2013–08–08
  2. By: Radovan Parrák (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)
    Abstract: In this paper we compared two distinct volatility forecasting approaches. GARCH models were contrasted to the models which modelled proxies of volatility directly. More precisely, focus was put on the economic valuation of forecasting accuracy of one-day-ahead volatility forecasts. Profits from trading of one-day at-the-money straddles on the hypothetical (artificial) market were used for assessing the relative volatility forecasting accuracy. Our contribution lies in developing a novel approach to the economic valuation of the volatility forecasts - the artificial option market with a single market price – and its comparison with the established approaches. Further on, we compared the relative intra- and inter-group volatility forecasting accuracy of the competing model families. Finally, we measured the economic value of richer information provided by high-frequency data. To preview the results, we show that the economic valuation of volatility forecasts can bring a meaningful and robust ranking. Additionally, we show that this ranking is similar to the ranking implied by established statistical methods. Moreover, it was shown that modelling of volatility directly is strongly dependent on the volatility proxy in place. It was also shown, as a corollary, that the use of high frequency data to predict a future volatility is of considerable economic value.
    Keywords: GARCH, Realized volatility, economic loss function, volatility forecasting
    JEL: C58
    Date: 2013–08
  3. By: Rangan Gupta (Department of Economics, University of Pretoria); Shawkat Hammoudeh (Lebow College of Business, Drexel University, Philadelphia, USA); Mampho P. Modise (Department of Economics, University of Pretoria); Duc Khuong Nguyen (IPAG Lab, IPAG Business School, France.)
    Abstract: This article attempts to examine whether the equity premium in the United States can be predicted from a com-prehensive set of 18 economic and financial predictors over a monthly out-of-sample period of 2000:2 to 2011:12, using an in-sample period of 1990:2-2000:1. To do so, we consider, in addition to the set of variables used in Rapach and Zhou (2013), the forecasting ability of four other important variables: the US economic policy uncertainty, the equity market uncertainty, the University of Michigan’s index of consumer sentiment, and the Kansas City Fed’s financial stress index. Using a more recent dataset compared to that of Rapach and Zhou (2013), our results from predictive regressions show that the newly added variables do not play any sig-nificant statistical role in explaining the equity premium relative to the historical average benchmark over the out-of-sample horizon, even though they are believed to possess valuable informative content about the state of the economy and financial markets. Interestingly, however, barring the economic policy uncertainty index, the three other indexes considered in this study yields economically significant out-of-sample gains, especially during recessions, when compared to the historical benchmark.
    Keywords: Equity premium forecasting, asset pricing model, economic uncertainty, business cycle
    JEL: C22 C38 C53 C58 E32 G11 G12 G14 G17
    Date: 2013–09
  4. By: Petreski, Marjan
    Abstract: The objective of the paper is to evaluate the forecasting power of the leading composite index of Macedonia. The leading index is a weighted index of indicators which are considered to lead the economic cycle. The main dynamic model in which, first, GDP is represented as autoregressive process, and then lags of the leading index are added, is used to measure the forecasting error behavior with the addition of the leading index and with the imposition of larger time span in the model. The main finding is that the inclusion of the leading index in the model reduces the forecasting error. The forecasting time of the leading composite index in Macedonia is found to be between one and two quarters.
    Keywords: economic cycle, leading index, root mean squared forecasting error, Macedonia, distributed lags model
    JEL: E37
    Date: 2013–09–01
  5. By: Paul Hubert (Ofce sciences-po)
    Abstract: We explore empirically the theoretical prediction that public information acts as a focal point in the context of the US monetary policy. We aim at establishing whether the publication of FOMC inflation forecasts affects the cross-sectional dispersion of private inflation expectations. Our main finding is that publishing FOMC inflation forecasts has a negative effect on the cross-sectional dispersion of private current-year inflation forecasts. This effect is found to be robust to another survey dataset and to various macroeconomic controls. Moreover, we find that the dispersion of private inflation forecasts is not affected by the dispersion of views among FOMC members.
    Keywords: monetary policy,central bank communication,public information, survey expectations,dispersion
    JEL: E52 E58 E37
    Date: 2013–07
  6. By: Halina Kowalczyk (National Bank of Poland, Economic Institute)
    Abstract: The paper discusses problems associated with communicating uncertainty by means of ‘fan charts’, used in many central banks for presenting density forecasts of inflation and other macroeconomic variables. Limitations of fan charts in the case of high macroeconomic uncertainty are shown. Issues related to definition of uncertainty are addressed, stressing the need to distinguish between statistical model errors and uncertainty due to lack of knowledge. Modifications of the standard methods of constructing fan charts are suggested. The proposed approach is based on t wo d istributions, o ne of w hich is s ubjective and describes possible macroeconomic scenarios, while the other describes model errors. Total uncertainty is represented as a mixture distribution or density convolution. The proposed approach, although it is a mix of judgment and statistics, allows preserving information about scenarios and separating in the analysis different types of uncertainties.
    Date: 2013
  7. By: Dionissi Aliprantis
    Abstract: This paper is concerned with understanding how causal effects can be identified in past data and then used to predict the future in light of the problem of context, or the fact that treatment always influences the outcome variable in combination with covariates. Structuralist and experimentalist views of econometric methodology can be reconciled by adopting notation capable of distinguishing between effects independent of and dependent on context, or direct and net effects. By showing that identification of direct and net effects imposes distinct assumptions on selection into covariates (i.e., exclusion restrictions) and explicitly constructing predictions based on past effects, the paper is able to characterize the tradeoff researchers face. Relative to direct effects, net effects can be identified in the past from more general data-generating processes (DGPs), but they can predict the future of less general DGPs. Predicting the future with either type of effect requires knowledge of direct effects. To highlight implications for applied work, I discuss why Local Average Treatment Effects and Marginal Treatment Effects of educational attainment are net effects and are therefore difficult to interpret, even when identified with a perfectly randomized treatment.
    Keywords: Statistical methods ; Econometric models
    Date: 2013
  8. By: Bargary, N.; Berkery, E.M.; Cellai, D.; Dobrzynski, M.; Faqeeh, A.; Krzyzanowski, G.; Melnik, S.; Nyamundanda, G.; O'Callaghan, R.; Sheikhi, A.; Sikora, M.; Vylegzhanin, E.; Wardell, O.; Wilson, R. E.
    Abstract: Report is the result of the working during 93rd European Study Group with Industry in Limerick.
    Keywords: ESGI 93 Niftecash
    JEL: L1
    Date: 2013–06

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