Testing investment forecast efficiency with textual data

I use textual data to model German professional macroeconomic forecasters' information sets and use machine-learning techniques to analyze the efficiency of forecasts. To this end, I extract information from forecast reports using a combination of topic models and word embeddings. I then use this information and traditional macroeconomic predictors to study the efficiency of investment forecasts.

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Year of publication:	2020
Authors:	Foltas, Alexander
Publisher:	Berlin : Humboldt University Berlin
Subject:	Forecast Efficiency \| Investment \| Random Forest \| Topic Modeling

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Series:	Working Papers of the Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour" ; 19
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	10.18452/21651 [DOI] 1728052467 [GVK] hdl:10419/223420 [Handle] RePEc:zbw:pp1859:19 [RePEc]
Classification:	C53 - Forecasting and Other Model Applications ; E27 - Forecasting and Simulation ; E22 - Capital; Investment (including Inventories); Capacity
Source:	EconStor

Persistent link: https://www.econbiz.de/10012269134