Examining the Long Run Effects of Export, Import and FDI Inflows on the FDI Outflows from India: A Causality Analysis

2009 ◽  
Vol 10 (1) ◽  
pp. 65-88
Author(s):  
Nandita Dasgupta
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Driving Force ◽  
Time Series Data ◽  
Series Data ◽  
Causality Analysis ◽  
Fdi Inflows ◽  
Long Run ◽  
Time Series Data Analysis ◽  
Trade And Investment

The objective of this paper is to examine the effects of international trade and investment related macro economic variables, namely, exports, imports and FDI inflows on the outflows of FDI from India over 1970 through 2005. Using time series data analysis, the empirical part of the paper finds unidirectional Granger Causality from export and import to FDI outflows but no such causality exists from FDI inflows to the corresponding outflows from India. Results confirm the assumption that lagged imports and exports are a driving force of ing front.

scholarly journals Periodic Time Series Data Analysis by Deep Learning Methodology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223078-223088
Author(s):  
Haolong Zhang ◽  
Haoye Lu ◽  
Amiya Nayak
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Periodic Time Series ◽  
Time Series Data Analysis

A Novel Method to Improve the Accuracy of Grey Prediction Model in Economic Prediction

2013 ◽  
Vol 347-350 ◽  
pp. 3331-3335
Author(s):  
Qian Ru Wang ◽  
Xi Wei Chen ◽  
Da Shi Luo ◽  
Yu Feng Wei ◽  
Li Ya Jin ◽  
...  
Keyword(s):  
System Theory ◽  
Time Series Data ◽  
Grey System Theory ◽  
Series Data ◽  
Model Parameters ◽  
Economic Data ◽  
Grey System ◽  
Forecasting Accuracy ◽  
Highly Nonlinear ◽  
The Impact

Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular and non-stationary. Many models based on grey system theory could adapt to various economic time series data. However, some of these models didnt consider the impact of the model parameters, or only considered a simple change of the model parameters for the prediction. In this paper, we proposed the PSO based GM (1, 1) model using the optimized parameters in order to improve the forecasting accuracy. The experiment shows that PSO based GM (1, 1) gets much better forecasting accuracy compared with other widely used grey models on the actual chaotic economic data.

Robust Extrema Features for Time-Series Data Analysis

2013 ◽  
Vol 35 (6) ◽  
pp. 1464-1479 ◽  
Author(s):  
Pramod K. Vemulapalli ◽  
Vishal Monga ◽  
Sean N. Brennan
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Data Analysis

scholarly journals Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

2018 ◽  
Vol 145 ◽  
pp. 97-104 ◽  
Author(s):  
Ronakben Bhavsar ◽  
Na Helian ◽  
Yi Sun ◽  
Neil Davey ◽  
Tony Steffert ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Sample Entropy ◽  
Series Data ◽  
Time Series Data Analysis
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