nep-for New Economics Papers
on Forecasting
Issue of 2010‒09‒11
ten papers chosen by
Rob J Hyndman
Monash University

  1. Alternative methods for forecasting GDP By Dominique Guegan; Patrick Rakotomarolahy
  2. How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  3. Recession Forecasting with Dynamic Probit Models under Real Time Conditions By Christian R. Proano
  4. Short-term forecasting GDP with a DSGE model augmented by monthly indicators By Marianna Červená; Martin Schneider
  5. A Comprehensive Look at Financial Volatility Prediction by Economic Variables By Charlotte Christiansen; Maik Schmeling; Andreas Schrimpf
  6. Model-free Model-fitting and Predictive Distributions By Politis, Dimitris N
  7. Sex offenders and residential location: A predictive analytical framework By Elizabeth A. Mack; Tony H. Grubesic
  8. Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning By Norbert Christopeit; Michael Massmann
  9. Home Bias in Currency Forecasts By Yu-chin Chen; Kwok Ping Tsang; Wen Jen Tsay
  10. On the Difficulty of Measuring Forecasting Skill in Financial Markets By Satchell, S.; Williams, O.J.

  1. By: Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Patrick Rakotomarolahy (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I)
    Abstract: An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, we get more accurate forecasts when using nearest neighbor method. We prove also the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series.
    Keywords: Forecast - Economic indicators - GDP - Euro area - VAR - Multivariate k nearest neighbor regression - Asymptotic normality
    Date: 2010–12
  2. By: Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); 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)
    Abstract: A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially.
    Keywords: Government forecasts, generated regressors, replicable government forecasts, non- replicable government forecasts, initial forecasts, revised forecasts
    JEL: C53 C22 E27 E37
    Date: 2010–08
  3. By: Christian R. Proano (IMK at the Hans Boeckler Foundation)
    Abstract: In this paper a dynamic probit model for recession forecasing under pseudo-real time is set up using a large set of macroeconomic and financial monthly indicators for Germany. Using different initial sets of explanatory variables, alternative dynamic probit specifications are obtained through an automatized general-to-specific lag selection procedure, which are then pooled in order to decrease the volatility of the estimated recession probabilities and increase their forecasting accuracy. As it is shown in the paper, this procedure does not only feature good in-sample forecast statistics, but has also good out-of-sample performance, as pseudo-real time evaluation exercises show.
    Keywords: Dynamic probit models, out-of-sample forecasting, yield curve, real-time econometrics
    JEL: C25 C53
    Date: 2010
  4. By: Marianna Červená; Martin Schneider
    Abstract: DSGE models are useful tools for evaluating the impact of policy changes but their use for (short-term) forecasting is still at an infant stage. Besides theory based restrictions, the timeliness of data is an important issue. Since DSGE models are based on quarterly data, they are vulnerable to a publication lag of quarterly national accounts. In this paper we propose a framework for a short-term forecasting of GDP based on a medium-scale DSGE model for a small open economy within a currency area that utilizes the timely information available in monthly conjunctural indicators. To this end we adopt a methodology proposed by Giannone, Monti and Reichlin (2009). Using Austrian data we find that the forecasting performance of the DSGE model can be improved considerably by conjunctural indicators while still maintaining the story-telling capability of the model. JEL classification:
    Keywords: DSGE models, nowcasting, short-term forecasting, monthly indicators
    Date: 2010–08–25
  5. By: Charlotte Christiansen (School of Economics and Management, Aarhus University and CREATES); Maik Schmeling (Department of Economics, Leibniz Universität Hannover); Andreas Schrimpf (Aarhus University and CREATES)
    Abstract: What drives volatility on financial markets? This paper takes a comprehensive look at the predictability of financial market volatility by macroeconomic and financial variables. We go beyond forecasting stock market volatility (by large the focus in previous studies) and additionally investigate the predictability of foreign exchange, bond, and commodity volatility by means of a data-rich modeling methodology which is able to handle a potentially large number of predictor variables. In line with previous research, we find relatively little economically meaningful predictability of stock market volatility. By contrast, volatility in foreign exchange, bond, and commodity markets appears predictable by macro and financial predictors both in-sample and out-of-sample.
