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on Forecasting |
| By: | Clements, Michael P (Department of Economics, University of Warwick) |
| Abstract: | Survey respondents who make point predictions and histogram forecasts of macrovariables reveal both how uncertain they believe the future to be, ex ante, as well as their ex post performance. Macroeconomic forecasters tend to be overconfident at horizons of a year or more, but over-estimate the uncertainty surrounding their predictions at short horizons. JEL classification: C53 |
| Keywords: | Subjective uncertainty ; realized uncertainty ; output growth forecasts ; inflation forecasts. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:warwec:995&r=for |
| By: | Pablo Pincheira; Carlos A. Medel |
| Abstract: | We explore the ability of several univariate models to predict inflation in a number of countries and at several forecasting horizons. We place special attention on forecasts coming from a family of ten seasonal models that we call the Driftless Extended Seasonal ARIMA (DESARIMA) family. Using out-of-sample Root Mean Squared Prediction Errors (RMSPE) we compare the forecasting accuracy of the DESARIMA models with that of traditional univariate time-series benchmarks available in the literature. Our results show that DESARIMA-based forecasts display lower RMSPE at short horizons for every single country, except one. We obtain mixed results at longer horizons. Roughly speaking, in half of the countries, DESARIMA-based forecasts outperform the benchmarks at long horizons. Remarkably, the forecasting accuracy of our DESARIMA models is surprisingly high in stable inflation countries, for which the RMSPE is barely higher than 100 basis points when the prediction is made 24- and even 36-months ahead. |
| Date: | 2012–08 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:677&r=for |
| By: | Baumeister, Christiane; Kilian, Lutz |
| Abstract: | Recent research has shown that recursive real-time VAR forecasts of the real price of oil tend to be more accurate than forecasts based on oil futures prices of the type commonly employed by central banks worldwide. Such monthly forecasts, however, differ in several important dimensions from the forecasts central banks require when making policy decisions. First, central banks are interested in forecasts of the quarterly real price of oil rather than forecasts of the monthly real price of oil. Second, many central banks are interested in forecasting the real price of Brent crude oil rather than any of the U.S. benchmarks. Third, central banks outside the United States are interested in forecasting the real price of oil measured in domestic consumption units rather than U.S. consumption units. Addressing each of these three concerns involves modeling choices that affect the relative accuracy of alternative forecasting methods. In addition, we investigate the costs and benefits of allowing for time variation in VAR model parameters and of constructing forecast combinations. We conclude that quarterly forecasts of the real price of oil from suitably designed VAR models estimated on monthly data generate the most accurate forecasts among a wide range of methods including forecasts based on oil futures prices, nochange forecasts and forecasts based on models estimated on quarterly data. |
| Keywords: | Central banks; Forecasting methods; Oil futures prices; Out-of-sample forecast; Quarterly horizon; Real price of oil; Real-time data; VAR |
| JEL: | C53 E32 Q43 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:9118&r=for |
| By: | Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey); Rangan Gupta (Department of Economics, University of Pretoria); Stephen M. Miller (College of Business, University of Las Vegas, Nevada) |
| Abstract: | This paper provides out-of-sample forecasts of linear and non-linear models of US and Census regions housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts of the housing price distributions. The non-linear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive model dominates the non-linear smooth-transition autoregressive model at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and non-linear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. |
| Keywords: | Forecasting, Linear and non-linear models, US and Census housing price indexes |
| JEL: | C32 R31 |
| Date: | 2012–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201226&r=for |
| By: | Taro Ikeda (Kurume University, Faculty of Economics) |
| Abstract: | This paper introduces asymmetric central bank forecasting into the standard New Keynesian model within the context of robust control theory. Asymmetric forecasting expresses policymakersf reservations about economic forecasts, and the degree of their reservations is reflected as an asymmetric preference whose existence warrants laying aside the assumption that policymakersf base decisions primarily on rational expectations. This study concludes that monetary policy becomes more aggressive because of policymakersf reservations about forecasts stemming from asymmetry, and preference for policies robust enough to overcome unanticipated situations. In addition, adopted policies will likely amplify economic fluctuations and significantly reduce social welfare. |
| Keywords: | robust control, asymmetric forecasting, bounded rationality |
