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Of all publications in the section: 4
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Article
Kholodilin K., Ulbricht D. Journal of Forecasting. 2017. Vol. 36. No. 5. P. 483-496.

In an uncertain world, decisions by market participants are based on expectations. Therefore, sentiment indicators reflecting expectations have a proven track record at predicting economic variables. However, survey respondents largely perceive the world through media reports. Here, we want to make use of that. We employ a rich dataset provided by Media Tenor International, based on sentiment analysis of opinion-leading media in Germany from 2001 to 2014, transformed into several monthly indices. German industrial production is predicted in a real-time out-of-sample forecasting experiment and media indices are compared to a huge set of alternative indicators. Media data turn out to be valuable for 10- to 12-month horizon forecasts, which is in line with the lag between monetary policy announcements and their effect on industrial production. This holds in the period during and after the Great Recession when many models fail.

Added: Jan 31, 2019
Article
Kholodilin K., Dreger C. Journal of Forecasting. 2013. Vol. 32. No. 1. P. 10-18.

Survey-based indicators such as the consumer confidence are widely seen as leading indicators for economic activity, especially for the future path of private consumption. Although they receive high attention in the media, their forecasting power appears to be very limited. Therefore, this paper takes a fresh look on the survey data, which serve as a basis for the consumer confidence indicator (CCI) reported by the EU Commission for the euro area and individual member states. Different pooling methods are considered to exploit the information embedded in the consumer survey. Quantitative forecasts are based on Mixed Data Sampling (MIDAS) and bridge equations. While the CCI does not outperform an autoregressive benchmark for the majority of countries, the new indicators increase the forecasting performance. The gains over the CCI are striking for Italy and the entire euro area (20 percent). For Germany and France the gains seem to be lower, but are nevertheless substantial (10 to 15 percent). The best performing indicator should be built upon pre-selection methods, while data-driven aggregation methods should be preferred to determine the weights of the individual ingredients.

Added: Feb 1, 2019
Article
Kholodilin K. Journal of Forecasting. 2014. Vol. 33. No. 1. P. 15-31.

We evaluate the informational content of ex post and ex ante predictors of periods of excess stock (market) valuation. For a cross section comprising 10 OECD economies and a time span of at most 40 years alternative binary chronologies of price bubble periods are determined. Using these chronologies as dependent processes and a set of macroeconomic and financial variables as explanatory variables, logit regressions are carried out. With model estimates at hand, both in-sample and out-of-sample forecasts are made. Overall, the degree of ex ante predictability is limited if an analyst targets the detection of particular turning points of market valuation. The set of 13 potential predictors is classified in measures of macroeconomic or monetary performance, stock market characteristics, and descriptors of capital valuation. The latter turn out to have strongest in-sample and out-of-sample explanatory content for the emergence of price bubbles. In particular, the price to book ratio is fruitful to improve the ex-ante signalling of stock price bubbles.

Added: Apr 17, 2017
Article
Ozhegov E. M., Ozhegova A. Journal of Forecasting. 2019. Vol. 39. No. 3. P. 489-500.

In this research we analyze a new approach for prediction of demand. In the studied market of performing arts the observed demand is limited by capacity of the house. Then one needs to account for demand censorship to obtain unbiased estimates of demand function parameters. The presence of consumer segments with different purposes of going to the theater and willingness‐to‐pay for performance and ticket characteristics causes a heterogeneity in theater demand. We propose an estimator for prediction of demand that accounts for both demand censorship and preferences heterogeneity. The estimator is based on the idea of classification and regression trees and bagging prediction aggregation extended for prediction of censored data. Our algorithm predicts and combines predictions for both discrete and continuous parts of censored data. We show that our estimator performs better in terms of prediction accuracy compared with estimators which account either for censorship or heterogeneity only. The proposed approach is helpful for finding product segments and optimal price setting.

Added: Nov 24, 2019