Fractional Integration Methods and Short Time Series: Evidence from a Simulation Study

2016 ◽  
Vol 24 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Agnar Freyr Helgason
Keyword(s):  
Time Series ◽  
Alternative Model ◽  
Fractional Integration ◽  
Short Time Series ◽  
Integration Methods ◽  
Correction Model ◽  
Long Run ◽  
Short Run ◽  
Short Time ◽  
General Error

Grant and Lebo (2016) and Keele, Linn, and Webb (2016) provide diverging recommendations to analysts working with short time series that are potentially fractionally integrated. While Grant and Lebo are quite positive about the prospects of fractionally differencing such data, Keele, Linn, and Webb argue that estimates of fractional integration will be highly uncertain in short time series. In this study, I simulate fractionally integrated data and compare estimates from the general error correction model (GECM), which disregards fractional integration, to models using fractional integration methods over thirty-two simulation conditions. I find that estimates of short-run effects are similar across the two models, but that models using fractionally differenced data produce superior predictions of long-run effects for all sample sizes when there are no short-run dynamics included. When short-run dynamics are included, the GECM outperforms the alternative model, but only in time series that consist of under 250 observations.

Concluding Comments

2016 ◽  
Vol 24 (1) ◽  
pp. 83-86 ◽  
Author(s):  
Luke Keele ◽  
Suzanna Linn ◽  
Clayton McLaughlin Webb
Keyword(s):  
Error Correction ◽  
Political Science ◽  
Fractional Integration ◽  
Error Correction Model ◽  
Costs And Benefits ◽  
Significant Progress ◽  
Integration Methods ◽  
Correction Model ◽  
General Error

This issue began as an exchange between Grant and Lebo (2016) and ourselves (Keele, Linn, and Webb 2016) about the utility of the general error correction model (GECM) in political science. The exchange evolved into a debate about Grant and Lebo's proposed alternative to the GECM and the utility of fractional integration methods (FIM). Esarey (2016) and Helgason (2016) weigh in on this part of the debate. Freeman (2016) offers his views on the exchange as well. In the end, the issue leaves readers with a lot to consider. In his comment, Freeman (2016) argues that the exchange has produced little significant progress because of the contributors' failures to consider a wide array of topics not directly related to the GECM or FIM. We are less pessimistic. In what follows, we distill what we believe are the most important elements of the exchange–the importance of balance, the costs and benefits of FIM, and the vagaries of pre-testing.

Causality between VAT and economic growth in Nigeria: An ARDL bounds testing approach

2018 ◽  
Vol 6 (1) ◽  
pp. 55
Author(s):  
Hammed Agboola Yusuf ◽  
Irwan Shah Zainal Abidin ◽  
Normiza Bakar ◽  
Oluwaseyi Hammed Musibau
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Error Correction ◽  
Trade Openness ◽  
Value Added ◽  
Error Correction Model ◽  
Domestic Investment ◽  
Correction Model ◽  
Long Run ◽  
Short Run

Value Added Tax(VAT) is a consumption tax imposed at every stage of consumption level whose burden is burned by final consumer of goods and services. In most developing economies in the world, VAT as a source of revenue to the government that has been notable for its significant role in ensuring economic efficiency. However, VAT revenue has been underutilised in Nigeria due to a high level of corruption in the process of administering the tax. This study examines the impact of VAT, domestic investment and trade openness on economic growth in Nigeria from 1980 to 2016 using ARDL techniques. The research design is time series, and the data were analysed using time series unit root test, error correction model regression, short run and long run ARDL. The result found that VAT, domestic investment and trade openness had a positive and significant impact on real GDP. Also, corruption index is negative also significant in the long run. In the same vein, past value added tax had a negative and weak significant impact on real gross domestic product indicating convergence to long-run causality between economic growths and VAT and economic growth. The Error Correction Model (ECM (-1)) coefficient had a negative and statistically significant sign. This shows that 39 percent can quickly correct short-run deviation. The study, therefore,  recommends that tax administrative loopholes should be plugged for tax revenue to contribute immensely to the development of the economy since past VAT had a significant impact on economic growth.

