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on Forecasting |
| By: | Ivan Kitov; Oleg Kitov |
| Abstract: | We model the rate of inflation and unemployment in Austria since the early 1960s within the Phillips/Fisher framework. The change in labour force is the driving force representing economic activity in the Phillips curve. For Austria, this macroeconomic variable was first tested as a predictor of inflation and unemployment in 2005 with the involved time series ended in 2003. Here we extend all series by nine new readings available since 2003 and re-estimate the previously estimated relationships between inflation, unemployment, and labour force. As before, a structural break is allowed in these relationships, which is related to numerous changes in definitions in the 1980s. The break year is estimated together with other model parameters by the Boundary Element Method with the LSQ fitting between observed and predicted integral curves. The precision of inflation prediction, as described by the root-mean-square (forecasting) error is by 20% to 70% better than that estimated by AR(1) model. The estimates of model forecasting error are available for those time series where the change in labour force leads by one (the GDP deflator) or two (CPI) years. For the whole period between 1965 and 2012 as well as for the intervals before and after the structural break (1986 for all inflation models) separately, our model is superior to the na\"ive forecasting, which in turn, is not worse than any other forecasting model. The level of statistical reliability and the predictive power of the link between inflation and labour force imply that the National Bank of Austria does not control inflation and unemployment beyond revisions to definitions. The labour force projection provided by Statistic Austria allows foreseeing inflation at a forty-year horizon: the rate of CPI inflation will hover around 1.3% and the GDP deflator will likely sink below zero between 2018 and 2034. |
| Date: | 2013–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1310.1786&r=for |
| By: | Konstantin Fursov (National Research University Higher School of Economics, Institute for Statistical Studies and Economics of Knowledge, International Laboratory for Economics of Innovation Research Fellow); Ian Miles |
| Abstract: | How can we think about the implications of radical technological change for employment and skills? Given the long lead-times required to train professionals, this is an important question, and standard approaches to modeling employment and occupational trends only provide limited parts of the answer. Innovation studies provide us with some further tools for tackling the question, such as diffusion and industry life-cycle analysis, and ideas about different sorts of technological change (including technical paradigms, regimes, and trajectories of change), which are very relevant to emerging technologies like nanotechnology. There are many claims and much argument about the scope and speed of the evolution of nanotechnology. It poses particular challenges to conventional forecasting approaches precisely because it is difficult to resolve such debates in the infancy of a technology, and in this case knowledge is fragmented because of the intersection of numerous lines of development at the nano-scale. Current skill and employment projections for nanoindustries are problematic, so it is important to consider new ways to improve understanding and provide more policy-relevant intelligence. |
| Keywords: | emerging technologies, nanotechnology, statistics, Foresight, skills |
| JEL: | C18 C46 C53 Z13 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:wpbrp15sti2013&r=for |
| By: | Ladislav Kristoufek |
| Abstract: | Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample. |
| Date: | 2013–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1310.1444&r=for |
| By: | Larry G. Epstein (Department of Economics, Boston University); Kyoungwon Seo |
| Abstract: | We generalize de Finetti’s exchangeable Bayesian model to accommodate ambiguity. As a central motivating example, we consider a policy maker facing a cross-section of markets in which …rms play an entry game. Her theory is Nash equilibrium and it is incomplete because there are multiple equilibria and she does not understand how equilibria are selected. This leads to partial identi…cation of parameters when drawing inferences from realized outcomes in some markets and to ambiguity when considering (policy) decisions for other markets. We model both her inference and choice. |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:bos:wpaper:wp2013-001&r=for |