scholarly journals Keterkaitan Instrumen Kebijakan Moneter dengan Tingkat Pengangguran

2018 ◽  
Vol 7 (4) ◽  
pp. 421-431
Author(s):  
Fitrawaty Fitrawaty
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Time Series Data ◽  
Policy Instruments ◽  
Series Data ◽  
Open Market ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression ◽  
Structural Vector

Penelitian ini bertujuan untuk menganalisis bagaimana interdependensi instrument kebijakan moneter dengan pengangguran di Indonesia selama periode tahun 2000 – 2011. Data yang digunakan adalah data time series yang diperoleh dari Bank Indonesia, Biro Pusat Statistik dan institusi lainnya. Penelitian ini menggunakan metode Vector Auturegression (VAR) dilanjutkan dengan Struktural Vector Auturegression (SVAR). Berdasarkan hasil interpretasi VAR dan SVAR, secara khusus diperoleh bahwa keterkaitan  antara instrumen moneter dengan pengangguran (UNEMP) memiliki arah yang berbeda. Variabel operasi pasar terbuka (OPT),  tingkat suku bunga diskonto (rDiskonto), dan tingkat bunga domestik (rDom), mempunyai arah yang negatif terhadap variabel pengangguran, sedangkan variabel giro wajib minimum (GWM), nilai tukar (EXC) mempunyai arah yang positif. Keseluruahan instrumen moneter secara parsial berpengaruh tidak signifikan terhadap UNEMP. Begitu juga setelah dilakukan shock dengan menaikkan OPT sebesar 5% pada tahun 2010, diperoleh bahwa variabel OPT, GWM, rDiskonto, rDOM, EXC, juga tidak berpengaruh signifikan terhadap pengangguran. This study is intended to analyze the correlation of monetary policy instruments with unemployment in Indonesia during the period 2000 - 2011. The data used are time series data obtained from Bank of Indonesia, the Central Statistics Bureau and other institutions. This study uses Vector Auto regression (VAR) method followed by Structural Vector Auto regression (SVAR). Based on the results of the interpretation of VAR and SVAR, it was found that the relationship between monetary instruments with unemployment (UNEMP) had different directions. Open market operations variable (OMO), discounted interest rates variable(discount), and domestic interest rates variable (FDOM), have a negative correlation to the unemployment variable, while the statutory reserve requirement (GWM), exchange rate (EXC) variables have a positive correlation. The partiality of monetary instruments has no significant effect on UNEMP. Likewise, after the shock of increasing OPT by 5% in 2010, it was found that the OMO variable, GWM, discount, FDOM, and EXC, also had no significant effects on unemployment.

scholarly journals Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Sri Hartini ◽  
Muhammad Pramono Hadi ◽  
Sudibyakto Sudibyakto ◽  
Aris Poniman
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Time Interval ◽  
Discharge Data ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

scholarly journals Relationship between lending interest rate, inflation rate and capital formation in Kenya

2021 ◽  
Vol 1 (5) ◽  
pp. 339-347
Author(s):  
Titus Mosoti Ogero
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Interest Rates ◽  
Inflation Rate ◽  
Time Series Data ◽  
Capital Formation ◽  
Series Data ◽  
Expectation Theory ◽  
Level Of Significance ◽  
The Relationship

The study seeks to understand the relationship between lending interest rate, inflation rate and capital formation in Kenya. Time series from World Bank for the 1988 to 2018 is employed. Development of literature is guided by expectation theory, classical theory of interest rate and the institutionalist theory of capital formation. The study finds capital formation, lending interest rate ad inflation rate time series data to be stationary at the 5% level of significance. This leads to the checking of the lag order used and estimating of VAR model. The results indicate that, current year’s; capital formation, inflation rate and lending interest rate are insignificant in determining next year’s level of capital formation. First lag of inflation rate is found positively significant in influencing lending interest rates as well as the first lag of lending interest rate is found significant on influencing itself. Capital formation first lag is found to be negatively significant in determining inflation rate. Lastly, inflation rate first lag is found to be positive

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

scholarly journals Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation

2020 ◽  
Vol 17 (11) ◽  
pp. 3891
Author(s):  
Lihua Liu ◽  
Jing Huang ◽  
Huimin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Coupling Efficiency ◽  
Aggregation Operator ◽  
Visibility Graph ◽  
Series Data ◽  
Efficiency Evaluation ◽  
Geometric Operator ◽  
The Relationship

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

Correlation analysis of continuous emotional response to music: Correcting for the effects of serial correlation

2001 ◽  
Vol 5 (1_suppl) ◽  
pp. 213-236 ◽  
Author(s):  
Emery Schubert
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Emotional Response ◽  
Serial Correlation ◽  
Time Series Data ◽  
Second Order ◽  
Series Data ◽  
First Order ◽  
The Relationship ◽  
Pearson Correlations

Publications of research concerning continuous emotional responses to music are increasing. The developing interest brings with it a need to understand the problems associated with the analysis of time series data. This article investigates growing concern in the use of conventional Pearson correlations for comparing time series data. Using continuous data collected in response to selected pieces of music, with two emotional dimensions for each piece, two falsification studies were conducted. The first study consisted of a factor analysis of the individual responses using the original data set and its first-order differenced transformation. The differenced data aligned according to theoretical constraints better than the untransformed data, supporting the use of first-order difference transformations. Using a similar method, the second study specifically investigated the relationship between Pearson correlations, difference transformations and the critical correlation coefficient above which the conventional correlation analysis remains internally valid. A falsification table was formulated and quantified through a hypothesis index function. The study revealed that correlations of undifferenced data must be greater than 0.75 for a valid interpretation of the relationship between bivariate emotional response time series data. First and second-order transformations were also investigated and found to be valid for correlation coefficients as low as 0.24. Of the three version of the data (untransformed, first-order differenced, and second-order differenced), first-order differenced data produced the fewest problems with serial correlation, whilst remaining a simple and meaningful transformation.

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