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Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Jumah, Adusei; Kunst, Robert M. - 2008

Persistent link: https://www.econbiz.de/10003778880

Bayesian Compression for Mixed Frequency Vector Autoregressions : A Forecast Study for Germany

Götz, Thomas - 2018

Up until now, the concept of compression in single- or multivariate regressions has been limited to the common-frequency case. Having an application of macroeconomic forecasting in mind, one inevitably has to deal with variables sampled at various frequencies. Consequently, this work attempts to...

Persistent link: https://www.econbiz.de/10012912645

Factor structural time series models for official statistics with an application to hours worked in Germany

Weigand, Roland; Wanger, Susanne; Zapf, Ines - 2015

factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step …. The methods are applied to the estimation of paid and unpaid overtime work as well as flows on working-time accounts in … Germany, which enter the statistics on hours worked in the national accounts. …

Persistent link: https://www.econbiz.de/10011309972

In search of leading indicators of economic activity in Germany

Bandholz, Harm; Funke, Michael - 2001

In this paper we present two new composite leading indicators of economicactivity in Germany estimated using a dynamic …

Persistent link: https://www.econbiz.de/10011400394

Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility : International Evidence

Degiannakis, Stavros Antonios - 2018

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...

Persistent link: https://www.econbiz.de/10012910119

Nowcasting consumer price inflation using high-frequency scanner data : evidence from Germany

Beck, Günter W.; Carstensen, Kai; Menz, Jan-Oliver; … - 2023

We study how millions of highly granular and weekly household scanner data combined with novel machine learning techniques can help to improve the nowcast of monthly German inflation in real time. Our nowcasting exercise targets three hierarchy levels of the official consumer price index. First,...

Persistent link: https://www.econbiz.de/10014467924

Variable Selection, Estimation and Inference for Multi-Period Forecasting Problems

Pesaran, M. Hashem; Pick, Andreas; Timmermann, Allan - 2011

correlation in the residuals of the multi-period direct forecasting models we propose a new SURE-based estimation method and …

Persistent link: https://www.econbiz.de/10014042344

Chapter 8 Forecasting economic variables with nonlinear models

Teräsvirta, Timo - 2006

building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating …

Persistent link: https://www.econbiz.de/10014023698

The Role of High-Frequency Prices, Long Memory and Jumps for Value-at-Risk Prediction

Fuertes, Ana-Maria - 2012

individual VaR rejections and a block-bootstrap unconditional coverage test that is robust to estimation uncertainty and model …

Persistent link: https://www.econbiz.de/10013105936

Forecasts with Single-Equation Markov-Switching Model : An Application to the Gross Domestic Product of Latvia

Buss, Ginters - 2010

The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform...

Persistent link: https://www.econbiz.de/10013147524