scholarly journals Trade and Income—Exploiting Time Series in Geography

2019 ◽  
Vol 11 (4) ◽  
pp. 1-35 ◽  
Author(s):  
James Feyrer
Keyword(s):  
Time Series ◽  
Fixed Effects ◽  
Time Varying ◽  
Omitted Variables ◽  
Trade Growth ◽  
Cross Country ◽  
Time Invariant ◽  
Variable Problem ◽  
Positive Effect ◽  
Over Time

Establishing a robust causal relationship between trade and income has been difficult. Frankel and Romer (1999) uses a geographic instrument to identify a positive effect of trade on income. Rodriguez and Rodrik (2001) shows that these results are not robust to controlling for omitted variables such as distance to the equator or institutions. This paper solves the omitted variable problem by generating a time-varying geographic instrument. Improvements in aircraft technology have caused the quantity of world trade carried by air to increase over time. Country pairs with relatively short air routes compared to sea routes benefit more from this change in technology. This heterogeneity can be used to generate a geography-based instrument for trade that varies over time. The time-series variation allows for controls for country fixed effects, eliminating the bias from time-invariant variables such as distance from the equator or historically determined institutions. Trade has a significant effect on income with an elasticity of roughly one-half. Differences in predicted trade growth can explain roughly 17 percent of the variation in cross-country income growth between 1960 and 1995. (JEL F14, F43, L93)

scholarly journals Not so Harmless After All: The Fixed-Effects Model

2018 ◽  
Vol 27 (1) ◽  
pp. 21-45 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Model Selection ◽  
Fixed Effects ◽  
Time Varying ◽  
Multiple Model ◽  
Fixed Effects Model ◽  
Omitted Variables ◽  
Hausman Test ◽  
Fixed Effects Estimator ◽  
Time Invariant

The fixed-effects estimator is biased in the presence of dynamic misspecification and omitted within variation correlated with one of the regressors. We argue and demonstrate that fixed-effects estimates can amplify the bias from dynamic misspecification and that with omitted time-invariant variables and dynamic misspecifications, the fixed-effects estimator can be more biased than the ‘naïve’ OLS model. We also demonstrate that the Hausman test does not reliably identify the least biased estimator when time-invariant and time-varying omitted variables or dynamic misspecifications exist. Accordingly, empirical researchers are ill-advised to rely on the Hausman test for model selection or use the fixed-effects model as default unless they can convincingly justify the assumption of correctly specified dynamics. Our findings caution applied researchers to not overlook the potential drawbacks of relying on the fixed-effects estimator as a default. The results presented here also call upon methodologists to study the properties of estimators in the presence of multiple model misspecifications. Our results suggest that scholars ought to devote much more attention to modeling dynamics appropriately instead of relying on a default solution before they control for potentially omitted variables with constant effects using a fixed-effects specification.

scholarly journals Homicides during the Barranquilla Carnival, Colombia: A 10 Year Time-Series Analysis

2019 ◽  
Vol 17 (1) ◽  
pp. 35
Author(s):  
Jhon Albert Guarin-Ardila ◽  
Rossycela Montero-Ariza ◽  
Claudia Iveth Astudillo-García ◽  
Julián Alfredo Fernández-Niño
Keyword(s):  
Time Series ◽  
Young Adults ◽  
Time Series Analysis ◽  
Fixed Effects ◽  
Weather Variables ◽  
Men And Women ◽  
The Third ◽  
Fixed Effects Models ◽  
The City ◽  
Positive Effect

Homicides are currently the third leading cause of death among young adults, and an increase has been reported during holidays. The aim of the present study was to explore whether an association exists between Carnival in Barranquilla, Colombia, and an increase in homicides in the city. We used mortality records to identify the number of daily homicides of men and women throughout the week of Carnival, and we compared those with records from all of standard days between 1 January 2005 and 31 December 2015. Conditional fixed-effects models were used, stratified by time and adjusted by weather variables. The average number of homicides on Carnival days was found to be higher than on a standard day, with an OR of 2.34 (CI 95%: 1.19–4.58) for the occurrence of at least one male homicide per day during Carnival, and 1.22 (CI 95%: 1.22–7.36) for female homicides, adjusted by weather variables. The occurrence of homicides during Carnival was observed and was similar to findings for other holidays. Given that violence is a multifactorial phenomenon, the identification of the factors involved serves as a basis for evaluating whether current strategies have a positive effect on controlling it.

scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

Generative Audio Systems Using Power-Preserving All-Pass Filters

2016 ◽  
Vol 40 (1) ◽  
pp. 54-69
Author(s):  
Greg Surges ◽  
Tamara Smyth ◽  
Miller Puckette
Keyword(s):  
Strong Coupling ◽  
Phase Distortion ◽  
Time Varying ◽  
Matrix Formulation ◽  
Pass Filter ◽  
Feedback Network ◽  
High Level ◽  
Time Invariant ◽  
Audio System ◽  
Over Time

This article describes the use of second-order all-pass filters as components in a feedback network, with parameters made time varying to enable effects such as phase distortion in a generative audio system. The term “audio” is used here to distinguish from generative “music” systems, emphasizing the strong coupling between processes governing the production of high-level music and lower-level audio. The classical time-invariant implementation of an all-pass filter is subject to issues of instability that can arise when time-invariant filter parameters are allowed to vary over time. These instabilities are examined, along with the adoption of a power-preserving rotation matrix formulation of the all-pass filter to ensure stability and ultimately an improved synthesis for a generative audio system.

