Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Nowcasting Prices Using Google Trends

Authors: Seabold, Skipper; Coppola, Andrea;

Nowcasting Prices Using Google Trends

Abstract

The objective of this study is to assess the possibility of using Internet search keyword data for forecasting price series in Central America, focusing on Costa Rica, El Salvador, and Honduras. The Internet search data comes from Google Trends. The paper introduces these data and discusses some of the challenges inherent in working with it in the context of developing countries. A new index is introduced for consumer search behavior for these countries using Google Trends data covering a two-week period during a single month. For each country, the study estimates one-step-ahead forecasts for several dozen price series for food and consumer goods categories. The study finds that the addition of the Internet search index improves forecasting over benchmark models in about 20 percent of the series. The paper discusses the reasons for the varied success and potential avenues for future research.

Country
United States
Related Organizations
Keywords

PROBABILITIES, COMMUNICATIONS, INDICATORS, HTML, DATA, INFORMATION, AGRICULTURE, ABBREVIATION, SAMPLES, ECONOMIC THEORY, CONSUMERS, CASE, RESEARCH, SOFTWARE, SEARCH TERM, ARIMA, FUTURE RESEARCH, GDP, VARIABLES, MEASUREMENT, SEARCH TERMS, DISTRIBUTED LAGS, RAW DATA, BASE YEAR, CRITERIA, OPEN ACCESS, INDEX, STATISTICAL METHODOLOGY, LAGS, UNEMPLOYMENT, VALUE, MACROECONOMICS, CONTENT, BENCHMARK, ABBREVIATIONS, USES, BENCHMARKS, STATISTICS, WEB, VARIABILITY, SEARCH QUERY, QUERY, GOODS, SEARCHING, EXPONENTIAL SMOOTHING, E-MAIL, PRICE, MACHINE LEARNING, FORECASTS, 330, CASES, ECONOMIC FORECASTING, LINEAR REGRESSION, ERRORS, DATA MINING, GROWTH RATE, ECONOMICS RESEARCH, SEARCHES, PRICES, TIME SERIES, SEARCH, FORECASTING, OPTIMIZATION, INTERNET, WELFARE, EXPECTATIONS, NOTATION, RESULTS, INTEREST, SAMPLING, SEARCH BEHAVIOR, TAXONOMY, THEORY, PERFORMANCE, LEAST SQUARES METHOD, DEVELOPMENT POLICY, INDICES, TRENDS, MISSING OBSERVATIONS, LINEAR MODELS, SEARCH ENGINE, FACTOR ANALYSIS, CONSUMER GOODS, LEADING INDICATORS, AUTOMOBILE, MATRIX, PRINCIPAL COMPONENTS ANALYSIS, SURVEYS, ARMA

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green