Error Correction Methods with Political Time Series

2016 ◽  
Vol 24 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Taylor Grant ◽  
Matthew J. Lebo
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Error Correction ◽  
Political Science ◽  
Correction Model ◽  
Long Run ◽  
Size Number ◽  
Political Time ◽  
True Time ◽  
General Error

While traditionally considered for non-stationary and cointegrated data, DeBoef and Keele suggest applying a General Error Correction Model (GECM) to stationary data with or without cointegration. The GECM has since become extremely popular in political science but practitioners have confused essential points. For one, the model is treated as perfectly flexible when, in fact, the opposite is true. Time series of various orders of integration–stationary, non-stationary, explosive, near- and fractionally integrated–should not be analyzed together but researchers consistently make this mistake. That is, withoutequation balancethe model is misspecified and hypothesis tests and long-run-multipliers are unreliable. Another problem is that the error correction term's sampling distribution moves dramatically depending upon the order of integration, sample size, number of covariates, and theboundednessofYt.This means that practitioners are likely to overstate evidence of error correction, especially when using a traditionalt-test. We evaluate common GECM practices with six types of data, 746 simulations, and five paper replications.

Equation Balance and Dynamic Political Modeling

2016 ◽  
Vol 24 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Matthew J. Lebo ◽  
Taylor Grant
Keyword(s):  
Error Correction ◽  
Correction Term ◽  
Moving Average ◽  
Fractional Integration ◽  
Time Series Modeling ◽  
Average Model ◽  
Correction Model ◽  
Long Run ◽  
Moving Average Model ◽  
General Error

The papers in this symposium agree on several points. In this article, we sort through some remaining areas of disagreement and discuss some of the practical issues of time series modeling we think deserve further explanation. In particular, we have five points: (1) clarifying our stance on the general error correction model in light of the comments in this issue; (2) clarifying equation balance and discussing how bounded series affects our thinking about stationarity, balance, and modeling choices; (3) answering lingering questions about our Monte Carlo simulations and exploring potential problems in the inferences drawn from long-run multipliers; (4) reviewing and defending fractional integration methods in light of the questions raised in this symposium and elsewhere; and (5) providing a short practical guide to estimating a multivariate autoregressive fractionally integrated moving average model with or without an error correction term.

scholarly journals Analyzing The Macroeconomic and Fundamental Determinants of Non-Performing Financing of Bank Muamalat Indonesia

2020 ◽  
Vol 11 (1) ◽  
pp. 49-78
Author(s):  
Pungky Lela Saputri ◽  
Ratno Agriyanto ◽  
Mujiyono Abdillah
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Time Series Data ◽  
Error Correction Model ◽  
Capital Adequacy ◽  
Series Data ◽  
Islamic Banking ◽  
Correction Model ◽  
Long Run ◽  
Short Run

Abstract: This study analyzes the macroeconomic and fundamentals of Islamic banking factors towards the non-performing financing (NPF) of Bank Muamalat Indonesia in the long run and short-run period 2005-2018. The data used in this study are quarterly time-series data of Bank Muamalat Indonesia Financial Report as the source of fundamentals of Islamic banking data and Bank Indonesia Monetary Policy Review as the source of macroeconomic data period 2005 - 2018. The analytical method used is the Error Correction Model (ECM). This study shows that in the long run, inflation, central bank (Bank Indonesia/BI) Rate, and capital adequacy ratio (CAR) significantly affect the NPF; meanwhile, the financing to deposit (FDR) ratio does not affect NPF. In the short term context, only CAR has a significant effect, yet inflation, BI Rate, and FDR have no significant impact on NPF. Thus, the novelty can present the result of analysis of factors that affect NPF in the long run and short run. The limitation of the study is the use of time-series data that are very likely to spurious regression.Abstrak: Penelitian ini bertujuan untuk menganalisis pengaruh faktor makroekonomi dan fundamental perbankan syariah terhadap NPF Bank Muamalat Indonesia dalam jangka panjang dan jangka pendek periode 2005-2018. Data yang digunakan dalam penelitian ini yaitu data runtun waktu Laporan Keuangan Triwulan Bank Muamalat Indonesia sebagai sumber data fundamental perbankan syariah dan Tinjauan Kebijakan Moneter Bank Indonesia sebagai sumber data makroekonomi periode 2005 – 2018. Faktor makroekonomi diwujudkan dalam variabel Inflasi dan BI Rate. Faktor fundamental perbankan syariah diwujudkan dalam variabel CAR dan FDR. Alat analisis yang digunakan yaitu Error Correction Model (ECM). Hasil analisis menunjukkan dalam jangka panjang Inflasi, BI Rate, dan CAR dengan nilai signifikansi 0.0026, 0.0001, dan 0.0032 berpengaruh signifikan dan FDR dengan nilai signifikansi 0.6940 tidak berpengaruh signifikan terhadap NPF. Dalam jangka pendek hanya CAR dengan nilai signifikansi 0.0056 yang berpengaruh signifikan sedangkan Inflasi, BI Rate, dan FDR dengan nilai signifikansi 0.0666, 0.9532, and 0.2065 berpengaruh tidak signifikan terhadap NPF. Kebaharuan penelitian ini yaitu penelitian ini mampu menyajikan hasil analisis faktor-faktor yang mempengaruhi NPF dalam jangka panjang dan jangka pendek. Meski begitu, terdapat juga limitasi pada penelitian ini yaitu penggunaan data time series yang rawan terkena regresi lancung.

