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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>trendEcon</title>
<meta charset="utf-8" />
<meta name="author" content=" Angelica Becerra (ETH KOF) Vera Z. Eichenauer (ETH KOF) Ronald Indergand (SECO) Stefan Legge (University of St.Gallen) Isabel Martinez (ETH KOF, formerly SGB) Nina Mühlebach (ETH KOF) Furkan Oguz (ETH KOF) Christoph Sax (cynkra) Kristina Schuepbach (SGB and University of Bern) Severin Thöni (ETH KOF) Uwe Thümmel (University of Zurich)" />
<link rel="stylesheet" href="css_files\shinobi.css" type="text/css" />
<link rel="stylesheet" href="css_files\ninpo.css" type="text/css" />
<link rel="stylesheet" href="css_files\ninjutsu.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# trendEcon
## Daily economic indicators based on Google searches
### <br>Angelica Becerra (ETH KOF)<br>Vera Z. Eichenauer (ETH KOF)<br>Ronald Indergand (SECO)<br>Stefan Legge (University of St.Gallen)<br>Isabel Martinez (ETH KOF, formerly SGB)<br>Nina Mühlebach (ETH KOF)<br>Furkan Oguz (ETH KOF)<br>Christoph Sax (cynkra)<br>Kristina Schuepbach (SGB and University of Bern)<br>Severin Thöni (ETH KOF)<br>Uwe Thümmel (University of Zurich)
### <br>pre-conference eRum2020::CovidR / 2020-05-29
---
exclude: true
<style type="text/css">
code.r{
font-size: 16px;
}
pre {
font-size: 16px !important;
}
</style>
---
class: bg-main1
# Why trendEcon?
<br><br>
--
### During the Covid-19 pandemic, information about the economic and social situation has changed rapidly. 📉
<br><br>
--
### Traditional economic indicators have a significant .yellow[lag] of up to 3 months, and are .yellow[not sufficiently frequent] to monitor the economy and social activity at high frequency during times of rapid change 🕣
<br><br>
--
### The project was initiated during the [#versusvirus](https://www.versusvirus.ch/) hackathon from April 3 to April 5.
<br><br>
---
class: bg-main1
# Our solution
--
### We use Google search trends to overcome this data gap and create meaningful indicators.
--
### We extract daily search data on keywords reflecting consumers' perception.
<img src="slide_img/gtrend_ex.gif" width=80%>
---
layout: true
class: split-two with-border border-white
.column[
.split-three[
.row.bg-main1[.content.font2[
Indicators based on Google Trends.
]]
.row.bg-main2[.content.font2[
R package [trendecon](https://trendecon.github.io/trendecon/)
]]
.row.bg-main3[.content.font2[
Website [www.trendecon.org](https://www.trendecon.org/) using flexdashboard.
]]
]]
.column.bg-main1[.content.center.vmiddle[
{{content}}
]]
---
class: hide-row2-col1 hide-row3-col1
<img src="slide_img/trendecon.gif" width=100%>
---
class: hide-row3-col1
<img src="slide_img/package.png" width=100%>
---
class:
<img src="slide_img/web.png" width=100%>
---
class: fade-row2-col1 fade-row3-col1
<img src="slide_img/trendecon.gif" width=100%>
---
layout: false
class: bg-main1
# General steps
.blockquote.font_large[
Use Google Trends data for keywords such as “Wirtschaftskrise” and “Insolvenz”. </br></br>
]
---
class: bg-main1
# General steps
.blockquote.font_large[
Use Google Trends data for keywords such as “Wirtschaftskrise” and “Insolvenz”. </br></br>
Retrieve Google Trends data through the Google API.</br></br>
]
---
class: bg-main1
# General steps
.blockquote.font_large[
Use Google Trends data for keywords such as “Wirtschaftskrise” and “Insolvenz”. </br></br>
Retrieve Google Trends data through the Google API.</br></br>
Construct indicators using PCA.</br></br>
]
---
class: bg-main1
# General steps
.blockquote.font_large[
Use Google Trends data for keywords such as “Wirtschaftskrise” and “Insolvenz”. </br></br>
Retrieve Google Trends data through the Google API.</br></br>
Construct indicators using PCA.</br></br>
Compare the data to existing economic indicators.</br></br>
]
---
class: middle bg-main1
# It looks very simple....but
<img src="slide_img/no.gif" width=50%>
---
class: bg-main1
#Sampling issues
##If you query Google Trends for a search term, e.g., insolvenz, the result is based on a subsample of all search results.
