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on Forecasting |
| By: | Anna Matysiak (Max Planck Institute for Demographic Research, Rostock, Germany); Beata Nowok (Max Planck Institute for Demographic Research, Rostock, Germany) |
| Abstract: | Forecasting the population of Poland is very challenging. Firstly, the country has been undergoing rapid demographic changes. In the 1990s, Poland experienced a fundamental shift from a communist regime to a democratic regime and entered the European Union in 2004. The political, economic, and social changes that accompanied the transformation had a profound influence on the demographic patterns in this country. International migration has been one of the first consequences of Poland’s entry into the EU, and it is expected to increase in the future. Secondly, the availability of statistics for Poland on past trends is strongly limited. The resulting high uncertainty of future trends should be dealt with systematically, which is an essential part of the stochastic forecast. In this article, we present to the best of our knowledge a first stochastic forecast of the population of Poland. The forecast constitutes a valuable alternative to considering various scenarios that have been applied so far. The forecast results show that the Polish population will constantly decline during the next decades. There is a probability of 50 % that in 2050 the population will number between 27 and 35 millions compared to 38.2 in 2004. Besides, Poland will face significant ageing as indicated by a rising old-age dependency-ratio. In 45 years, there will be at least 63 persons aged 65+ per 100 persons aged 19-64, and this with a probability of 50 %. A description of the most important limitations to the official Polish demographic statistics and an analysis of past trends in fertility, mortality, and international migration are important by-products of this study. |
| Keywords: | Poland, population forecasts |
| JEL: | J1 Z0 |
| Date: | 2006–08 |
| URL: | http://d.repec.org/n?u=RePEc:dem:wpaper:wp-2006-026&r=for |
| By: | Carl Bonham (Department of Economics, University of Hawaii at Manoa); Richard Cohen (College of Business and Public Policy, University of Alaska Anchorage); Shigeyuki Abe (Center for Contemporary Asian Studies, Doshisha University) |
| Abstract: | This paper examines the rationality and diversity of industry-level forecasts of the yen-dollar exchange rate collected by the Japan Center for International Finance. In several ways we update and extend the seminal work by Ito (1990). We compare three specifications for testing rationality: the ”conventional” bivariate regression, the univariate regression of a forecast error on a constant and other information set variables, and an error correction model (ECM). We find that the bivariate specification, while producing consistent estimates, suers from two defects: first, the conventional restrictions are sucient but not necessary for unbiasedness; second, the test has low power. However, before we can apply the univariate specification, we must conduct pretests for the stationarity of the forecast error. We find a unit root in the six-month horizon forecast error for all groups, thereby rejecting unbiasedness and weak eciency at the pretest stage. For the other two horizons, we find much evidence in favor of unbiasedness but not weak eciency. Our ECM rejects unbiasedness for all forecasters at all horizons. We conjecture that these results, too, occur because the restrictions test suciency, not necessity. In our systems estimation and micro- homogeneity testing, we use an innovative GMM technique (Bonham and Cohen (2001)) that allows for forecaster cross-correlation due to the existence of common shocks and/or herd eects. Tests of micro-homogeneity uniformly reject the hypothesis that forecasters across the four industries exhibit similar rationality characteristics. |
| Keywords: | Rational Expectations, Heterogeneity, Exchange Rate, Survey Forecast |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:hai:wpaper:200611&r=for |
| By: | Kenneth D. West; Todd Clark |
| Abstract: | Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure. |
| JEL: | C22 C53 E17 F37 |
| Date: | 2006–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberte:0326&r=for |
| By: | Klaus Abberger (IFO Munich) |
| Abstract: | The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast errors using lead time as regressor. With this method there is no need for a distribution assumption. But for the data pattern in this case a double kernel method which allows smoothing in two directions is required. An estimation algorithm is presented and applied to some simulation examples. |
| Keywords: | Forecasting, Prediction intervals, Non normal distributions, Nonparametric estimation, Quantile regression |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0202&r=for |
| By: | John Malcolm Dowling (School of Economics and Social Sciences, Singapore Management University); Ganeshan Wignaraja (Asian Development Bank) |
| Abstract: | Central Asia has emerged as one of the world’s fastest growing regions since the late 1990s and has shown notable development potential. This is significant for a region comprising largely of small landlocked economies with no access to the sea for trade. Among the advantages, of the region are its high- priced commodities (oil, gas, cotton and gold), reasonable infrastructure and human capital as legacies of Soviet rule; and a strategic location between Asia and Europe. Furthermore, many Central Asian Republics (CARs) have embarked on market-oriented economic reforms to boost economic performance and private sector competitiveness. Central Asia: Mapping Future Prospects considers the region’s economic prospects to 2015. It charts recent economic performance, highlighting the economic revival. It also synthesizes recent forecasts and constructs scenarios for future economic variables against a constant global background. Projections include, among others, gross domestic product (GDP), manufactured exports per head, GDP per capita and poverty. A special theme chapter develops a manufacturing competitiveness index to compare the CARs with other transition economies and explores the impact of economic reform and supply-side factors (e.g. foreign investment and human capital) on industrial performance |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:05-2006&r=for |
| By: | Lei Lei Song (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne) |
| Abstract: | This paper examines the relationship between the general price level and the relative price of fuel by measuring correlation from VAR forecast errors. The results suggest a significant positive correlation between quarterly changes in the relative price of fuel and the CPI, at least in the short to medium term from two to four years. The finding has important implications for measuring the long-term trend in inflation as relative price changes in fuel contain important information about future inflation. |
| JEL: | E31 E37 |
| Date: | 2006–08 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2006n17&r=for |
| By: | Richard Deaves (McMaster University); Erik Lüders (Pinehill Capital and Laval University); Michael Schröder (Center for European Economic Research (ZEW)) |
| Abstract: | As a group, market forecasters are egregiously overconfident. In conformity to the dynamic model of overconfidence of Gervais and Odean (2001), successful forecasters become more overconfident. What’s more, more experienced forecasters have “learned to be overconfident,” and hence are more susceptible to this behavioral flaw than their less experienced peers. It is not just individuals who are affected. Markets also become more overconfident when market returns have been high. |
| Date: | 2005–10–07 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0510&r=for |