| JEL: | E50 E52 E58 |
| Date: | 2012–08 |
| URL: | http://d.repec.org/n?u=RePEc:koe:wpaper:1216&r=for |
| By: | Xisong Jin; Francisco Nadal De Simone |
| Abstract: | The estimation of banks? marginal probabilities of default using structural credit risk models can be enriched incorporating macro-financial variables readily available to economic agents. By combining Delianedis and Geske?s model with a Generalized Dynamic Factor Model into a dynamic t-copula as a mechanism for obtaining banks? dependence, this paper develops a framework that generates an early warning indicator and robust out-of-sample forecasts of banks? probabilities of default. The database comprises both a set of Luxembourg banks and the European banking groups to which they belong. The main results of this study are, first, that the common component of the forward probability of banks? defaulting on their long-term debt, conditional on not defaulting on their short-term debt, contains a significant early warning feature of interest for an operational macroprudential framework driven by economic activity, credit and interbank activity. Second, incorporating the common and the idiosyncratic components of macro-financial variables improves the analytical features and the out-of-sample forecasting performance of the framework proposed. |
| Keywords: | financial stability, macroprudential policy, credit risk, early warning indicators, default probability, Generalized Dynamic Factor Model, dynamic copulas, GARCH |
| JEL: | C30 E44 G1 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:bcl:bclwop:bclwp075&r=for |
| By: | Banbura, Marta; Giannone, Domenico; Modugno, Michele; Reichlin, Lucrezia |
| Abstract: | The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics In this paper we survey recent developments on economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland). |
| Keywords: | Dynamic factor model; High-Dimensional Data; Macroeconomic forecasting; Macroeconomic News; Mixed-Frequency; Real-Time Data; State-Space Models |
| JEL: | C01 C33 C53 E32 E37 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:9112&r=for |
| By: | Bhirombhakdi, Kornpob; Potipiti, Tanapong |
| Abstract: | This study experimentally tests the performance in predicting decisions of a reciprocity model that was proposed by Dufwenberg et al. (2004). By applying a new approach, the study directly and individually predicts a subject's future decision from his past decision. The prediction performance is measured by the rate of correct predictions (accuracy) and the gain in the rate of the correct predictions (informativeness). Six scenarios of trust game are used to test the model's performance. Further, we compare the performance of the model with two other prediction methods; one method uses a decision in a dictator game to predict a decision in a trust game; the other uses personal information including IQ-test scores, personal attitudes and socio-economic factors. Seventy-nine undergraduate students participated in this hand-run experimental study. The results show that the reciprocity model has the best performance when compared with other prediction methods. |
| Keywords: | Reciprocity; Model performance; Trust game |
| JEL: | C91 |
| Date: | 2012–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:40954&r=for |
| By: | Marcelo C. Medeiros (Pontifical Catholic University of Rio de Janeiro); Eduardo F. Mendes (Pontifical Catholic University of Rio de Janeiro) |
| Abstract: | We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows the method performs well in very general settings. Finally, we consider two applications: in the first one the goal is to forecast quarterly US inflation one-step ahead, and in the second we are interested in the excess return of the S&P 500 index. The method used outperforms the usual benchmarks in the literature. |
| Keywords: | sparse models, shrinkage, LASSO, adaLASSO, time series, forecasting. |
| JEL: | C22 |
| Date: | 2012–09–04 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2012-37&r=for |
| By: | Rebecca B. Morton (Department of Politics, NYU); Marco Piovesan (Harvard University); Jean-Robert Tyran (Department of Economics, University of Vienna, and Department of Economics, University of Copenhagen) |
| Abstract: | We experimentally investigate information aggregation through majority voting when some voters are biased. In such situations, majority voting can have a "?dark side"?, i.e. result in groups making choices inferior to those made by individuals acting alone. We develop a model to predict how two types of social information shape efficiency in the presence of biased voters and we test these predictions using a novel experimental design. In line with predictions, we find that information on the popularity of policy choices is beneficial when a minority of voters is biased, but harmful when a majority is biased. In theory, information on the success of policy choices elsewhere de-biases voters and alleviates the inefficiency. In the experiment, providing social information on success is ineffective. While voters with higher cognitive abilities are more likely to be de-biased by such information, most voters do not seem to interpret such information rationally. |
| JEL: | C92 D7 D02 D03 |
| Date: | 2012–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:1208&r=for |