</br>
--
<!-- -->
--
---
class: bg-main1
#Available data
</br>
##Google search results are available on a daily, weekly or monthly frequency; depending on the time window queried.
</br>
##Our goal is to produce long daily time series, ideally from 2006, but Google does not provide daily or weekly data for such a long time period.
---
class: middle bg-main1
# How did we solve this?
</br>
# You can check [www.trendecon.org/#method](https://www.trendecon.org/#method)
</br>
# We create the R package [trendecon](https://trendecon.github.io/trendecon/)
</br>
---
class: split-two with-border border-white fade-row1-col1 fade-row3-col1
.column[
.split-three[
.row.bg-main1[.content.font2[
Indicators based on Google Trends.
]]
.row.bg-main2[.content.font2[
R package [trendecon](https://trendecon.github.io/trendecon/)
]]
.row.bg-main3[.content.font2[
Website .yellow[www.trendecon.org] using flexdashboard.
]]
]]
.column.bg-main1[.content.center.vmiddle[
<img src="slide_img/package.png" width=100%>
]]
---
class: bg-main1
#[trendecon](https://trendecon.github.io/trendecon/) package
</br>
</br>
.font_large[
+ Construct long daily time series from Google Trends
]
---
class: bg-main1
#[trendecon](https://trendecon.github.io/trendecon/) package
</br>
</br>
.font_large[
+ Construct long daily time series from Google Trends.
</br>
</br>
+ Robustness of the data is achieved by querying Google mulitple times.
]
---
class: bg-main1
#[trendecon](https://trendecon.github.io/trendecon/) package
</br>
</br>
.font_large[
+ Construct long daily time series from Google Trends.
</br>
</br>
+ Robustness of the data is achieved by querying Google mulitple times.
</br>
</br>
+ The queries are sampled at daily, weekly and monthly frequencies and then harmonized such that the long term trend is preserved.
]
---
class: bg-main1
#[trendecon](https://trendecon.github.io/trendecon/) package
</br>
</br>
.font_large[
+ Construct long daily time series from Google Trends.
</br>
</br>
+ Robustness of the data is achieved by querying Google mulitple times.
</br>
</br>
+ The queries are sampled at daily, weekly and monthy frequencies and then harmonized such that the long term trend is preserved.
</br>
</br>
+ The download itself relies on the [gtrendsR](https://cran.r-project.org/web/packages/gtrendsR/index.html) package by Philippe Massicotte and Dirk Eddelbuettel.
]
---
class: bg-main1
# Installation
## You can install the trendecon package from GitHub.
```r
# install.packages("remotes")
remotes::install_github("trendecon/trendecon")
```
---
class: bg-main1
# Usage
## To download a series from Google Trends:
```r
x <- ts_gtrends("cinema", geo = "CH")
```
```
## Downloading data for today+5-y
```
```r
#> Downloading data for today+5-y
tsbox::ts_plot(x)
```
<!-- -->
---
class: split-25 bg-main1 fade-row2-col2 with-border
.row[
#Usage
Same parameters as ``gtrendsR::gtrends()``.<br>But with ``trendecon::ts_gtrends()`` you will have independent downloads for each keyword.]
