|
on Forecasting |
| By: | Mehmet Balcilar (Eastern Mediterranean University); Rangan Gupta (University of Pretoria); Stephen M. Miller (University of Nevada, Las Vegas and University of Connecticut) |
| 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:uct:uconnp:2012-12&r=for |
| By: | Francis X. Diebold (Department of Economics, University of Pennsylvania) |
| Abstract: | The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo-) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo-) out-of-sample analysis is somehow the “only," or “best," or even a “good" way to provide insurance against in-sample over fitting in model comparisons proves largely false. On the other hand, (pseudo-) out-of-sample analysis may be useful for learning about comparative historical predictive performance. |
| Keywords: | : Forecasting, model comparison, model selection, out-of-sample tests |
| JEL: | C01 C53 |
| Date: | 2012–09–07 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:12-035&r=for |
| By: | Pfajfar, D.; Zakelj, B. (Tilburg University, Center for Economic Research) |
| Abstract: | Abstract: This paper compares the behavior of subjects' uncertainty in different monetary policy environments when forecasting inflation in the laboratory. We find that inflation targeting produces lower uncertainty and higher accuracy of interval forecasts than inflation forecast targeting. We also establish several stylized facts about the behavior of individual uncertainty, aggregate distribution of forecasts, and disagreement between individuals. We find that the average confidence interval is the measure that performs best in forecasting inflation uncertainty. Subjects correctly perceive the underlying inflation uncertainty in only 60% of cases and tend to report asymmetric confidence intervals, perceiving higher uncertainty with respect to inflation increases. |
| Keywords: | Laboratory Experiments;Confidence Bounds;New Keynesian Model;Inflation Expectations. |
| JEL: | C91 C92 E37 D80 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:kubcen:2012072&r=for |
| By: | Charles Engel; Nelson C. Mark; Kenneth D. West |
| Abstract: | We construct factors from a cross section of exchange rates and use the idiosyncratic deviations from the factors to forecast. In a stylized data generating process, we show that such forecasts can be effective even if there is essentially no serial correlation in the univariate exchange rate processes. We apply the technique to a panel of bilateral U.S. dollar rates against 17 OECD countries. We forecast using factors, and using factors combined with any of fundamentals suggested by Taylor rule, monetary and purchasing power parity (PPP) models. For long horizon (8 and 12 quarter) forecasts, we tend to improve on the forecast of a “no change” benchmark in the late (1999-2007) but not early (1987-1998) parts of our sample. |
| JEL: | C53 C58 F37 G17 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18382&r=for |
| By: | Petar Jevtic; Elisa Luciano; Elena Vigna |
| Abstract: | We consider the problem of nding a suitable cohort-based model to capture the characteristics of a mortality surface with a parsimonious, continuous-time factor model. The model allows for imperfect correlation of mortality intensity across generations. It is implemented on UK data for the period 1900-2008. Calibration by means of stochastic search and the Differ- ential Evolution optimization algorithm proves to be outstanding and to yield robust and stable parameters. Both in-sample and out-of-sample, determin- istic as well as stochastic forecasts are examined. Calibration confirms that correlation across generations is smaller than one. |
| Keywords: | stochastic mortality, age effect, cohort effect, differential evolution algorithm, mortality forecasting. |
| JEL: | C1 C13 C38 C53 J11 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:264&r=for |
| By: | Francis X. Diebold |
| Abstract: | The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo-) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo-) out-of-sample analysis is somehow the "only," or "best," or even a "good" way to provide insurance against in-sample over-fitting in model comparisons proves largely false. On the other hand, (pseudo-) out-of-sample analysis may be useful for learning about comparative historical predictive performance. |
| JEL: | C01 C52 C53 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18391&r=for |
| By: | Bruno, Giancarlo |
| Abstract: | The consumer confidence index is a highly observed indicator among short-term analysts and news reporters and it is generally considered to convey some useful information about the short-term evolution of consumer expenditure. Notwithstanding this, its usefulness in forecasting aggregate consumption is sometimes questioned in empirical studies. Overall, the conclusions seem to be that the extensive press coverage about this indicator is somewhat undue. Nevertheless, from time to time, attention revamps on consumer confidence, especially when turns of the business cycle are expected and/or abrupt changes in this indicator occur. Some authors argue that in such events consumer confidence is a more relevant variable in predicting consumption. This fact can be a signal that a linear functional form is inadequate to explain the relationship between these two variables. Nevertheless, the choice of a suitable non-linear model is not straightforward. Here I propose that a non-parametric model can be a possible choice, in order to explore the usefulness of confidence in forecasting consumption, without making too restrictive assumptions about the functional form to use. |
| Keywords: | Forecasting; Consumer confidence; Non-parametric methods; Non linear methods |
| JEL: | C53 C14 E21 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:41312&r=for |
| By: | Dreger, Christian; Kholodilin, Konstantin A. |
| Abstract: | In this paper, the authors construct country-specific chronologies of house price bubbles for 12 OECD countries over the period 1969:Q1-2009:Q4. These chronologies are obtained using a combination of fundamental and filter approaches. The resulting speculative bubble chronology is the one providing the highest concordance between these two techniques. In addition, the authors suggest an early warning system based on three alternative approaches: signaling approach, logit, and probit models. It is shown that the latter two models allow much more accurate predictions of house price bubbles than the signaling approach. The prediction accuracy of the logit and probit models is high enough to make them useful in forecasting the future speculative bubbles in the housing market. Thus, this method can be used by policymakers in their attempts to timely detect house price bubbles and to attenuate their devastating effects on the domestic and world economy. -- |
| Keywords: | house prices,early warning system,OECD countries |
| JEL: | C25 C33 E32 E37 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:201244&r=for |
| By: | Nassim N. Taleb |
| Abstract: | Ex ante forecast outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. Reapplying measurements of uncertainty about the estimation errors of the estimation errors of an estimation leads to branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error. Nested counterfactuals of error rates invariably lead to fat tails, regardless of the probability distribution used, and to powerlaws under some conditions. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) higher moments than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails). |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1209.2298&r=for |
| By: | Katja Landau; Stephan Klasen; Walter Zucchini |
| Abstract: | We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply di erent regression models, based on household covariates and previous-year equivalence income, to classify a household as vulnerable or not. Predictive performance is assessed using the Receiver Operating Characteristics (ROC), which takes account of false positive as well as true positive rates. Estimates based on cross-sectional data are much less accurate than those based on panel data, but for Germany, the accuracy of vulnerability predictions is limited even when panel data are used. In part this low accuracy is due to low poverty incidence and high mobility in and out of poverty. |
| Keywords: | vulnerability, poverty, ROC, German panel data |
| JEL: | C23 C52 I32 O29 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp481&r=for |