scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

ASYMPTOTIC THEORY OF STATISTICAL INFERENCE FOR TIME SERIES

2002 ◽  
Vol 18 (4) ◽  
pp. 993-999
Author(s):  
Offer Lieberman
Keyword(s):  
Time Series ◽  
Statistical Inference ◽  
Asymptotic Theory ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Time Series Econometrics ◽  
Non Gaussian ◽  
Unit Root Models

Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation and hypothesis testing, and so, in the absence of a usable finite sample theory (as is the case for the models just mentioned), an enormous amount of effort has been given to developing adequate asymptotics for statistical inference. There is often a lag between the introduction of a new model and the development of an asymptotic theory. In consequence, applied econometricians sometimes have to estimate time series models for which no asymptotic theory is available. For instance, multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models have been in use in empirical research for a while, and practitioners have been using asymptotic normality of estimators in this model even though a theoretical justification is not available.

The Impact of Aggregate Level Alcohol Consumption on Homicide Rates: A Time Series Analysis

2021 ◽  
pp. 0306624X2110287
Author(s):  
TK Vinod Kumar
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Moving Average ◽  
Policy Implications ◽  
Autoregressive Moving Average ◽  
Aggregate Level ◽  
Homicide Rates ◽  
Time Series Analyses ◽  
The Impact ◽  
The Relationship

Consumption of alcohol has an impact on violent crimes and homicides. The study examines the association between aggregate level consumption of spirit and homicide rates in the State of Kerala in India. Time-series analyses were conducted by building Autoregressive Moving Average with Exogenous Variables (ARMAX) models and OLS Regression models to explain the relationship between the monthly rate of consumption of alcoholic spirits and homicide rates. The study concludes that consumption of alcoholic spirits has a statistically significant impact on the total homicide rates and the male and female homicide rates. The study has significant policy implications being one of the first studies examining the relationship between alcohol consumption and homicide rates in India and suggesting methods to address challenges of adverse public health consequences associated with alcohol consumption.

scholarly journals Spatial Transmission Network Construction of Influenza-Like Illness Using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

2020 ◽  
Author(s):  
Jianqing Qiu ◽  
Huimin Wang ◽  
Tao Zhang ◽  
Changhong Yang
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Intervention Strategy ◽  
Dynamic Bayesian Network ◽  
Sichuan Province ◽  
Autoregressive Moving Average ◽  
Transmission Network ◽  
Vector Autoregressive ◽  
Vector Autoregressive Moving Average ◽  
Time Lagged

Abstract Background: Influenza is an acute respiratory infection caused by an influenza virus, and the primary intervention strategy is seasonal vaccine. Due to various influenza strains and their rapid mutation each year, how to recognize the key population and timing of the vaccination becomes essential. Considering the importance of finding possible spreading directions and effects of influenza between cities for department of influenza prevention, the construction of influenza transmission network becomes meaningful.Methods: 21 cities in Sichuan province were divided into different learning communities according to whether they were adjacent to each other or not. In each community, the first-order conditional dependencies approximation algorithm was performed to learn the possible structure of the time-lagged correlations between different time series vectors of the ILI estimated weekly number, and the vector autoregressive moving average models were performed for learning the lag orders and parameters of the time-lagged correlations between different time series vectors in each community.Results: It detected a number of significant time-lagged correlations between cities in Sichuan province using two models, and the lag was from 1 week to 3 weeks. The parameters indicating the suspected propagation relationship were between -0.90 and 0.75, and the proportion of the negative values in parameters increased with time. Furthermore, the spreading routes learning from two models were almost in accordance with the traffic network of Sichuan province.Conclusions: This study proposed an innovative framework for exploring the potentially stable transmission routes between different regions and measuring specific size of the transmission effect. It could be used for the infectious disease key area confirmation by considering their adjacent areas’ incidence and the transmission relationship.

scholarly journals PERBANDINGAN ANTARA MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN MODEL FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN DI KABUPATEN MANOKWARI

2019 ◽  
Vol 15 (2) ◽  
pp. 78-87
Author(s):  
Esther Ria Matulessy
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Transfer Function Model ◽  
Function Model ◽  
Autoregressive Integrated Moving Average ◽  
West Papua ◽  
Input Variables

This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).

Multivariate Transfer Function-Noise Model of River Flow for Hydropower Operation

1986 ◽  
Vol 17 (3) ◽  
pp. 185-202 ◽  
Author(s):  
Tryggvi Olason ◽  
W. Edgar Watt
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
River Flow ◽  
Moving Average ◽  
Noise Model ◽  
Lead Times ◽  
Autoregressive Moving Average ◽  
Northern Ontario ◽  
Daily Data ◽  
Univariate Time Series

The formulation of multivariate autoregressive moving average (ARMA) time series models and their transfer function noise (TFN) form is described. Development of a multivariate TFN model is difficult if the multiple inputs are correlated. Various methods for developing a multivariate TFN models with correlated multiple inputs are critically reviewed. A simple approach to developing multiple input TFN models with correlated inputs is described. This approach is successfully applied to developing a forecasting model for average daily flow of the Mattagami River at Little Long Generation Station in Northern Ontario, Canada. System inputs are upstream and tributary flows. Only three years of daily data for the period April 1st to October 31st were required to calibrate the model. Two further years were used to verify the model. Forecasts at lead times of one and two days were good for both calibration and verification periods. The average standard errors were 8% of average inflows (1-day lead) and 18% (2-day lead). The system produces significantly better forecasts than a univariate time series model.

scholarly journals Comparison of GARCH & ARMA Models to Forecasting Exchange Rate

2021 ◽  
Vol 8 (6) ◽  
pp. 979-983
Author(s):  
Meshal Harbi Odah
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Arma Models ◽  
Series Analysis ◽  
Homogeneity Of Variance ◽  
The Garch Model

Financial time series are defined by their fluctuations, which are characterized by instability or uncertainty, implying that there are periods of volatility followed by periods of relative calm. Therefore, time series analysis requires homogeneity of variance. In this paper, some models used in time series analysis have been studied and applied. Comparison between Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models to identify the efficient model through (MAE, MASE) measures to determine the best forecasting model is studied. The findings show that the models of Generalised Autoregressive Conditional Heteroscedastic are more efficient in forecasting time series of financial. In addition, the GARCH model (1,1) is the best to forecasting exchange rate.

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