.row[
.split-two.with-border[
.column.bg-main2[.content[
.center[``gtrendsR::gtrends()``]
```r
gtrends_dwload <- gtrends(keyword = c("theater","kino"),
geo = "CH",
time = "today 12-m")
gtrends_dwload$interest_over_time %>% ggplot(...)
```
<!-- -->
]]
.column.bg-main3[.content[
.center[``trendecon::ts_gtrends()``]
```r
trendecon_dwload <- ts_gtrends(keyword = c("theater","kino"),
geo = "CH",
time = "today 12-m")
tsbox::ts_ggplot(trendecon_dwload) + ...
```
<!-- -->
]]
]]
---
class: split-25 bg-main1 fade-row2-col1 with-border
.row[
#Usage
Same parameters as ``gtrendsR::gtrends()``.<br>But with ``trendecon::ts_gtrends()`` you will have independent downloads for each keyword.]
.row[
.split-two.with-border[
.column.bg-main2[.content[
.center[``gtrendsR::gtrends()``]
```r
gtrends_dwload <- gtrends(keyword = c("theater","kino"),
geo = "CH",
time = "today 12-m")
gtrends_dwload$interest_over_time %>% ggplot(...)
```
<!-- -->
]]
.column.bg-main3[.content[
.center[``trendecon::ts_gtrends()``]
```r
trendecon_dwload <- ts_gtrends(keyword = c("theater","kino"),
geo = "CH",
time = "today 12-m")
tsbox::ts_ggplot(trendecon_dwload) + ...
```
<!-- -->
]]
]]
---
class: split-25 bg-main1 fade-row2-col2 with-border
.row[
#Usage
Independent download is important because you might end up with many zeros.]
.row[
.split-two.with-border[
.column.bg-main2[.content[
.center[``gtrendsR::gtrends()``]
```r
gtrends_dwload <- gtrends(keyword = c("insolvenz","kino"),
geo = "CH",
time = "today 12-m")
gtrends_dwload$interest_over_time %>% ggplot(...)
```
<!-- -->
]]
.column.bg-main3[.content[
.center[``trendecon::ts_gtrends()``]
```r
trendecon_dwload <- ts_gtrends(keyword = c("insolvenz","kino"),
geo = "CH",
time = "today 12-m")
tsbox::ts_ggplot(trendecon_dwload) + ...
```
<!-- -->
]]
]]
---
class: split-25 bg-main1 fade-row2-col1 with-border
.row[
#Usage
Independent download is important because you might end up with many zeros.]
.row[
.split-two.with-border[
.column.bg-main2[.content[
.center[``gtrendsR::gtrends()``]
```r
gtrends_dwload <- gtrends(keyword = c("insolvenz","kino"),
geo = "CH",
time = "today 12-m")
gtrends_dwload$interest_over_time %>% ggplot(...)
```
<!-- -->
]]
.column.bg-main3[.content[
.center[``trendecon::ts_gtrends()``]
```r
trendecon_dwload <- ts_gtrends(keyword = c("insolvenz","kino"),
geo = "CH",
time = "today 12-m")
tsbox::ts_ggplot(trendecon_dwload) + ...
```
<!-- -->
]]
]]
---
class: split-two with-border border-white fade-row1-col1 fade-row2-col1
.column[
.split-three[
.row.bg-main1[.content.font2[
Indicators based on Google Trends.
]]
.row.bg-main2[.content.font2[
R package [trendecon](https://trendecon.github.io/trendecon/)
]]
.row.bg-main3[.content.font2[
Website [www.trendecon.org](https://www.trendecon.org/) using flexdashboard.
]]
]]
.column.bg-main1[.content.center.vmiddle[
Indicators.
<br><br>Data available online.
<br><br>R package.<br><br>
<img src="slide_img/web.png" width=100%>
]]
---
class: middle bg-main1
<img src="slide_img/thank_you.gif" width=50%>
# [www.trendecon.org](https://www.trendecon.org/)
</textarea>
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