scholarly journals Are Inflation Rates Really Nonstationary? New Evidence from Non-linear STAR Framework and African Data

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Augustine Chuck Arize
Keyword(s):  
Inflation Rates ◽  
Non Linear ◽  
New Evidence

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Inflationary Thresholds, Financial Development and Economic Growth: New Evidence from Two West African Countries

2017 ◽  
Vol 17 (2) ◽  
pp. 20160042 ◽  
Author(s):  
Bernard Njindan Iyke ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Financial Development ◽  
Inflation Rate ◽  
Data Availability ◽  
Development Economic ◽  
African Countries ◽  
Inflation Rates ◽  
New Evidence ◽  
Threshold Range

This paper examines the role of inflationary threshold effects in the finance-growth relationship for Ghana and Nigeria. Ghana and Nigeria are relatively homogenous in terms of financial development, economic growth, and inflationary history and therefore provide an acceptable choice for this empirical analysis. Due to lack of data availability, the sample spans the period 1964–2011 for Ghana and 1961–2011 for Nigeria. Using appropriately specified threshold regressions, we found inflationary thresholds in both countries during the study periods. Specifically, the inflationary threshold range for Ghana is 10.73 %–29.83 %. For Nigeria, the inflationary threshold range is 10.07 %–19.25 %. By estimating the threshold regressions, we found financial development to have positive and significant effect on economic growth during low and moderate inflationary regimes; and insignificant effect on growth during high inflationary regimes, for both countries. In particular, financial development impact greatly on growth in Ghana when the rate of inflation is below a threshold of 10.73 % but dissipates when inflation rate reaches and exceeds 29.83 %. Similarly, financial development impact greatly on growth in Nigeria when the rate of inflation is below a threshold of 10.07 % but dissipates when inflation rate reaches and exceeds 19.25 %. The results imply that policymakers in these countries should take inflation into account when devising policies to promote financial development with the aim of generating economic growth. For without low or moderate inflation rates, such policies will not achieve their intended purposes.

scholarly journals Health Impacts of Exposure to Gaseous Pollutants and Particulate Matter in Beijing—A Non-Linear Analysis Based on the New Evidence

2018 ◽  
Vol 15 (9) ◽  
pp. 1969 ◽  
Author(s):  
Yunfei Cheng ◽  
Tatiana Ermolieva ◽  
Gui-Ying Cao ◽  
Xiaoying Zheng
Keyword(s):  
Air Pollutants ◽  
Respiratory Diseases ◽  
Meteorological Data ◽  
Positive Association ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Control Plan ◽  
Short Term Exposure ◽  
Non Linear ◽  
New Evidence

This paper aimed to estimate health risks focusing on respiratory diseases from exposure to gaseous multi-pollutants based on new data and revealed new evidence after the most stringent air pollution control plan in Beijing which was carried out in 2013. It used daily respiratory diseases outpatient data from a hospital located in Beijing with daily meteorological data and monitor data of air pollutants from local authorities. All data were collected from 2014 to 2016. Distributed lag non-linear model was employed. Results indicated that NO2 and CO had positive association with outpatients number on the day of the exposure (1.045 (95% confidence interval (CI): 1.003, 1.089) for CO and 1.022 (95% CI: 1.008, 1.036) for NO2) (and on the day after the exposure (1.026 (95% CI: 1.005, 1.048) for CO and 1.013 (95% CI: 1.005, 1.021) for NO2). Relative risk (RR) generally declines with the number of lags; ozone produces significant effects on the first day (RR = 0.993 (95% CI: 0.989, 0.998)) as well as second day (RR = 0.995 (95% CI: 0.991, 0.999)) after the exposure, while particulate pollutants did not produce significant effects. Effects from the short-term exposure to gaseous pollutants were robust after controlling for particulate matters. Our results contribute to a comprehensive understanding of the dependencies between the change of air pollutants concentration and their health effects in Beijing after the implementation of promising air regulations in 2013. Results of the study can be used to develop relevant measures minimizing the adverse health consequences of air pollutants and supporting sustainable development of Beijing as well as other rapidly growing Asian cities.

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