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‎.gitignore

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.Rproj.user
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.Rhistory
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.RData
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.Ruserdata

‎License.png

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‎README.md

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# Tutorial-synthetic-control-methods-Felipe-Thesis-Chapter5
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This tutorial presents the analysis of the 5th chapter of my thesis
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RStudio: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/quarcs-lab/tutorial-long-run-filters/HEAD?urlpath=rstudio) [![DOI](https://zenodo.org/badge/239955444.svg)](https://zenodo.org/badge/latestdoi/239955444)
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# Long Run vs Short Run Decompositions in R: The HP filter vs the Hamilton filter
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- https://rpubs.com/quarcs-lab/long-run-filters
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## Online Environments
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- R Studio cloud project: https://rstudio.cloud/project/25043
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- This notebook can also be executed online at [GESIS Notebooks](https://notebooks.gesis.org). Just copy the URL of this repository and paste it on the [BINDER form](https://notebooks.gesis.org/binder/) To open a virtual R Studio session, make sure you change you click on `File` and change it to `URL`. Then, write `rstudio` in the field `URL to open (optional)`. Finally, click on `launch`.
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[![Creative Commons Lizenzvertrag](https://i.creativecommons.org/l/by-sa/4.0/88x31.png)](http://creativecommons.org/licenses/by-sa/4.0/)
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---
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Suggested Citation:
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> Mendez C. (2020). Long Run vs Short Run Decompositions in R: The HP
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> filter vs the Hamilton filter. R Studio/RPubs. Available at
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> <https://rpubs.com/quarcs-lab/long-run-filters> . DOI: https://zenodo.org/badge/latestdoi/239955444
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---
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# Tutorial
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# Set parameters of the program
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- Name of the series
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<!-- end list -->
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``` r
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seriesName <- "RGDPNAIDA666NRUG"
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```
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Code examples for other series
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- Total GDP of Japan: “JPNRGDPEXP”
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- GDP per capita of Japan: “RGDPCHJPA625NUPN”
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- GPD per capita of Bolivia: “NYGDPPCAPKDBOL”
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- Total GDP of Bolivia: “RGDPNABOA666NRUG”
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- Total GDP of Indonesia: “RGDPNAIDA666NRUG”
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# Load libraries
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``` r
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library(mFilter)
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library(quantmod)
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library(dplyr)
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library(ggplot2)
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library(dygraphs)
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library(xts)
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library(neverhpfilter)
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# Change the presentation of decimal numbers to 3 and avoid scientific notation
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options(digits=3, scipen=999)
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```
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# Import data
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``` r
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seriesName <- getSymbols(seriesName, src="FRED", auto.assign = FALSE)
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```
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``` r
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periodicity(seriesName)
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```
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## Yearly periodicity from 1960-01-01 to 2019-01-01
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# Transform the data
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- Take the log of the series
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<!-- end list -->
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``` r
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seriesName <- log(seriesName)
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```
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# Plot evolution of the variable
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``` r
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dygraph(seriesName) %>%
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dyRangeSelector()
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```
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![](tutorial-long-run-filters_files/figure-gfm/unnamed-chunk-6-1.png)<!-- -->
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# Apply the HP filter
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``` r
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seriesName_filtered_HP <- hpfilter(seriesName,
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freq = 6.25
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)
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```
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## Plot the HP filter
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### Long-run trend
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Create matrix of actual, trend , and cycle values
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``` r
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actual <- seriesName_filtered_HP[["x"]]
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trendHP <- seriesName_filtered_HP[["trend"]]
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cycleHP <- actual - trendHP
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colnames(actual) <- c("actual")
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colnames(trendHP) <- c("trendHP")
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colnames(cycleHP) <- c("cycleHP")
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actual_and_trend <- cbind(actual, trendHP)
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```
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``` r
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dygraph(actual_and_trend[,1:2]) %>%
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dyRangeSelector()
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```
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![](tutorial-long-run-filters_files/figure-gfm/unnamed-chunk-9-1.png)<!-- -->
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### Short-run fluctuations
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``` r
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dygraph(cycleHP) %>%
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dyRangeSelector()
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```
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![](tutorial-long-run-filters_files/figure-gfm/unnamed-chunk-10-1.png)<!-- -->
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# Apply the Hamilton filter
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``` r
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seriesName_filtered_Hamilton <- yth_filter(seriesName,
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h = 2,
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p = 4,
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output = c("x", "trend", "cycle"))
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```
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## Plot the Hamiltion filter
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### Long-run trend
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Rename columns
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``` r
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colnames(seriesName_filtered_Hamilton) <- c("actual",
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"trendHamilton",
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"cycleHamilton")
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```
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``` r
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dygraph(seriesName_filtered_Hamilton[,1:2]) %>%
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dyRangeSelector()
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```
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![](tutorial-long-run-filters_files/figure-gfm/unnamed-chunk-13-1.png)<!-- -->
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### Short-run fluctuation
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``` r
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dygraph(seriesName_filtered_Hamilton[,3]) %>%
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dyRangeSelector()
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```
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![](tutorial-long-run-filters_files/figure-gfm/unnamed-chunk-14-1.png)<!-- -->
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# Run it in the cloud
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Tip: Copy and paste this link another tab of your browser.
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<https://rstudio.cloud/project/25043>
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Or simply
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<a href="https://rstudio.cloud/project/25043" target="_blank">ClickHERE</a>

