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.

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 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.

Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan

2014 ◽  
Vol 692 ◽  
pp. 97-102 ◽  
Author(s):  
Ijaz Ahmad ◽  
De Shan Tang ◽  
Mei Wang ◽  
Sarfraz Hashim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Positive Trend ◽  
Precipitation Time Series ◽  
Significance Level ◽  
Annual Basis ◽  
Time Period ◽  
Seasonal Basis ◽  
Correlation Trend

This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh station exhibits the most number of significant cases on the seasonal basis while, no significant trends are found at Thalozom, Kalam and Dir stations. On the annual basis, only Charbagh station shows a significant positive trend, while on other stations, no significant trends are found annually. The performance of MK and SR tests was consistent in detecting the trend at different stations.

A Temporal Pattern Discovery Method and its Application to Physiological Data: A Case Study (Preprint)

2019 ◽  
Author(s):  
Catherine Inibhunu ◽  
Carolyn McGregor
Keyword(s):  
Time Series ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Mathematical Formulation ◽  
Series Data ◽  
Physiological Data ◽  
Time Period ◽  
Temporal Pattern Discovery

BACKGROUND High frequency data collected from monitors and sensors that provide measures relating to patients’ vital status in intensive care units (NICUs) has the potential to provide valuable insights which can be crucial when making critical decisions for the care of premature and ill term infants. However, this exercise is not trivial when faced with huge volumes of data that are captured every second at the bedside/home. The ability to collect, analyze and understand any hidden relationships in the data that may be vital for clinical decision making is a central challenge. OBJECTIVE The main goal of this research is to develop a method to detect and represent relationships that may exist in temporal abstractions (TA) and temporal patterns (TP) derived from time oriented data. The premise of this research is that in clinical care, the discovery of unknown relationships among physiological time oriented data can lead to detection of onset of conditions, aid in classifying abnormal or normal behaviors or derive patterns of an altered trajectory towards a problematic future state for a patient. That is, there is great potential to use this approach to uncover previously unknown pathophysiologies that are present in high speed physiological data. METHODS This research introduces a TPR process and an associated TPRMine algorithm which adopts a stepwise approach to temporal pattern discovery by first applying a scaled mathematical formulation of the time series data. This is achieved by modelling the problem space as a finite state machine representation where for a given timeframe, a time series data segment transitions from one state to another based on probabilistic weights and then quantifying the many paths a time series data may transition to. RESULTS The TPRMine Algorithm has been designed, implemented and applied to patient physiological data streams captured from the McMaster Children’s Hospital NICU. The algorithm has been applied to understand the number of states a patient in a NICU bed can transition to in a given time period and a demonstration of formulation of hypothesis tests. In addition, a quantification of these states is completed leading to creation of a vital scoring. With this, it’s possible to understand the percent of time a patient remains in a high or low vital score. CONCLUSIONS The developed method allows understanding the number of states a patient may transition to in any given time period. Adding some clinical context to the identified states facilitates state quantification allowing formulation of thresholds which leads to generating patient scores. This is an approach that can be utilized for identifying patient at risk of some clinical condition prior to disease progress. Additionally the developed method facilitates identification of frequent patterns that could be associated with generated thresholds.

scholarly journals Deterministic dynamics of the magnetosphere: results of the 0–1 test

2013 ◽  
Vol 20 (1) ◽  
pp. 11-18 ◽  
Author(s):  
S. Prabin Devi ◽  
S. B. Singh ◽  
A. Surjalal Sharma
Keyword(s):  
Magnetic Field ◽  
Time Series ◽  
Solar Wind ◽  
Time Series Data ◽  
Nonlinear Dynamical System ◽  
Series Data ◽  
Nonlinear Dynamical ◽  
Deterministic Dynamics ◽  
Time Period ◽  
Solar Wind Magnetic Field

Abstract. A test for deterministic dynamics in a time series data, namely the 0–1 test (Gottawald and Melbourne, 2004, 2005), is used to study the magnetospheric dynamics. The data, corresponding to the same time period, of the auroral electrojet index AL and the magnetic field component Bz of the solar wind magnetic field measured at 1 AU are used to compute the parameter K, which is zero for non-chaotic and unity for chaotic systems. For the magnetosphere and also for the turbulent solar wind, K has values corresponding to a nonlinear dynamical system with chaotic behaviour. This result is consistent with the Lyapunov exponents computed from the same time series data.

scholarly journals A convergence based assessment of relative differences in age-stratified susceptibility and infectiousness for SARS-CoV-2 variants of B.1.1.7 lineage

2021 ◽  
Author(s):  
Sarah Dean Rasmussen
Keyword(s):  
Time Series ◽  
Environmental Effects ◽  
Total Population ◽  
Time Series Data ◽  
Relative Increase ◽  
Series Data ◽  
Gene Target ◽  
Modelling Framework ◽  
Time Period ◽  
Relative Differences

