nep-for New Economics Papers
on Forecasting
Issue of 2011‒05‒07
seven papers chosen by
Rob J Hyndman
Monash University

  1. Evaluating Macroeconomic Forecasts: A Review of Some Recent Developments By Philip Hans Franses; Michael McAleer; Rianne Legerstee
  2. Hierarchical shrinkage priors for dynamic regressions with many predictors By Korobilis, Dimitris
  3. Evaluating Individual and Mean Non-Replicable Forecasts By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  4. Multi-period credit default prediction with time-varying covariates. By Orth, Walter
  5. A Vector Auto-Regressıve (VAR) Model for the Turkish Financial Markets By Bayraci, Selcuk; ARI, YAKUP; YILDIRIM, YAVUZ
  6. Systemic Risks and the Macroeconomy By Gianni De Nicolò; Marcella Lucchetta
  7. Using weight-for-age for predicting wasted children By Nguefack-Tsague, Georges; Tanya K. N., Agatha

  1. By: Philip Hans Franses (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University); Rianne Legerstee (Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute)
    Abstract: Macroeconomic forecasts are frequently produced, widely published, intensively discussed and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.
    Keywords: Macroeconomic forecasts, econometric models, human intuition, biased forecasts, forecast performance, forecast evaluation, forecast comparison.
    JEL: C22 C51 C52 C53 E27 E37
    Date: 2011–04
  2. By: Korobilis, Dimitris
    Abstract: This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using this single hierarchical Bayes formulation. Using 129 U.S. macroeconomic quarterly variables for the period 1959 -- 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results show that for particular data series hierarchical shrinkage dominates factor model forecasts, and hence it becomes a valuable addition to existing methods for handling large dimensional data.
    Keywords: Forecasting; shrinkage; factor model; variable selection; Bayesian LASSO
    JEL: C53 C63 C52 C22 E37 C11
    Date: 2011–04–17
  3. By: Chia-Lin Chang; Philip Hans Franses; Michael McAleer (University of Canterbury)
    Abstract: Macroeconomic forecasts are often based on the interaction between econometric models and experts. A forecast that is based only on an econometric model is replicable and may be unbiased, whereas a forecast that is not based only on an econometric model, but also incorporates expert intuition, is non-replicable and is typically biased. In this paper we propose a methodology to analyze the qualities of individual and means of non-replicable forecasts. One part of the methodology seeks to retrieve a replicable component from the non-replicable forecasts, and compares this component against the actual data. A second part modifies the estimation routine due to the assumption that the difference between a replicable and a non-replicable forecast involves measurement error. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the methodological approach using both individuals and mean forecasts.
    Keywords: Individual forecasts; mean forecasts; efficient estimation; generated regressors; replicable forecasts; non-replicable forecasts; expert intuition
    JEL: C53 C22 E27 E37
    Date: 2011–04–01
  4. By: Orth, Walter
    Abstract: In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.
    Keywords: Credit default; multi-period predictions; hazard models; panel data; out-of-sample tests
    JEL: C53 C41 G32
    Date: 2011–03–17
  5. By: Bayraci, Selcuk; ARI, YAKUP; YILDIRIM, YAVUZ
    Abstract: In this paper, we develop a vector autoregressive (VAR) model of the Turkish financial markets for the period of June 15 2006 – June 15 2010 and forecasts ISE100 index, TRY/USD exchange rate, and short-term interest rates. The out-of-sample forecast performance of the VAR model is compared with the results from the univariate models. Moreover, the dynamics of the financial markets are analyzed through Granger causality and impulse response analysis.
    Keywords: multivariate financial time series; vector auto-regressive (VAR) model; impulse response analysis; Granger causality
    JEL: C51 C01
    Date: 2011–04–24
  6. By: Gianni De Nicolò; Marcella Lucchetta
    Abstract: This paper presents a modeling framework that delivers joint forecasts of indicators of systemic real risk and systemic financial risk, as well as stress-tests of these indicators as impulse responses to structural shocks identified by standard macroeconomic and banking theory. This framework is implemented using large sets of quarterly time series of indicators of financial and real activity for the G-7 economies for the 1980Q1-2009Q3 period. We obtain two main results. First, there is evidence of out-of sample forecasting power for tail risk realizations of real activity for several countries, suggesting the usefulness of the model as a risk monitoring tool. Second, in all countries aggregate demand shocks are the main drivers of the real cycle, and bank credit demand shocks are the main drivers of the bank lending cycle. These results challenge the common wisdom that constraints in the aggregate supply of credit have been a key driver of the sharp downturn in real activity experienced by the G-7 economies in 2008Q4-2009Q1.
    JEL: E17 E44 G21
    Date: 2011–04
  7. By: Nguefack-Tsague, Georges; Tanya K. N., Agatha
    Abstract: Background: The equipments for taking body weights (scales) are more frequent in Cameroon health centres than measuring boards for heights. Even when the later exist there are some difficulties inherent in their qualities; thus the height measurement is not always available or accurate. Objective: To construct statistical models for predicting wasting from weight-for-age. Methods: 3742 children a ged 0 to 59 months were enrolled in a cross-sectional household survey (2004 Cameroon Demographic and Health Surveys (DHS)) covering the entire Cameroon national territory. Results: There were highly significant association between underweight and wasting. For all discriminant statistical methods used, the test error rates (using an independent testing sample) are less than 5%; the Area Under the Curve (AUC) using the Receiver Operating Characteristic (ROC) is 0.86. Conclusions: Weight-for-age can be used for accurately classifying a child whose wasting status is unknown. The result is useful in Cameroon as too often the height measurements may not be feasible, thus the need for estimating wasted children.
    Keywords: Anthropometric measures; nutritional status; discriminant analysis; underweight; wasting
    JEL: I12 C35
    Date: 2011–01–16

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