‎biblio.bib

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@Article{angristEvans1998,
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author={Angrist, Joshua D and Evans, William N},
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title={Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size},
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journal={American Economic Review},
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year={1998},
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volume={88},
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number={3},
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pages={450--477},
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url={http://www.jstor.org/stable/116844}
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},
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@incollection{card1995,
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author = {Card, David},
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title = {Using Geographic Variation in College Proximity to Estimate the Return to Schooling},
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booktitle = {Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp},
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publisher = {University of Toronto Press},
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pages = {201--222},
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address = {Toronto},
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year = {1995},
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editor = {Louis N. Christofides and E. Kenneth Grant and Robert Swidinsky}
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},
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@article{meyer_al1995,
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author={Meyer, Bruce D. and Viscusi, W. Kip and Durbin, David L.},
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title={Workers' Compensation and Injury Duration: Evidence from a Natural Experiment},
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journal={American Economic Review},
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year=1995,
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volume={85},
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number={3},
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pages={322--340}
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},
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@book{r4ds,
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author = {Hadley Wickham and Garrett Grolemund},
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title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
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publisher = {O'Reilly Media},
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year = {2017},
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ISBN = {1491910399},
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URL = {http://r4ds.had.co.nz}
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},
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@book{wooldridge,
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author = {Jeffrey M. Wooldridge},
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title = {Introductory Econometrics: A Modern Approach},
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publisher = {Cengage Learning},
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edition = {6},
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year = {2015},
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isbn = {130527010X}
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},
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@article{solon_al2015,
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title={What are We Weighting For?},
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author={Solon, Gary and Haider, Steven J. and Wooldridge, Jeffrey M.},
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journal={Journal of Human Resources},
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volume={50},
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number={2},
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pages={301--316},
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year={2015},
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doi={10.3368/jhr.50.2.301}
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},
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@incollection{stockYogo2005,
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author = {Stock, James H. and Motohiro Yogo},
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title = {Testing for Weak Instruments in Linear {IV} Regression},
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booktitle = {Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg},
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publisher = {Cambridge University Press},
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pages = {80--108},
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address = {Cambridge},
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year = {2005},
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editor = {Andrews, D.W.K. and Stock, J.H.}
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},
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@article{winshipRadbill1994,
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author = {Christopher Winship and Larry Radbill},
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title ={Sampling Weights and Regression Analysis},
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journal = {Sociological Methods \& Research},
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volume = {23},
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number = {2},
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pages = {230-257},
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year = {1994},
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doi = {10.1177/0049124194023002004}
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}
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‎binder/.ipynb_checkpoints/environment-checkpoint.yml

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channels:
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- conda-forge
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- defaults
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dependencies:
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- python =3.7
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- bokeh
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- pip
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- geopandas
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- seaborn
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- descartes
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- folium
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- ipython
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- ipywidgets
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- jupyterlab
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- pysal
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- splot
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- mapclassify
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- nbconvert
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- networkx
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- palettable
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- scikit-learn
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- seaborn
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- statsmodels
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- hvplot
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- holoviews
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- geoviews
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- watermark
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- rasterio
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- cenpy
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- contextily
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- vega_datasets
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- pip:
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- segregation==1.4

‎binder/apt.txt

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#libgdal-dev
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#libproj-dev
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#libgeos-dev
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#libudunits2-dev

‎binder/install.R

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#install.packages("AER", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("basetheme", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("barsurf", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("bivariate", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("BMS", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ConvergenceClubs", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("dineq", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("dygraphs", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("educineq", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ExPanDaR", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggmap", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggridges", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggpointdensity", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggspatial", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggthemes", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("GWmodel", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("hdrcde", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ineq", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ineqJD", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("intoo", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ivpack", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("isoband", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("KernSmooth", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("lorenz", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("margins", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("McSpatial", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("mFilter", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("neverhpfilter", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("np", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("patchwork", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("pder", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("pdfCluster", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("pglm", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("plotly", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("quantmod", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("RColorBrewer", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("remotes", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("rgeos", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("sf", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("skimr", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spanel", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spatialprobit", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spatialreg", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spdep", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spgwr", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("sphet", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("splm", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("spsur", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("strucchange", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("tidyverse", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("ggplot2", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("dplyr", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("tmap", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("viridis", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("wbstats", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("xts", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("tidyverse", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("readr", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("Synth", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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install.packages("readxl", repos = "https://cloud.r-project.org/", dependencies=TRUE)
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#install.packages("ggstatsplot", repos = "https://cloud.r-project.org/", dependencies=TRUE)

‎binder/runtime.txt

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r-2020-12-25

‎data/PANEL_CARACTERISTICAS_GENERALES(2019).xlsx

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