In search of a job: Forecasting employment growth using Google Trends

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@techreport{36ef10f2d4cb4170a7d518775fbbc315,
title = "In search of a job: Forecasting employment growth using Google Trends",
abstract = "We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US over the period 2004-2018 at both short and long horizons. Using a subset of ten keywords associated with “jobs”, we construct a large panel of 173 variables using Google’s own algorithms to find related search queries. We find that the best Google Trends model achieves an out-of-sample R2 between 26{\%} and 59{\%} at horizons spanning from one month to a year ahead, strongly outperforming benchmarks based on a large set of macroeconomic and financial predictors. This strong predictability extends to US state-level employment growth, using state-level specific Google search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a non-linear model it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.",
keywords = "Google Trends, Forecast comparison, US employment growth, Targeting predictors, Random forests, Keyword search",
author = "Daniel Borup and {Montes Sch{\"u}tte}, {Erik Christian}",
year = "2019",
month = "8",
day = "22",
language = "English",
pages = "1",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - In search of a job: Forecasting employment growth using Google Trends

AU - Borup, Daniel

AU - Montes Schütte, Erik Christian

PY - 2019/8/22

Y1 - 2019/8/22

N2 - We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US over the period 2004-2018 at both short and long horizons. Using a subset of ten keywords associated with “jobs”, we construct a large panel of 173 variables using Google’s own algorithms to find related search queries. We find that the best Google Trends model achieves an out-of-sample R2 between 26% and 59% at horizons spanning from one month to a year ahead, strongly outperforming benchmarks based on a large set of macroeconomic and financial predictors. This strong predictability extends to US state-level employment growth, using state-level specific Google search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a non-linear model it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.

AB - We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US over the period 2004-2018 at both short and long horizons. Using a subset of ten keywords associated with “jobs”, we construct a large panel of 173 variables using Google’s own algorithms to find related search queries. We find that the best Google Trends model achieves an out-of-sample R2 between 26% and 59% at horizons spanning from one month to a year ahead, strongly outperforming benchmarks based on a large set of macroeconomic and financial predictors. This strong predictability extends to US state-level employment growth, using state-level specific Google search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a non-linear model it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.

KW - Google Trends

KW - Forecast comparison

KW - US employment growth

KW - Targeting predictors

KW - Random forests

KW - Keyword search

M3 - Working paper

SP - 1

BT - In search of a job: Forecasting employment growth using Google Trends

CY - Aarhus

ER -