    Keywords: Realized volatility, Forecasting, Data-rich modeling, Bayesian Model Averaging, Model Uncertainty.
    JEL: G12 G15 C53
    Date: 2010–09–02
  6. By: Politis, Dimitris N
    Abstract: The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given set-up into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained.
    Keywords: bootstrap, cross-validation, heteroskedasticity, nonparametric estimation, predictive distribution, predictive intervals, regression, smoothing
    Date: 2010–03–01
  7. By: Elizabeth A. Mack (GeoDa Center for Geospatial Analysis and Computation; Arizona State University); Tony H. Grubesic
    Abstract: Despite the growing body of research dealing with sex offenders and the collateral consequences of legislation governing their post release movements, a complete understanding of the residential choices of registered sex offenders remains elusive. The purpose of this paper is to introduce a predictive analytical framework for determining which demographic and socioeconomic factors best forecast the residential choices of convicted sex offenders. Specifically, using a derived index of social disorganization (ISDOR) and a commercial geographic information system (GIS), we implement both linear statistical and non-linear data mining approaches to predict the presence of sex offenders in a community.  The results of this analysis are encouraging, with nearly 75% of registered offender locations predicted correctly. The implications of these approaches for public policy are discussed.
    Date: 2010
  8. By: Norbert Christopeit (University of Bonn); Michael Massmann (VU University Amsterdam)
    Abstract: In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic approximation algorithms whose gain sequence is decreasing to zero. Our focus is on the estimation of the parameters in the resulting actual law of motion. For a special case we show that the ordinary least squares estimator is consistent.
    Keywords: Adaptive learning; forecast feedback; stochastic approximation; linear regression with stochastic regressors; consistency
    JEL: C13 C22 D83 D84
    Date: 2010–08–23
  9. By: Yu-chin Chen; Kwok Ping Tsang; Wen Jen Tsay
    Abstract: The "home bias" phenomenon states that empirically, economic agents often under- utilize opportunities beyond their country borders, and it is well-documented in various international pricing and purchase patterns. This bias manifests in the forms of fewer exchanges of goods and net equity-holdings, as well as less arbitrage of price differences across borders than theoretically predicted to be optimal. Our paper documents another form of home bias, where market participants appear to under-weigh information beyond their borders when making currency forecasts. Using monthly data from 1995 to 2010 for seven major exchange rates relative to the US dollar, we show that excess currency returns and the errors in investors' consensus forecasts not only depend on the interest differentials between the pair of countries, but they depend more strongly on interest rates in a broader set of countries. A global short interest differential and a global long interest differential are driving the results.
    Keywords: Survey Data, Excess Currency Returns, Global Shock
    Date: 2010
  10. By: Satchell, S.; Williams, O.J.
    Abstract: The use of correlation between forecasts and actual returns is commonplace in the literature, often used as a measurement of investors’ skill. A prominent application of this is the concept of the Information Coefficient (IC). Not only can IC be used as a tool to rate analysts and fund managers but it also represents an important parameter in the asset allocation and portfolio construction process. Nevertheless, theoretical understanding of it has typically been limited to the partial equilibrium context where the investing activities of each agent have no effect on other market participants. In this paper we show that this can be an undesirable oversimplification and we demonstrate plausible circumstances in which conventional empirical measurements of IC can be highly misleading. We suggest that improved understanding of IC in a general equilibrium setting can lead to refined portfolio decision making ex ante and more informative analysis of performance ex post.
    Keywords: Performance measurement, skill, financial forecasting, active management, Information Coefficient, Information Ratio
    JEL: D53 D82 D84 G11
    Date: 2010–08–26

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