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on Forecasting |
| By: | Thiyanga S Talagala; Rob J Hyndman; George Athanasopoulos |
| Abstract: | A crucial task in time series forecasting is the identification of the most suitable forecasting method. We present a general framework for forecast-model selection using meta-learning. A random forest is used to identify the best forecasting method using only time series features. The framework is evaluated using time series from the M1 and M3 competitions and is shown to yield accurate forecasts comparable to several benchmarks and other commonly used automated approaches of time series forecasting. A key advantage of our proposed framework is that the time-consuming process of building a classifier is handled in advance of the forecasting task at hand. |
| Keywords: | FFORMS (Feature-based FORecast-model Selection), time series features, random forest, algorithm selection problem, classsification. |
| JEL: | C10 C14 C22 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2018-6&r=for |
| By: | Laura Liu |
| Abstract: | This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by pooling the information from the whole cross-section together. Theoretically, I prove that both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches. |
| Keywords: | Bayesian nonparametric methods ; Density forecasts ; Panel data ; Posterior consistency ; Young firm dynamics |
| JEL: | C11 C14 C23 C53 L25 |
| Date: | 2018–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2018-36&r=for |
| By: | Marie-Hélène Gagnon; Gabriel Power; Dominique Toupin |
| Abstract: | This paper investigates international index return predictability using option-implied information. We document the significant predictive power of the variance risk premium (VRP), Foster-Hart risk (FH), and higher-order moments for horizons ranging from 1 to 250 days. Our results from predictive regressions show that these four risk-neutral metrics, which have the advantage of daily updating, perform well internationally. VRP and FH risk are significant predictors for several horizons, including less than one month (VRP) and longer horizons (FH). Risk-neutral skewness and kurtosis are significant for several countries across multiple horizons. Out-of-sample forecasts and utility gain calculations confirm the statistical and economic significance of these risk-neutral variables internationally. |
| Keywords: | Options, risk-neutral distribution, variance risk premium, return predictability, predictive regressions, international stock market returns, Foster-Hart riskiness, higher-order moments, skewness |
| JEL: | C12 C22 G12 G13 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:crrecr:1807&r=for |
| By: | Zorzi, Michele Ca'; Rubaszek, Michał |
| Abstract: | This paper shows that there are two regularities in foreign exchange markets in advanced countries with flexible regimes. First, real exchange rates are mean-reverting, as implied by the Purchasing Power Parity model. Second, the adjustment takes place via nominal exchange rates. These features of the data can be exploited, even on the back of a napkin, to generate nominal exchange rate forecasts that outperform the random walk. The secret is to avoid estimating the pace of mean reversion and assume that relative prices are unchanged. Direct forecasting or panel data techniques are better than the random walk but fail to beat this simple calibrated model. JEL Classification: C32, F31, F37, F41 |
| Keywords: | exchange rates, forecasting, mean reversion, panel data, Purchasing Power Parity |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20182151&r=for |
| By: | Florian Ziel; Rafal Weron |
| Abstract: | We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling framework does not uniformly outperform the univariate one across all 12 considered datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This is an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. We show that this indeed can be the case, even for a simple averaging scheme involving only two models. Finally, we also analyze variable selection for the best performing high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.06649&r=for |