nep-for New Economics Papers
on Forecasting
Issue of 2017‒11‒12
nine papers chosen by
Rob J Hyndman
Monash University

  1. What's the Story? A New Perspective on the Value of Economic Forecasts By Steven A. Sharpe; Nitish R. Sinha; Christopher A. Hollrah
  2. Predicting Exchange Rate Volatility in Brazil: an approach using quantile autoregression By Alessandra Pasqualina Viola; Marcelo Cabus Klotzle; Antonio Carlos Figueiredo Pinto; Wagner Piazza Gaglianone
  3. Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter By Martinez, Andrew
  4. A state space approach to evaluate multi-horizon forecasts By Thomas Goodwin; Jing Tian
  5. Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts By Kunze, Frederik
  6. The information content in the offshore Renminbi foreign-exchange option market : Analytics and implied USD/CNH densities By Funke, Michael; Loermann, Julius; Tsang, Andrew
  7. Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors By Todd E Clark; Michael W McCracken; Elmar Mertens
  8. Forecasting models for non-performing loans in the EU countries By Karsten Staehr; Lenno Uusküla
  9. Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory By Asai, M.; McAleer, M.J.; Peiris, S.

  1. By: Steven A. Sharpe; Nitish R. Sinha; Christopher A. Hollrah
    Abstract: We apply textual analysis tools to measure the degree of optimism versus pessimism of the text that describes Federal Reserve Board forecasts published in the Greenbook. We then examine whether this measure of sentiment, or Greenbook text "Tonality", has incremental power for predicting the economy, specifically, unemployment, GDP growth, and inflation up to four quarters ahead; we also test whether Tonality helps predict monetary policy and stock returns. Tonality is found to have significant and substantive directional predictive power for the GDP growth and the change in unemployment over the subsequent four-quarter horizon, particularly since 1990. Higher (more optimistic) Tonality presages higher than forecast GDP growth and lower unemployment. Higher Tonality is also found to help predict tighter monetary policy up to four quarters ahead. Finally, we find that Tonality has substantial positive and significant power for predicting 3-month-ahead and 6-month ahea d stock market returns.
    Keywords: Economic Forecasts ; Monetary policy ; Text Analysis
    JEL: C53 E17 E27 E37 E52
    Date: 2017–11–03
  2. By: Alessandra Pasqualina Viola; Marcelo Cabus Klotzle; Antonio Carlos Figueiredo Pinto; Wagner Piazza Gaglianone
    Abstract: We apply quantile regression in some of its new formulations to analyze exchange rate volatility. We use the conditional autoregressive value at risk (CAViaR) model of Engle and Manganelli (2004), which applies autoregressive functions to quantile regression to estimate volatility. That model has proved effective when compared to others for various purposes. We not only compare the forecasting power of models based on quantile regression with some models of the GARCH family, but also examine the behavior of the exchange rate along its conditional distribution and its consequent volatility. When applying CAViaR in the whole distribution, our results show differentiation of the angular coefficients for each quantile interval of the distribution for the asymmetric CAViaR model. With respect to the exchange rate volatility, we build forecasts from 60 models and use two models as reference to apply the predictive ability test of Giacomini and White (2006). The results indicate that the prediction of the asymmetric CAViaR model with quantile interval of (1, 99) is better than (or equal to) 66% of the models and worse than 34%. In turn, the other benchmark model, the GARCH (1,1), is worse than 71% of the models, better than 13%, and equal in forecasting precision to 16% of the models
    Date: 2017–11
  3. By: Martinez, Andrew (Federal Reserve Bank of Cleveland)
    Abstract: Although the trajectory and path of future outcomes plays an important role in policy decisions, analyses of forecast accuracy typically focus on individual point forecasts. However, it is important to examine the path forecasts errors since they include the forecast dynamics. We use the link between path forecast evaluation methods and the joint predictive density to propose a test for differences in system path forecast accuracy. We also demonstrate how our test relates to and extends existing joint testing approaches. Simulations highlight both the advantages and disadvantages of path forecast accuracy tests in detecting a broad range of differences in forecast errors. We compare the Federal Reserve’s Greenbook point and path forecasts against four DSGE model forecasts. The results show that differences in forecast-error dynamics can play an important role in the assessment of forecast accuracy.
    Keywords: GFESM; log determinant; log score; mean square error;
