How Smart Is my Dummy? Time Series Tests for the Influence of Politics

2005 ◽  
Vol 13 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Tony Caporale ◽  
Kevin Grier
Keyword(s):  
Time Series ◽  
Null Hypothesis ◽  
Potential Problem ◽  
Time Series Data ◽  
Political Influence ◽  
Single Equation ◽  
Series Data ◽  
The Political ◽  
Dummy Variables ◽  
Inference Problems

Of necessity, many tests for political influence on policies or outcomes involve the use of dummy variables. However, it is often the case that the hypothesis against which the political dummies are tested is the null hypothesis that the intercept is otherwise constant throughout the sample. This simple null can cause inference problems if there are (nonpolitical) intercept shifts in the data and the political dummies are correlated with these unmodeled shifts. Here we present a method for more rigorously testing the significance of political dummy variables in single equation models estimated with time series data. Our method is based on recent work on detecting multiple regime shifts by Bai and Perron. The article illustrates the potential problem caused by an overly simple null hypothesis, exposits the Bai and Perron model, gives a proposed methodology for testing the significance of political dummy variables, and illustrates the method with two examples. Before the curse of statistics fell upon mankind we lived a happy, innocent life—Hilaire Belloc, On Statistics

scholarly journals The Presidential Political Business Cycle of 1972

1984 ◽  
Vol 44 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Robert R. Keller ◽  
Ann Mari May
Keyword(s):  
Time Series ◽  
Business Cycle ◽  
Time Series Data ◽  
Political Business Cycle ◽  
Series Data ◽  
The Political ◽  
Operating Environment ◽  
Macroeconomic Conditions ◽  
Price Controls ◽  
Time Period

Previous studies of the political business cycle have examined time series data to determine whether a pattern of pre-election boom and post-election slump exists. The studies do not investigate the behavior and mechanisms by which a politician may effectuate a political business cycle. We focus on one time period, 1969 to 1972, and conclude that President Nixon's personality and operating environment explain why he manipulated the economy for political gain. The mechanisms he utilized to improve macroeconomic conditions before the 1972 election include monetary policy, fiscal policy, and wage-price controls.

scholarly journals Correlates of Certification Application Success in British Columbia, Saskatchewan and Manitoba

2005 ◽  
Vol 51 (3) ◽  
pp. 544-562 ◽  
Author(s):  
Felice Martinello
Keyword(s):  
British Columbia ◽  
Time Series ◽  
Time Series Data ◽  
Economic Conditions ◽  
Political Regime ◽  
Series Data ◽  
The Political ◽  
Political Environment ◽  
Labour Relations ◽  
Labour Legislation

Time series data are used to estimate the effects of labour legislation, the political regime, and economie conditions on the proportion of certification applications granted. Applications filed with the British Columbia, Saskatchewan, and Manitoba Labour Relations Boards (1951-92) are considered and analyzed separately. Changes in labour legislation haue the largest impact on certification application success in all three provinces. The political environment is estimated to be important in British Columbia, but not in Saskatchewan or Manitoba. Economic conditions affect certification success in Saskatchewan and to a lesser extent in British Columbia, but not in Manitoba. Large changes in economic conditions are estimated to have only small effects on the proportion of applications granted.

Data Preprocessing

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Signal Processing ◽  
Real World ◽  
Null Hypothesis ◽  
Time Series Data ◽  
Nonlinear Behavior ◽  
Surrogate Data ◽  
Series Data ◽  
Linear Behavior

Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.

scholarly journals Political Parties and Trade Union Growth in Canada

2005 ◽  
Vol 37 (4) ◽  
pp. 876-886 ◽  
Author(s):  
Dennis R. Maki
Keyword(s):  
Time Series ◽  
Political Parties ◽  
Cross Section ◽  
Political Party ◽  
Time Series Data ◽  
Series Data ◽  
Political Factors ◽  
Dummy Variables ◽  
Union Growth ◽  
Canadian Provinces

This study retests the Ashenfelter-Pencavel hypothesis that political factors matter, using pooled cross section-time series data on union growth for Canadian provinces and a set of dummy variables representing political party in power in each province. Both theoretical and practical aspects are presented.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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