nep-for New Economics Papers
on Forecasting
Issue of 2017‒03‒05
eight papers chosen by
Rob J Hyndman
Monash University

  1. Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve's Approach By David Reifschneider; Peter Tulip
  2. Forecasting Inflation in Vietnam with Univariate and Vector Autoregressive Models By Tran Thanh Hoa
  3. Using the payment system data to forecast the Italian GDP By Valentina Aprigliano; Guerino Ardizzi; Libero Monteforte
  4. Forecast Performance, Disagreement, and Heterogeneous Signal-to-Noise Ratios By Hartmann, Matthias; Dovern, Jonas
  5. Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers By Andrea Bastianin; Marzio Galeotti; Matteo Manera
  6. Forecasting euro area inflation using targeted predictors: is money coming back? By Falagiarda, Matteo; Sousa, João
  7. The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis By Rich, Robert W.; Tracy, Joseph
  8. A Power Booster Factor for Out-of-Sample Tests of Predictability By Pincheira, Pablo

  1. By: David Reifschneider (Board of Governors of the Federal Reserve System); Peter Tulip (Reserve Bank of Australia)
    Abstract: Since November 2007, the Federal Open Market Committee (FOMC) of the US Federal Reserve has regularly published participants' qualitative assessments of the uncertainty attending their individual forecasts of real activity and inflation, expressed relative to that seen on average in the past. The benchmarks used for these historical comparisons are the average root mean squared forecast errors (RMSEs) made by various private and government forecasters over the past twenty years. This paper documents how these benchmarks are constructed and discusses some of their properties. We draw several conclusions. First, if past performance is a reasonable guide to future accuracy, considerable uncertainty surrounds all macroeconomic projections, including those of FOMC participants. Second, different forecasters have similar accuracy. Third, estimates of uncertainty about future real activity and interest rates are now considerably greater than prior to the financial crisis; in contrast, estimates of inflation accuracy have changed little. Finally, fan charts – constructed as plus-or-minus one RMSE intervals about the median FOMC forecast, under the expectation that future projection errors will be unbiased and symmetrically distributed, and that the intervals cover about 70 percent of possible outcomes – provide a reasonable approximation to future uncertainty, especially when viewed in conjuction with the FOMC's qualitative assessments. That said, an assumption of symmetry about the interest rate outlook is problematic if the expected path of the federal funds rate is expected to remain low.
    Keywords: forecast uncertainty; fan charts; interval estimation
    JEL: C53 E37 E58
    Date: 2017–02
  2. By: Tran Thanh Hoa (The State Bank of Vietnam)
    Abstract: In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naïve benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on Atkeson-Ohanian (2001), Gosselin-Tkacz (2001) and the specific properties of inflation in Vietnam. Then, I compute the pseudo out-of-sample root mean square error (RMSE) as a measure of forecast accuracy for the candidate models and benchmarks, using rolling window and expanding window forecasting evaluation strategies. The process is applied to both monthly and quarterly data from Vietnam for the period from 2000 through the first half of 2015. I also apply the forecast-encompassing Diebold-Mariano test to support choosing statistically better forecasting models from among the different candidates. I find that VAR_m2 is the best monthly model to forecast inflation in Vietnam, whereas AR(6) is the best of the quarterly forecasting models, although it provides a statistically insignificantly better forecast than the benchmark BM2_q.
    Keywords: Inflation, Forecast, Univariate Models, Vector Autoregressive Models, Forecast Accuracy
    JEL: C22 C32 C51 C53 E31 E37
    Date: 2017–02
  3. By: Valentina Aprigliano (Bank of Italy); Guerino Ardizzi (Bank of Italy); Libero Monteforte (Bank of Italy)
    Abstract: Payment systems track economic transactions and therefore could be considered important indicators of economic activity. This paper describes the available monthly data on the retail settlement system for Italy and selects some of them for short-term forecasting. Using a mixed frequency factor model to predict Italian GDP, we find that payment system flows stand out when compared to other standard business cycle indicators.
    Keywords: short term forecasting, LASSO, mixed frequency models, Kalman smoothing, payment systems, TARGET2
    JEL: C53 E17 E27 E32 E37 E42
    Date: 2017–02
  4. By: Hartmann, Matthias; Dovern, Jonas
    Abstract: We propose an imperfect information model for the expectations of macroeconomic forecasters that explains differences in average disagreement levels across forecasters by means of cross sectional heterogeneity in the variance of private noise signals. We show that the forecaster-specific signal-to-noise ratios determine both the average individual disagreement level and an individuals' forecast performance: forecasters with very noisy signals deviate strongly from the average forecasts and report forecasts with low accuracy. We take the model to the data by empirically testing for this implied correlation. Evidence based on data from the Surveys of Professional Forecasters for the US and for the Euro Area supports the model for short- and medium-run forecasts but rejects it based on its implications for long-run forecasts.
