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on Forecasting |
| By: | Arora, Vipin |
| Abstract: | I evaluate the out-of-sample forecasting performance of five models of Chinese and Indian energy consumption. The results are mixed, but in general the auto-regressive distributed lag and unobserved components models perform the best over multiple evaluation criteria. I then use these two models and generate long-term forecasts [2010-2040] for comparison with the International Energy Outlook of the U.S. Energy Information Administration and other similar publications. For both countries the forecasting models predict higher levels and growth rates of energy consumption than the published estimates. |
| Keywords: | energy consumption, forecast, projection, China, India |
| JEL: | C53 Q41 Q47 |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:48621&r=for |
| By: | Travis Berge |
| Abstract: | This paper evaluates the ability of several commonly followed economic indicators to predict business cycle turning points. As a baseline, forecasts from univariate models are combined by taking averages or by weighting forecasts with model-implied posterior probabilities. These combined forecasts are compared to those from a sophisticated model selection algorithm that allows for nonlinear model speci_cations. The preferred forecasting model is one that allows for nonlinear behavior across the business cycle and combines information from the yield curve with other indicators, especially at very short and very long horizons. |
| Keywords: | Recessions ; Economic indicators ; Business cycles |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp13-05&r=for |
| By: | Piergiorgio Alessandri; Haroon Mumtaz |
| Abstract: | The authors reassess the predictive power of financial indicators for output and inflation in the US by studying predictive densities generated by set of linear and nonlinear forecasting models. They argue that, if the linkage between financial and real economy is state-dependent as implied by standard models with financial frictions, predictive densities should reveal aspects of the co-movements between financial and macroeconomic variables that are ignored by construction in an ordinary (central) forecasting exercise. The authors study the performance of linear and nonlinear (Threshold and Markov-Switching) VARs estimated on a monthly US dataset including various commonly-used financial indicators. We obtain three important results. First, adding financial indicators to an otherwise standard VAR improves both central forecasts and predictive distributions for output, but the improvement is more substantial for the latter. Even in a linear model, financial indicators are more useful in predicting 'tails', or deviations of output and inflation from their expected paths, than 'means', namely the expected paths themselves. Second, nonlinear models with financial indicators tend to generate noisier central forecasts than their linear counterparts, but they clearly outperform them in predicting distributions. This is mainly because nonlinear models predict the likelihood of recessionary episodes more accurately. Third, the discrepancies between models are themselves predictable: a Bayesian forecaster can formulate a reasonable real-time guess on which model is likely to be more accurate in the near future. |
| Keywords: | Financial conditions, density forecasts, US, output, inflation |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:ccb:jrpapr:4&r=for |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Shawkat Hammoudeh (Lebow College of Business, Drexel University, Philadelphia, USA); Won Joong Kim (Department of Economics, Konkuk University, Seoul, Korea); Beatrice D. Simo-Kengne (Department of Economics, University of Pretoria) |
| Abstract: | We develop models for examining possible predictors of growth of China’s foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US-China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided. |
| Keywords: | BAyesian, state space models, foreign reserve, macroeconomic fundamentals, forecasting |
| JEL: | C11 C53 F37 F47 |
| Date: | 2013–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201338&r=for |
| By: | George Constantinides (University Of Chicago) |
| Abstract: | We model consumption and dividend growth as different processes across two latent regimes. We estimate the equilibrium model over 1930-2009 and show that the second regime is associated with recessions, market downturns, higher risk premia, lower consumption and dividend growth, higher volatility of returns and growth rates, and lower market-wide price-dividend ratio. The model performs better at in-sample forecasting and significantly better at out-of-sample prediction of the equity, size, and value premia and consumption and dividend growth rates and their variances than the price-dividend ratio and risk free rate do. The calibrated model replicates several features of the data. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed012:1197&r=for |
| By: | Eric Ghysels (UNC); Andros Kourtellos (University of Cyprus); Elena Andreou (University of Cyprus) |
