nep-for New Economics Papers
on Forecasting
Issue of 2014‒02‒08
seven papers chosen by
Rob J Hyndman
Monash University

  2. Business Confidence and Forecasting of Housing Prices and Rents in Large German Cities By Konstantin A. Kholodilin; Boriss Siliverstovs
  3. Adaptive learning and survey data By Agnieszka Markiewicz; Andreas Pick
  5. On the calculation of safety stocks By Teunter, R.H.; Syntetos, A.A.
  6. Noisy information and fundamental disagreement By Andrade, Philippe; Crump, Richard K.; Eusepi, Stefano; Moench, Emanuel
  7. Performance of credit risk prediction models via proper loss functions By Silvia Figini; Mario Maggi

  1. By: Wojciech Charemza; Carlos Diaz; Svetlana Makarova
    Abstract: Empirical evaluation of macroeconomic uncertainties and their use for probabilistic forecasting are investigated. A new weighted skew normal distribution which parameters are interpretable in relation to monetary policy outcomes and actions is proposed. This distribution is fitted to recursively obtained forecast errors of monthly and annual inflation for 38 countries. It is found that this distribution fits inflation forecasts errors better than the two-piece normal distribution, which is often used for inflation forecasting. The new type of ‘fan charts’ net of the epistemic (potentially predictable) element is proposed and applied for UK and Poland.
    Keywords: macroeconomic forecasting, inflation, uncertainty, monetary policy, non-normality, density forecasting
    JEL: C54 E37 E52
    Date: 2014–01
  2. By: Konstantin A. Kholodilin; Boriss Siliverstovs
    Abstract: In this paper, we evaluate the forecasting ability of 115 indicators to predict the housing prices and rents in 71 German cities. Above all, we are interested in whether the local business confidence indicators can allow substantially improving the forecasts, given the local nature of the real-estate markets. The forecast accuracy of different predictors is tested in a framework of a quasi out-of-sample forecasting. Its results are quite heterogeneous. No single indicator appears to dominate all the others for all cities and market segments. However, there are several predictors that are especially useful, namely the business confidence at the national level, consumer confidence, and price-to-rent ratios. Given the short sample size, the combinations of individual forecast do not improve the forecast accuracy. On average, the forecast improvements attain about 20%, measured by reduction in RMSFE, compared to the naïve model. In separate cases, however, the magnitude of improvement is about 50%.
    Keywords: Housing prices, housing rents, forecasting, spatial dependence, German cities, confidence indicators, chambers of commerce and industry
    JEL: C21 C23 C53
    Date: 2014
  3. By: Agnieszka Markiewicz; Andreas Pick
    Abstract: This paper investigates the ability of the adaptive learning approach to replicate the expectations of professional forecasters. For a range of macroeconomic and financial variables, we compare constant and decreasing gain learning models to simple, yet powerful benchmark models. We find that constant gain models provide a better fit for the expectations of professional forecasters. For macroeconomic series they usually perform significantly better than a naïve random walk forecast. In contrast, we find it difficult to beat the no-change benchmark using the adaptive learning models to forecast financial variables.
    Keywords: expectations; survey of professional forecasters; adaptive learning; bounded rationality
    JEL: E37 E44 G14 G15
    Date: 2014–01
  4. By: Jonas Dovern (University of Heidelberg); Ulrich Fritsche (Hamburg University); Prakash Loungani (International Monetary Fund); Natalia Tamirisa (International Monetary Fund)
    Abstract: We study forecasts for real GDP growth using a large panel of individual forecasts from 36 advanced and emerging economies during 1989–2010. We show that the degree of information rigidity in average forecasts is substantially higher than that in individual forecasts. Individual level forecasts are updated quite frequently, a behavior more in line “noisy” information models (Woodford, 2002; Sims, 2003) than with the assumptions of the sticky information model (Mankiw and Reis, 2002). While there are cross-country variations in information rigidity, there is no systematic difference between advanced and emerging economies.
    Keywords: Rational Inattention, Aggregation Bias, Growth Forecasts, Information Rigidity, Forecast Behavior
    JEL: E27 E37
    Date: 2014–01
  5. By: Teunter, R.H.; Syntetos, A.A. (Groningen University)
    Abstract: In forecasting and inventory control textbooks and software applications, the variance of the cumulative lead-time forecast error is, almost invariably, taken as the sum of the error variances of the individual forecast intervals. For stationary demand and a constant lead time, this implies multiplying the single period variance (or Mean Squared Error) by the lead-time. This standard approach is shown in this paper to always underestimate the true lead-time demand variability, resulting in too low safety stocks and poor service. For two of the most widely applied forecasting techniques (Single Exponential Smoothing and Simple Moving Average) we present corrected expressions and show that the error in the standard approach is often considerable. The same fundamental problem exists for all forecasting techniques and all demand processes, and so this issue deserves wider recognition and offers ample opportunities for further research.
    Date: 2014
  6. By: Andrade, Philippe; Crump, Richard K. (Federal Reserve Bank of New York); Eusepi, Stefano (Federal Reserve Bank of New York); Moench, Emanuel (Federal Reserve Bank of New York)
    Abstract: We study the term structure of disagreement of professional forecasters for key macroeconomic variables. We document a novel set of facts: 1) forecasters disagree at all horizons, including the very long run; 2) the shape of the term structure of disagreement differs markedly across variables: the term structure is downward-sloping for real output growth, relatively flat for CPI inflation, and upward-sloping for the federal funds rate; 3) disagreement is time varying at all horizons, including the very long run. We suggest a model with noisy information and shifting long-run beliefs that is consistent with these stylized facts. Notably, our model does not rely on the heterogeneity of prior beliefs, bounded rationality, or differences in the precision of signals across agents.
    Keywords: expectations; survey forecasts; imperfect information; term structure of disagreement
    JEL: D83 D84 E37
    Date: 2013–12–01
  7. By: Silvia Figini (Department of Political and Social Sciences, University of Pavia); Mario Maggi (Department of Economics and Management, University of Pavia)
    Abstract: The performance of predictions models can be assessed using a variety of methods and metrics. Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the AUC (Area Under the ROC curve), such as the H index. It is widely recognized that AUC suffers from lack of coherency especially when ROC curves cross. On the other hand, the H index requires subjective choices. In our opinion the problem of model comparison should be more adequately handled using a different approach. The main contribution of this paper is to evaluate the performance of prediction models using proper loss function. In order to compare how our approach works with respect to classical measures employed in model comparison, we propose a simulation studies, as well as a real application on credit risk data.
    Keywords: Model Comparison, AUC, H index, Loss Function, Proper Scoring Rules, Credit Risk
    Date: 2014–01

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