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on Forecasting |
| By: | Konstantin A. Kholodilin; Maximilian Podstawski; Boriss Siliverstovs |
| Abstract: | In this paper, we investigate whether the Google search activity can help in nowcasting the year-on-year growth rates of monthly US private consumption using a real-time data set. The Google-based forecasts are compared to those based on a benchmark AR(1) model and the models including the consumer surveys and financial indicators. According to the Diebold-Mariano test of equal predictive ability, the null hypothesis can be rejected suggesting that Google-based forecasts are significantly more accurate than those of the benchmark model. At the same time, the corresponding null hypothesis cannot be rejected for models with consumer surveys and financial variables. Moreover, when we apply the test of superior predictive ability (Hansen, 2005) that controls for possible data-snooping biases, we are able to reject the null hypothesis that the benchmark model is not inferior to any alternative model forecasts. Furthermore, the results of the model confidence set (MCS) procedure (Hansen et al., 2005) suggest that the autoregressive benchmark is not selected into a set of the best forecasting models. Apart from several Google-based models, the MCS contains also some models including survey-based indicators and financial variables. We conclude that Google searches do help improving the nowcasts of the private consumption in US. |
| Keywords: | Google indicators, real-time nowcasting, principal components, US private consumption |
| JEL: | C22 C53 C82 |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp997&r=for |
| By: | Tierney, Heather L.R. |
| Abstract: | This paper examines whether core inflation is able to predict the overall trend of total inflation using real-time data in a parametric and nonparametric framework. Specifically, two sample periods and five in-sample forecast horizons in two measures of inflation, which are the personal consumption expenditure and the consumer price index, are used in the exclusions-from core inflation persistence model. This paper finds that core inflation is only able to capture the overall trend of total inflation for the twelve-quarter in-sample forecast horizon using the consumer price index in both the parametric and nonparametric models in the longer sample period. The nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons. |
| Keywords: | Inflation Persistence; Real-Time Data; Monetary Policy; Nonparametrics; In-Sample Forecasting |
| JEL: | C53 C14 E52 |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:22409&r=for |
| By: | Robin Greenwood; Samuel Hanson |
| Abstract: | We use differences between the attributes of stock issuers and repurchasers to forecast characteristic-related stock returns. For example, we show that large firms underperform following years when issuing firms are large relative to repurchasing firms. Our approach is useful for forecasting returns to portfolios based on book-to-market (HML), size (SMB), price, distress, payout policy, profitability, and industry. We consider interpretations of these results based on both time-varying risk premia and mispricing. Our results are primarily consistent with the view that firms issue and repurchase shares to exploit time-varying characteristic mispricing. |
| JEL: | G14 G3 G32 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:15948&r=for |
| By: | Claudia Miani (Bank of Italy); Stefano Siviero (Bank of Italy) |
| Abstract: | It has increasingly become standard practice to supplement point macroeconomic forecasts with an appraisal of the degree of uncertainty and the prevailing direction of risks. Several alternative approaches have been proposed in the literature to compute the probability distribution of macroeconomic forecasts; all of them rely on combining the predictive density of model-based forecasts with subjective judgment about the direction and intensity of prevailing risks. We propose a non-parametric, model-based simulation approach, which does not require specific assumptions to be made regarding the probability distribution of the sources of risk. The probability distribution of macroeconomic forecasts is computed as the result of model-based stochastic simulations which rely on re-sampling from the historical distribution of risk factors and are designed to deliver the desired degree of skewness. By contrast, other approaches typically make a specific, parametric assumption about the distribution of risk factors. The approach is illustrated using the Bank of Italy’s Quarterly Macroeconometric Model. The results suggest that the distribution of macroeconomic forecasts quickly tends to become symmetric, even if all risk factors are assumed to be asymmetrically distributed. |
| Keywords: | macroeconomic forecasts, stochastic simulations, balance of risks, uncertainty, fan-charts |
| JEL: | C14 C53 E37 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_758_10&r=for |
| By: | Giovanni De Luca (Dipartimento di Statistica e Matematica per la Ricerca Economica Università di Napoli Parthenope.); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti") |
| Abstract: | In this paper we model the dynamics of realized volatility as a Multiplicative Error Model with a mixture of distributions for the innovation term with time-varying mixing weights forced by past behavior of volatility. The mixture considers innovations as a source of time-varying volatility of volatility and is able to capture the right tail behavior of the distribution of volatility. The empirical results show that there is no substantial difference in the one-step ahead conditional expectations obtained according to various mixing schemes but that fixity of mixing weights may be a binding constraint in deriving accurate quantiles of the predicted distribution. |
| Keywords: | Multiplicative Error Models, Realized Volatility, Mixture Distributions |
| JEL: | C22 C51 C53 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2010_03&r=for |