| Abstract: | We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of Mixed Data Sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about one thousand daily financial assets. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis that started in 2007, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed012:1196&r=for |
| By: | Andrade, P.; Fourel, V.; Ghysels, E.; Idier, I. |
| Abstract: | Recent studies emphasize that survey-based inflation risk measures are informative about future inflation and thus useful for monetary authorities. However, these data are typically available at a quarterly frequency whereas monetary policy decisions require a more frequent monitoring of such risks. Using the ECB survey of professional forecasters, we show that high-frequency financial market data have predictive power for the low-frequency survey-based inflation risk indicators observed at the end of a quarter. We rely on MIDAS regressions to handle the problem of mixing data with different frequencies that such an analysis implies. We also illustrate that upside and downside risks react differently to financial indicators. |
| Keywords: | inflation forecasts, inflation risk, survey data, financial data, MIDAS regression. |
| JEL: | E31 E37 C53 C83 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:437&r=for |
| By: | Alec Smith; B. Douglas Bernheim; Colin Camerer; Antonio Rangel |
| Abstract: | We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “non-choice” neural responses and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. |
| JEL: | C91 D12 |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:19270&r=for |
| By: | Yannick Appert-Raullin (Group Risk Management, GIE AXA - GIE AXA); Laurent Devineau (Recherche et Développement, Milliman Paris - Milliman, SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429); Hinarii Pichevin (Recherche et Développement, Milliman Paris - Milliman); Philippe Tann (Group Risk Management, GIE AXA - GIE AXA) |
| Abstract: | The one-year prediction error (one-year MSEP) proposed by Merz and Wüthrich has become a market-standard approach for the assessment of reserve volatilities for Solvency II purposes. However, this approach is declined in a univariate framework. Moreover, Braun proposed a closed-formed expression of the prediction error of several run-off portfolios at the ultimate horizon by taking into account their dependency. This article proposes an analytical expression of the one-year MSEP obtained by generalizing the modeling developed by Braun to the one-year horizon with an approach similar to Merz and Wüthrich. A full mathematical demonstration of the formula has been provided in this paper. A case study is presented to assess the dependency between commercial and motor liabilities businesses based on data coming from a major international insurer. |
| Keywords: | multivariate reserving; correlation; run-off portfolio; prediction error; estimation error; process error; one-year multivariate reserve risk; claims development result; Solvency II; aggregation; dependency; lines of business |
| Date: | 2013–07–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00848492&r=for |
| By: | Newton, John; Thraen, Cameron S.; Bozic, Marin |
| Abstract: | In this analysis we compare the total expected government outlays and distribution of benefits under newly proposed dairy margin insurance programs to those under existing counter-cyclical payment programs. We combine simulation and structural modeling techniques to forecast milk price and dairy income-over-feed-cost margins. Using the price forecasts we employ Monte-Carlo experiments to evaluate the total expected government outlays for a sample of 5000 representative farms given a constant relative risk aversion utility framework. We find that expected outlays favor large farm operations and are an order of magnitude higher than those under existing programs. Under the current policy framework (MILC), farms with less than 100 cows (76% of farms) account for 42% of net payments and farms over 1000 cows (2% of farms) account for 6% of net payments. Under the new policy regime farms with fewer than 100 cows will get 17-21% of net program benefits, and farms over 1000 cows will get 36-43% of benefits. |
| Keywords: | dairy, margin insurance, farm bill, supply-management, dairy security act, dairy freedom act, Gini coefficient, farm payments, Agribusiness, Agricultural and Food Policy, Demand and Price Analysis, Farm Management, Livestock Production/Industries, Risk and Uncertainty, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:153750&r=for |
| By: | Y. Shi; A. N. Gorban; T. Y. Yang |
| Abstract: | This case study tests the possibility of prediction for "success" (or "winner") components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012). To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The "winner" components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The "loser" components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series. The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the winner components are closer to each other and winner/loser components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation. |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1307.8308&r=for |