We propose (a) a method for aggregating and processing age-stratified subregional time series data for positive tests of infection given partial sampling for variant-of-concern biomarkers, and (b) a simple model-based theoretical framework for interpreting these processed data, to assess whether observed heterogeneity in age-specific relative differences can be explained by environmental effects alone. We then apply this strategy to public-domain subregional time series data with S-gene target failure (SGTF) sampling as a proxy for B.1.1.7 lineage, from weeks 45 to 50 of 2020 from England. For the time period in question, we observe convergence toward a 1.27 (95% CI 1.17-1.38) times higher ratio of SGTF to non-SGTF infection for 0-9-year-olds than for the total population, and a 1.16 (95% CI 1.09-1.23) times higher ratio for 10-19-year-olds. These are roughly comparable to previous findings, but this time we find high significance evidence for adequate compatibility with our proposed modelling framework criteria to conclude that these relative elevations for 0-19-year-olds are very unlikely to be explained by environmental effects alone. We also find possible indications that 0-19-year-olds might experience a higher relative increase in infectiousness than susceptibility for B.1.1.7.

scholarly journals Asymmetric Effects of the Effect of Oil Price on Stock Markets in Four Asian Countries: Markov Switching Analysis

2018 ◽  
pp. 423-428
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Business Cycle ◽  
Time Series Data ◽  
Markov Switching ◽  
Oil Price ◽  
Series Data ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
The Business Cycle

The Markov switching vector autoregressive model is a dynamic stochastic system with stochastic autoregressive parameters. This model able to measure a time varying problem when the variables undergoing regime switching. Structural change or shock is an ordinary fact in time series data. Some shocks have an important role under specific regimes in examining the business cycle contraction. Excluding changes in regime for the measurement of variance decomposition may produce biased results. Moreover, the parameters in the time series model might also have a structural change. Therefore, linear models are no longer suitable to be used in analyzing the financial model; and nonlinear time series models that are Markov switching models are proposed to solve these kinds of problems. A two regimes Markov switching vector autoregressive model is used in this study to analysis the time series data. The regime is dependent heterogeneous with varying the variance to detect every change of the business cycle. The correlations between oil price, Malaysia, Singapore, Thailand and Indonesia stock price are examining using Markov switching model. The result shows that the regimes dependent models suitable to employ in study the asymmetric business cycle; and oil price have a negative relationship with the changes of the four selected Asian stock markets.

scholarly journals Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment

2021 ◽  
Author(s):  
Muthunagai S U ◽  
Anitha R
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Computing Time ◽  
Transportation Cost ◽  
Series Data ◽  
Similar Data ◽  
Space Reduction ◽  
Time Period ◽  
Innovative Environment ◽  
Industrial Iot

Abstract As a result of the development in Industry 4.0, the data generated within the Industries are increasing rapidly every day to attain the innovative environment within the industry through maximal asset utilization. Meanwhile, the redundancy rate in the server is also increasing, which has an impact on the storage as well as in the analysis of data. Most existing de-duplication techniques partition the data with respect to memory. However if the time period is considered for partition, time-series analysis would be achieved during the de-duplication process. To address the above issue, the proposed work presents the Index Based De-duplication technique with Categorized Region Method for computing time-series data. The Merkle Tree with a super feature called reckoning of occurrence is combined in the proposed system to rapidly identify the existence of similar data in the distributed system with an accurate existence count that significantly helps in predicting the future drifts of the industrial environment. Finally, the proposed system also concludes with optimal transportation cost to reach the storage nodes in the cloud using MODI method. The experimental results reveal that the proposed model is efficient since it facilitates less memory and less computation overhead. The proposed technique achieves space reduction by 98%, reduces the computation overhead during analysis by 55%, and increases the efficacy of cloud storage by 60%.

scholarly journals ANALYSIS OF RAINFALL RECORDS IN INDIA: SELF-ORGANIZED CRITICALITY AND SCALING

Fractals ◽  
2006 ◽  
Vol 14 (04) ◽  
pp. 289-293 ◽  
Author(s):  
A. SARKAR ◽  
P. BARAT
Keyword(s):  
Time Series ◽  
Power Law ◽  
Rainfall Intensity ◽  
Time Series Data ◽  
Series Data ◽  
Scaling Analysis ◽  
Rainfall Time Series ◽  
Self Organized ◽  
Time Period ◽  
Self Organized Criticality

The time series data of the monthly rainfall records (for the time period 1871–2002) in All India and different regions of India are analyzed. It is found that the distributions of the rainfall intensity exhibit perfect power law behavior. The scaling analysis revealed two distinct scaling regions in the rainfall time series.

Effect of Climate Change on Crop Productivity and Prices in Benue State, Nigeria

2018 ◽  
pp. 244-268
Author(s):  
Goodness C. Aye ◽  
Ruth F. Haruna
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Crop Productivity ◽  
Least Square ◽  
Data Availability ◽  
Series Data ◽  
Least Square Regression ◽  
Time Period ◽  
Over Time

The chapter is aimed at assessing the effect of climate change on crop productivity and prices in Benue State, Nigeria. Time series data on selected output of crops (maize, rice, sorghum, yam, millet, groundnut, beans, and cassava), area planted, price, and climate variables such as rainfall, temperature, and sea level were used. Due to differing periods in data availability, this study used the time period 1995-2009 for analysis, in order to maintain a common period for all the series. First, the trend of productivity, prices, and climate change was analyzed using visual plots and results indicate some level of variability in these series over time. Second, a three stage least square regression was used to simultaneously analyze the effect of climate change on productivity and prices. Results show that climate change had significant impact on the productivity of millet, sorghum, cassava, and groundnut while it had significant impact only on the price of maize. These findings have important implications for food security situation in Benue State, Nigeria.

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