    JEL: C12 C22 C52 C53
    Date: 2017–11–02
  4. By: Thomas Goodwin; Jing Tian
    Abstract: We propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. We derive the conditions under which forecasts that contain error that is irrelevant to the target can still present the second moment bounds of rational forecasts. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decision.
    Keywords: Rational forecasts, implicit forecasts, forecast revision structure, weather forecasts.
    JEL: C32 C53
    Date: 2017–11
  5. By: Kunze, Frederik
    Abstract: This paper evaluates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar using common forecast accuracy measures. Additionally, the rationality of the exchange rate predictions are assessed utilizing tests for unbiasedness and efficiency. All investigated forecasts are irrational in the sense that the predictions are biased. However, these results are inconsistent with an alternative measure of rationality based on methods of applied time series analysis. Investigating the order of integration of the time series and using cointegration analysis, empirical evidence supports the conclusion that the majority of forecasts are rational. Regarding forerunning properties of the predictions, the results are less convincing, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one important evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.
    Keywords: exchange rates,survey forecasts,forecast evaluation,forecast acccuracy,forecast rationality,cointegration,impulse response analysis
    JEL: F31 F37 G17 O24
    Date: 2017
  6. By: Funke, Michael; Loermann, Julius; Tsang, Andrew
    Abstract: In line with the deepening of the derivative foreign-exchange market in Hong Kong, we recover risk-neutral probability densities for future US dollar/offshore renminbi exchange rates as implied by exchange rate option prices. The risk-neutral densities (RND) approach is shown to be useful in analyzing market sentiment and risk aversion in the renminbi market. We include a forecasting exercise that confirms market participants were able to forecast the shape of the actual densities correctly for short horizons, even if their exact location could not be determined.
    JEL: C53 F31 F37
    Date: 2017–10–23
  7. By: Todd E Clark; Michael W McCracken; Elmar Mertens
    Abstract: We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon speci cation of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
    Keywords: stochastic volatility, survey forecasts, fan charts
    JEL: E37 C53
    Date: 2017–10
  8. By: Karsten Staehr; Lenno Uusküla
    Abstract: This paper estimates panel data models that use macroeconomic and macrofinancial variables to forecast the ratio of non-performing loans to total loans. The panels consist of either all EU countries or various subgroups, and the time sample is 1997Q4 to 2017Q1. The estimations show that macroeconomic and macro-financial variables have important roles in forecasting nonperforming loans. The ratio of non-performing loans exhibits substantial persistence and higher GDP growth, lower inflation and lower debt are robust leading indicators of the ratio of lower non-performing loans. The current account balance and real house prices are important indicators for Western Europe but are less important for Central and Eastern Europe
    Keywords: non-performing loans, forecasting, financial stability
    JEL: E44 E47 G21
    Date: 2017–11–09
  9. By: Asai, M.; McAleer, M.J.; Peiris, S.
    Abstract: In recent years fractionally differenced processes have received a great deal of attention due to their exibility in nancial applications with long memory. In this paper, we develop a new re- alized stochastic volatility (RSV) model with general Gegenbauer long memory (GGLM), which encompasses a new RSV model with seasonal long memory (SLM). The RSV model uses the infor- mation from returns and realized volatility measures simultaneously. The long memory structure of both models can describe unbounded peaks apart from the origin in the power spectrum. For estimating the RSV-GGLM model, we suggest estimating the location parameters for the peaks of the power spectrum in the rst step, and the remaining parameters based on the Whittle likelihood in the second step. We conduct Monte Carlo experiments for investigating the nite sample properties of the estimators, with a quasi-likelihood ratio test of RSV-SLM model against theRSV-GGLM model. We apply the RSV-GGLM and RSV-SLM model to three stock market indices. The estimation and forecasting results indicate the adequacy of considering general long memory.
    Keywords: Stochastic Volatility, Realized Volatility Measure, Long Memory, Gegenbauer Poly-nomial, Seasonality, Whittle Likelihood
    JEL: C18 C21 C58
    Date: 2017–11–01

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