Impulse response function analysis of the impacts of land value‐added tax policy on government performance

2020 ◽  
Vol 41 (6) ◽  
pp. 1020-1032
Author(s):  
Day‐Yang Liu ◽  
Hsin‐Hsin Yao ◽  
Wen‐Min Lu ◽  
Cheng‐Hsien Lin
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Tax Policy ◽  
Function Analysis ◽  
Impulse Response Function ◽  
Value Added ◽  
Government Performance ◽  
Land Value ◽  
Value Added Tax

scholarly journals A Study On The Productivity Of Koreas Industrial Sector With IT Technology Capital And R&D Stocks Variation

2011 ◽  
Vol 1 (10) ◽  
Author(s):  
Chuhwan Park ◽  
Tae-Woong Park ◽  
Byoung-Moo Heo
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Impulse Response ◽  
Response Function ◽  
Function Analysis ◽  
Impulse Response Function ◽  
Industrial Sector ◽  
Response Analysis ◽  
Industrial Growth ◽  
Time Periods

This paper examines the effects of IT technology capital and R&D stocks variation on the growth of Koreas industries with time series approaches. In detail, we analyze the Granger causality and impulse response analysis among the Koreas industrial growth, IT technology capital, and R&D stocks. When it comes to this research conclusion, we know that IT technology capital and R&D stocks shocks affect the growth of Koreas industrial sector. However, the revere effect is ambiguous in each industrial sector. Also, the impulse response function analysis shows that the effect of IT technology capital and R&D stocks fluctuation in each industrial sector is presented with different time periods.

scholarly journals Can a simple structural time series model beat the random walk?

2014 ◽  
Vol 2 (1-2) ◽  
Author(s):  
Anton Abdulbasah Kamil ◽  
Zainudin Arsad ◽  
Quah Soon Hoe ◽  
Yip Chee Yin
Keyword(s):  
Random Walk ◽  
Impulse Response ◽  
Response Function ◽  
Structural Model ◽  
Function Analysis ◽  
Impulse Response Function ◽  
Random Walk Model ◽  
Comparison Study ◽  
Out Of Sample ◽  
Arfima Model

This paper tries to address the question that if the long run PPP holds, then there should exist a structural model which can outperform the random walk in out of sample forecasting. We propose an ARFIMA based model with log of the independent variable as an explanatory variable and make a comparison study of this structural model with the benchmark random walk model. Then, we compare our results with that as obtained by Engel and Hamilton, and by Clarida, Sarno, Taylor and Valente. We present the standard ARFIMA model and show how can make an extension of it so that it becomes a variant of ARFIMA and name it as YQ-ARFIMA, then construct a bivariate model relating the dependent variable yt and ln yt , and with that, perform an impulse response function analysis of the predictive ability of ln yt . We also transform the YQ-ARFIMA into a moving average representation, and thereafter perform the impulse response function analysis again, then make a comparison study between the standard ARFIMA and the YQARFIMA by comparing the out of sample forecasting ability of each one of them with the benchmark random walk model. After that, compare the performance of YQ-ARFIMA with that of the Markov switching model put forward by Engel and Hamilton, and the MSIH(3)-VECM as put forward by CSTV. Last, we test the robustness of the YQ-ARFIMA by fitting it into different exchange rate series spanning the five continents of the globe, then, test the consistency of the forecast by YQ-ARFIMA by a cointegration technique. By using the loss functions RMSE and MAPE, cointegration consistency in forecasts and impulse response function analysis, we have shown beyond doubt that theYQ-ARFIMA model is very much superior in forecasting ability.

Strontium-89 radionuclide therapy: a dosimetric study using impulse response function analysis

1987 ◽  
Vol 60 (715) ◽  
pp. 685-692 ◽  
Author(s):  
G. M. Blake ◽  
J. M. Gray ◽  
M. A. Zivanovic ◽  
A. J. McEwan ◽  
J. S. Fleming ◽  
...  
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Radionuclide Therapy ◽  
Function Analysis ◽  
Impulse Response Function ◽  
Dosimetric Study
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