Causal Inference with Time-Series Cross-Sectional Data: A Reflection

2022 ◽  
Author(s):  
Yiqing Xu
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Cross Sectional

Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data

2021 ◽  
Author(s):  
Kosuke Imai ◽  
In Song Kim ◽  
Erik H. Wang
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Cross Sectional ◽  
Matching Methods

Causal Inference and Mediation: Cross-sectional Biases of Longitudinal Processes

2006 ◽  
Author(s):  
Jonathan N. Frecceri ◽  
Scott E. Maxwell
Keyword(s):  
Causal Inference ◽  
Cross Sectional

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

scholarly journals Spectral Pitch Similarity is a Predictor of Perceived Change in Sound- as Well as Note-Based Music

2019 ◽  
Vol 2 ◽  
pp. 205920431984735
Author(s):  
Roger T. Dean ◽  
Andrew J. Milne ◽  
Freya Bailes
Keyword(s):  
Time Series ◽  
Piano Music ◽  
Individual Response ◽  
Discrete Events ◽  
Acoustic Intensity ◽  
Spectral Parameters ◽  
Cross Sectional ◽  
Perceived Change ◽  
Predictive Role ◽  
The Individual

Spectral pitch similarity (SPS) is a measure of the similarity between spectra of any pair of sounds. It has proved powerful in predicting perceived stability and fit of notes and chords in various tonal and microtonal instrumental contexts, that is, with discrete tones whose spectra are harmonic or close to harmonic. Here we assess the possible contribution of SPS to listeners’ continuous perceptions of change in music with fewer discrete events and with noisy or profoundly inharmonic sounds, such as electroacoustic music. Previous studies have shown that time series of perception of change in a range of music can be reasonably represented by time series models, whose predictors comprise autoregression together with series representing acoustic intensity and, usually, the timbral parameter spectral flatness. Here, we study possible roles for SPS in such models of continuous perceptions of change in a range of both instrumental (note-based) and sound-based music (generally containing more noise and fewer discrete events). In the first analysis, perceived change in three pieces of electroacoustic and one of piano music is modeled, to assess the possible contribution of (de-noised) SPS in cooperation with acoustic intensity and spectral flatness series. In the second analysis, a broad range of nine pieces is studied in relation to the wider range of distinctive spectral predictors useful in previous perceptual work, together with intensity and SPS. The second analysis uses cross-sectional (mixed-effects) time series analysis to take advantage of all the individual response series in the dataset, and to assess the possible generality of a predictive role for SPS. SPS proves to be a useful feature, making a predictive contribution distinct from other spectral parameters. Because SPS is a psychoacoustic “bottom up” feature, it may have wide applicability across both the familiar and the unfamiliar in the music to which we are exposed.

scholarly journals Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

2021 ◽  
Vol 13 (14) ◽  
pp. 2741
Author(s):  
John Gibson ◽  
Geua Boe-Gibson
Keyword(s):  
Time Series ◽  
Economic Activity ◽  
Validation Studies ◽  
National Level ◽  
Predictive Performance ◽  
The United States ◽  
Cross Sectional ◽  
Nation States ◽  
Nighttime Lights ◽  
Defense Meteorological Satellite Program

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

Free Traders and Drug Smugglers: The Effects of Trade Openness on States' Ability to Combat Drug Trafficking

2009 ◽  
Vol 51 (2) ◽  
pp. 117-145 ◽  
Author(s):  
Horace A. Bartilow ◽  
Kihong Eom
Keyword(s):  
Time Series ◽  
Statistical Model ◽  
Drug Trafficking ◽  
Trade Openness ◽  
Cross Sectional ◽  
Theoretical Literature ◽  
Openness To Trade ◽  
Pooled Time Series

AbstractThe theoretical literature presents conflicting expectations about the effects of trade openness on the ability of states to interdict drug trafficking. One view expects that trade openness will undermine drug interdiction; a second argues the opposite; a third argues that trade openness does not necessarily affect drug interdiction. This article assesses empirically the effects of trade openness on drug interdiction for countries in the Americas using a pooled time-series cross-sectional statistical model. It finds that trade openness decreases the interdiction capabilities of states in drug-consuming countries while increasing those of states in drug-producing countries. Greater openness to trade does not have a consistently significant effect on the interdiction capabilities of states in drug transit countries.

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