    JEL: E37 D80 C53
    Date: 2016
  5. By: Andrea Bastianin (University of Milan); Marzio Galeotti (University of Milan and IEFE Bocconi); Matteo Manera (University of Milan-Bicocca and Fondazione Eni Enrico Mattei)
    Abstract: Call centers' managers are interested in obtaining accurate forecasts of call arrivals because these are a key input in staffing and scheduling decisions. Therefore their ability to achieve an optimal balance between service quality and operating costs ultimately hinges on forecast accuracy. We present a strategy to model selection in call centers which is based on three pillars: (i) a flexible loss function; (ii) statistical evaluation of forecast accuracy; (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of call arrivals. We show that second moment modeling is important when forecasting call arrivals. From the point of view of a call center manager, our results indicate that outsourcing the development of a forecasting model is worth its cost, since the simple Seasonal Random Walk model is always outperformed by other, relatively more sophisticated, specifications.
    Keywords: ARIMA, Call Center Arrivals, Loss Function, Seasonality, Telecommunications Forecasting
    JEL: C22 C25 C53 D81 M15
    Date: 2017–02
  6. By: Falagiarda, Matteo; Sousa, João
    Abstract: This paper sheds new light on the information content of monetary and credit aggregates for future price developments in the euro area. Overall, we find strong variation in the information content of these variables over time. We show that monetary and credit aggregates are very often selected among the top predictors of inflation, with their predictive power relative to other predictors generally improving in the post-2012 period. An out-of-sample forecasting exercise indicates that, when monetary and credit aggregates are loaded directly in the forecasting equation, the additional gains over the benchmark model are generally high and significant across horizons and HICP components only in the most recent period. When the forecasts are computed using factor-augmented regressions based on the best predictors, we confirm the importance of monetary and credit variables in forecasting inflation, even if their information content is diluted in a much broader pool of variables. JEL Classification: C53, E37, E41, E51, E58
    Keywords: diffusion index, forecasting, inflation, money, targeted predictors
    Date: 2017–02
  7. By: Rich, Robert W. (Federal Reserve Bank of New York); Tracy, Joseph (Federal Reserve Bank of New York)
    Abstract: This paper examines point and density forecasts from the European Central Bank’s Survey of Professional Forecasters. We derive individual uncertainty measures along with individual point- and density-based measures of disagreement. We also explore the relationship between uncertainty and disagreement, as well as their roles in respondents’ forecast performance and forecast revisions. We observe substantial heterogeneity in respondents’ uncertainty and disagreement. In addition, there is little co-movement between uncertainty and disagreement, and forecast performance shows a more robust inverse relationship with disagreement than with uncertainty. Further, forecast revisions display a more meaningful association with disagreement than with uncertainty: Those respondents displaying higher levels of disagreement revise their point and density forecasts by a larger amount.
    Keywords: uncertainty; disagreement; ECB-SPF; density forecasts; point forecasts; forecast accuracy; forecast revisions
    JEL: C10 C20 C40
    Date: 2017–02–01
  8. By: Pincheira, Pablo
    Abstract: In this paper we introduce a “power booster factor” for out-of-sample tests of predictability. The relevant econometric environment is one in which the econometrician wants to compare the population Mean Squared Prediction Errors (MSPE) of two models: one big nesting model, and another smaller nested model. Although our factor can be used to improve the power of many out-of-sample tests of predictability, in this paper we focus on boosting the power of the widely used test developed by Clark and West (2006, 2007). Our new test multiplies the Clark and West t-statistic by a factor that should be close to one under the null hypothesis that the short nested model is the true model, but that should be greater than one under the alternative hypothesis that the big nesting model is more adequate. We use Monte Carlo simulations to explore the size and power of our approach. Our simulations reveal that the new test is well sized and powerful. In particular, it tends to be less undersized and more powerful than the test by Clark and West (2006, 2007). Although most of the gains in power are associated to size improvements, we also obtain gains in size-adjusted power. Finally we present an empirical application in which more rejections of the null hypothesis are obtained with our new test.
    Keywords: Time-series, forecasting, inference, inflation, exchange rates, random walk, out-of-sample
    JEL: C22 C52 C53 C58 E17 E27 E37 E47 F37
    Date: 2017–02

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