scholarly journals On the Fixed-Effects Vector Decomposition

2011 ◽  
Vol 19 (2) ◽  
pp. 123-134 ◽  
Author(s):  
Trevor Breusch ◽  
Michael B. Ward ◽  
Hoa Thi Minh Nguyen ◽  
Tom Kompas
Keyword(s):  
Monte Carlo ◽  
Instrumental Variables ◽  
Fixed Effects ◽  
Formal Analysis ◽  
Standard Errors ◽  
Panel Data Models ◽  
Time Varying ◽  
Vector Decomposition ◽  
Time Invariant ◽  
Instrumental Variables Approach

This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with group effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis that finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence do not account for presence of a group effect. (5) The decomposition estimator has higher risk than existing shrinkage approaches, unless the endogeneity problem is known to be small or no relevant instruments exist.

scholarly journals Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data

2014 ◽  
Vol 3 (1) ◽  
pp. 133-153 ◽  
Author(s):  
Andrew Bell ◽  
Kelvyn Jones
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Coefficients ◽  
Omitted Variables ◽  
Cross Sectional ◽  
Variance Functions ◽  
Variable Bias ◽  
Random Effects Modeling

This article challenges Fixed Effects (FE) modeling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling—correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plümper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.

scholarly journals Estimation of Market Risk Premium for Japan

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Seoungpil AHN ◽  
Keshab SHRESTHA
Keyword(s):  
Time Series ◽  
Risk Aversion ◽  
Kalman Filtering ◽  
Risk Premium ◽  
Time Varying ◽  
Random Walk Process ◽  
Market Risk Premium ◽  
Time Varying Parameter ◽  
Variance Risk ◽  
Over Time

In this paper, the time series of risk aversion parameter is estimated for the Japanese stock market using weekly return data covering 2/7/1973 to 12/27/2000. The time series of risk aversion parameter is estimated with the Time Varying Parameter (EVP) GARCH-M model proposed by Chou, Engle and Kane (1992), which allows for the risk aversion parameter to change over time by modeling the risk aversion parameter to follow a random walk process. The risk aversion parameter is found to range between 3.5 to 2.2. We also find that the risk aversion parameter has not significantly changed over time. This implies that most of the variation in excess return can be explained by the variation in the market (variance) risk. Keywords: GARCH-M, Kalman Filtering, risk aversion, time-varying parameter, volatility.

scholarly journals Does Contextual Change Affect Basic Human Values? A Dynamic Comparative Multilevel Analysis Across 32 European Countries

2017 ◽  
Vol 48 (4) ◽  
pp. 490-510 ◽  
Author(s):  
Raül Tormos ◽  
Christin-Melanie Vauclair ◽  
Henrik Dobewall
Keyword(s):  
Income Inequality ◽  
Contextual Effects ◽  
Time Varying ◽  
Unemployment Rates ◽  
Openness To Change ◽  
National Wealth ◽  
Self Transcendence ◽  
Time Invariant ◽  
Contextual Change ◽  
Over Time

This article examines the relationship of stable contextual differences and contextual change with the endorsement of Schwartz’s (1992) two basic value dimensions—Openness-to-Change versus Conservation and Self-Enhancement versus Self-Transcendence. Using six waves of the European Social Survey, an extension of multilevel analysis is used which combines both a cross-national comparative and a dynamic analysis of values. The hierarchical data structure and the covariates for value endorsement are defined at three distinct levels: a first level for individuals (with sociodemographic variables, such as age and gender), a second level for country-waves (with time-varying covariates), and a third level for country (with time-invariant covariates). The main aim is to determine if changes in contextual covariates over time are related to value differences between countries over and above contextual time-invariant covariates. High national wealth and low income inequality predicted high Self-Transcendence values and low Conservation values. Low national unemployment rates were associated with less conservatism. When entered simultaneously into the model, only time-invariant differences in gross domestic product (GDP) remained to be a significant predictor of Schwartz’s two basic value dimensions. Finally, we found that an increase in income inequality over time has a certain incremental effect on the endorsement of Conservation over Openness-to-Change values. There were no associations for changes in national wealth and unemployment rates, suggesting that for value endorsement, time-varying contextual effects are less important overall than time-invariant contextual effects.

scholarly journals A cross-country database of COVID-19 testing

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Joe Hasell ◽  
Edouard Mathieu ◽  
Diana Beltekian ◽  
Bobbie Macdonald ◽  
Charlie Giattino ◽  
...  
Keyword(s):  
Time Series ◽  
Data Quality ◽  
Epidemiological Studies ◽  
Daily Number ◽  
Crucial Information ◽  
Cross Country ◽  
Official Data ◽  
Virus Testing ◽  
Over Time

Abstract Our understanding of the evolution of the COVID-19 pandemic is built upon data concerning confirmed cases and deaths. This data, however, can only be meaningfully interpreted alongside an accurate understanding of the extent of virus testing in different countries. This new database brings together official data on the extent of PCR testing over time for 94 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The database is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries’ responses to COVID-19.

Fixed-Effects Vector Decomposition: Properties, Reliability, and Instruments

2011 ◽  
Vol 19 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Shrinkage Estimator ◽  
Time Varying ◽  
Finite Sample ◽  
Simultaneous Presence ◽  
Political Analysis ◽  
Vector Decomposition ◽  
Finite Sample Properties ◽  
Time Invariant ◽  
Decomposition Properties

This article reinforces our 2007 Political Analysis publication in demonstrating that the fixed-effects vector decomposition (FEVD) procedure outperforms any other estimator in estimating models that suffer from the simultaneous presence of time-varying variables correlated with unobserved unit effects and time-invariant variables. We compare the finite-sample properties of FEVD not only to the Hausman-Taylor estimator but also to the pretest estimator and the shrinkage estimator suggested by Breusch, Ward, Nguyen and Kompas (BWNK), and Greene in this symposium. Moreover, we correct the discussion of Greene and BWNK of FEVD's asymptotic and finite-sample properties.

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