scholarly journals Comparison Of Cointegration Tests For Near Integrated Time Series Data With Structural Break

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Esin FİRUZAN ◽  
Berhan ÇOBAN
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Structural Break ◽  
Series Data ◽  
Cointegration Tests ◽  
Integrated Time Series

Modeling Sales-Advertising Relationships: An Integrated Time Series–Econometric Approach

1983 ◽  
Vol 20 (3) ◽  
pp. 291-295 ◽  
Author(s):  
Robert P. Leone
Keyword(s):  
Time Series ◽  
Model Building ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Response Models ◽  
Dynamic Relationship ◽  
Sales Response ◽  
Integrated Time Series ◽  
Econometric Approach

Since Palda's pioneering work investigating the dynamic relationship between sales and advertising, the marketing literature has contained many articles on the topic of sales response model building. Until recently, most of these articles have reported the construction of econometric models based on time series data. Recent applications of multivariate time series extensions of the work by Box and Jenkins have shown the usefulness of this methodology in building sales response models. The author discusses the distinctions between the econometric and time series approaches and, through a multivariate time series analysis, explores the competitive environment of an industry in which advertising is the main source of competition.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

scholarly journals HOW TO ESTIMATE AUTOREGRESSIVE ROOTS NEAR UNITY

2001 ◽  
Vol 17 (1) ◽  
pp. 29-69 ◽  
Author(s):  
Peter C.B. Phillips ◽  
Hyungsik Roger Moon ◽  
Zhijie Xiao
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Time Series Data ◽  
Diffusion Processes ◽  
Rates Of Convergence ◽  
Series Data ◽  
New Class ◽  
Power Rate ◽  
Integrated Time Series ◽  
Conventional Unit

A new model of near integration is formulated in which the local to unity parameter is identifiable and consistently estimable with time series data. The properties of the model are investigated, new functional laws for near integrated time series are obtained that lead to mixed diffusion processes, and consistent estimators of the localizing parameter are constructed. The model provides a more complete interface between I(0) and I(1) models than the traditional local to unity model and leads to autoregressive coefficient estimates with rates of convergence that vary continuously between the O(√n) rate of stationary autoregression, the O(n) rate of unit root regression, and the power rate of explosive autoregression. Models with deterministic trends are also considered, least squares trend regression is shown to be efficient, and consistent estimates of the localizing parameter are obtained for this case also. Conventional unit root tests are shown to be consistent against local alternatives in the new class.

scholarly journals PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

2019 ◽  
Vol 8 (4) ◽  
pp. 518-529
Author(s):  
Setya Adi Rahmawan ◽  
Diah Safitri ◽  
Tatik Widiharih
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
High Order ◽  
Training Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
First Order ◽  
Rice Prices ◽  
Integrated Time Series

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

scholarly journals Comment on "Polynomial cointegration tests of anthropogenic impact on global warming" by Beenstock et al. (2012) – some hazards in econometric modelling of climate change

2013 ◽  
Vol 4 (2) ◽  
pp. 375-384 ◽  
Author(s):  
F. Pretis ◽  
D. F. Hendry
Keyword(s):  
Time Series ◽  
Anthropogenic Impact ◽  
Time Series Data ◽  
Series Data ◽  
Climate Data ◽  
Econometric Modelling ◽  
Empirical Modelling ◽  
Cointegration Tests ◽  
Polynomial Cointegration ◽  
Made In

Abstract. We outline six important hazards that can be encountered in econometric modelling of time-series data, and apply that analysis to demonstrate errors in the empirical modelling of climate data in Beenstock et al. (2012). We show that the claim made in Beenstock et al. (2012) as to the different degrees of integrability of CO2 and temperature is incorrect. In particular, the level of integration is not constant and not intrinsic to the process. Further, we illustrate that the measure of anthropogenic forcing in Beenstock et al. (2012), a constructed "anthropogenic anomaly", is not appropriate regardless of the time-series properties of the data.

scholarly journals Moving forward with time series analysis

2017 ◽  
Vol 4 (4) ◽  
pp. 205316801773223
Author(s):  
Peter K. Enns ◽  
Nathan J. Kelly ◽  
Takaaki Masaki ◽  
Patrick C. Wohlfarth
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Correction ◽  
Time Series Data ◽  
Error Correction Model ◽  
Series Data ◽  
Correction Model ◽  
Standard Procedures ◽  
Cointegration Tests ◽  
General Error

In a recent Research and Politics article, we showed that for many types of time series data, concerns about spurious relationships can be overcome by following standard procedures associated with cointegration tests and the general error correction model (GECM). Matthew Lebo and Patrick Kraft (LK) incorrectly argue that our recommended approach will lead researchers to identify false (i.e., spurious) relationships. In this article, we show how LK’s response is incorrect or misleading in multiple ways. Most importantly, when we correct their simulations, their results reinforce our previous findings, highlighting the utility of the GECM when estimated and interpreted correctly.

scholarly journals Rising Unemployment in Nigeria-Public Debt to the Rescue?

2020 ◽  
Vol 3 (2) ◽  
pp. 280-290
Author(s):  
Jude Chukwunyere Iwuoha
Keyword(s):  
Time Series ◽  
Unemployment Rate ◽  
Public Debt ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Critical Infrastructures ◽  
Cointegration Tests ◽  
Weak Growth ◽  
Stationarity Test

Among the macroeconomic challenges facing Nigeria as a country are weak growth of the economy, ever increasing unemployment rate, and increasing inequality occasioned by increasing poverty. In trying to mitigate these challenges, the Nigeria government usually run aborrowing. In all these, the unemployment rate keep rising year-on-year. In this study, we tried to find out whether borrowing will come to the rescue in reducing unemployment in Nigeria, using time series data from 1981 - 2019. Employing the VECM model, we carried out the stationarity and cointegration tests respectively. While the stationarity test confirmed all variables being stationary at I(1), existence of cointegration was also confirmed indicating a relationship between public debt and unemployment which turned out to be an inverse relationship. A high value of ECM was recorded. It was found that unemployment granger causes government debt and debt servicing. The overall result shows that public debt have rendered little or no assistance in combating unemployment in Nigeria. While we do not discourage government from borrowing for the provision of critical infrastructures, corruption should be put in check so as to allow the amount of borrowing be reflected on the infrastructures available, as public debt also have some adverse effects on the economy.

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