ANALISIS PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2017 ◽  
Vol 11 (1) ◽  
pp. 1-20
Author(s):  
Ari Mulianta Ginting
Keyword(s):  
Economic Growth ◽  
Export Performance ◽  
Descriptive Analysis ◽  
Correction Model ◽  
Administration System ◽  
Long Run ◽  
Short Run ◽  
Available Information ◽  
Increasing Trends ◽  
The Relationship

Ekspor merupakan salah satu faktor terjadinya peningkatan pertumbuhan ekonomi suatu negara, sejalan dengan hipotesis export-led growth (ELG). Penelitian ini menganalisis perkembangan ekspor dan pertumbuhan ekonomi Indonesia periode kuartal I 2001 sampai dengan kuartal IV 2015. Penelitian ini menggunakan analisis deskriptif dalam menggambarkan perkembangan pertumbuhan ekonomi serta ekspor dan analisis kuantitatif metode Error Correction Model (ECM) dalam menganalisis efek jangka panjang dan jangka pendek dari ekspor terhadap pertumbuhan ekonomi. Pada periode penelitian, data yang ada menunjukkan bahwa ekspor dan pertumbuhan ekonomi Indonesia sama-sama mengalami peningkatan. Hasil regresi ECM menunjukkan bahwa ekspor memiliki pengaruh yang positif dan signifikan secara statistik terhadap pertumbuhan ekonomi Indonesia, yang mendukung hipotesis bahwa ELG berlaku untuk Indonesia. Berdasarkan hasil penelitian ini, maka untuk mendorong pertumbuhan ekonomi Indonesia diperlukan peningkatan kinerja ekspor Indonesia. Peningkatan kinerja ekspor Indonesia dapat dilakukan dengan berbagai cara, salah satunya adalah dengan perbaikan sistem administrasi ekspor, peningkatan riset dan pengembangan produk Indonesia, peningkatan sarana dan prasarana infrastruktur, stabilitas nilai tukar dan perluasan pasar non tradisional, termasuk perbaikan struktur ekspor komoditas. Export is one of the factors behind the economic growth which is in line with the export-led growth hypotesis (ELG). This research analyzes the relationship between economic growth and export of Indonesia during first quarter of 2001 until fourth quarter of 2015. It employs descriptive analysis to describe export movement and economic growth during the study period and ECM model to analyze the long run and the short run effects of export on the economic growth. The available information indicated that, during the study period, both export and economic growth showed similar increasing trends. The result of the ECM model revealed that export had a positive and statistically significant relationship with the economic growth, supporting the hypotesis of ELG in Indonesia. Hence, to accelerate economic growth, efforts are required to boost the export performance in Indonesia. The Export performance can be increased by several way, such as improving the export administration system, increasing the research and development of Indonesian products, improving the facilities and infrastructure, exchange rate stability and the non-tradisional markets expansion, and including improvement of the export commodity structure.

Hypothesis testing with error correction models

2021 ◽  
pp. 1-9
Author(s):  
Patrick W. Kraft ◽  
Ellen M. Key ◽  
Matthew J. Lebo
Keyword(s):  
Hypothesis Testing ◽  
Error Correction ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Correction Coefficient ◽  
Correction Model ◽  
Long Run ◽  
Independent Variable ◽  
The Right ◽  
General Error

Abstract Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$ , to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

Enhancing Economic Growth and Government Revenue Generation in Nigeria: The Role of Diaspora Remittances

2021 ◽  
pp. 003464462110256
Author(s):  
Dal Didia ◽  
Suleiman Tahir
Keyword(s):  
Economic Growth ◽  
Parameter Estimates ◽  
Gross Domestic Product Growth ◽  
Correction Model ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
Short Run ◽  
The Empirical Analysis

Even though remittances constitute the second-largest source of foreign exchange for Nigeria, with a $24 billion inflow in 2018, its impact on economic growth remains unclear. This study, therefore, examined the short-run and long-run impact of remittances on the economic growth of Nigeria using the vector error correction model. Utilizing World Bank data covering 1990–2018, the empirical analysis revealed that remittances hurt economic growth in the short run while having no impact on economic growth in the long run. Our parameter estimates indicate that a 1% increase in remittances would result in a 0.9% decrease in the gross domestic product growth rate in the short run. One policy implication of this study is that Nigeria needs to devise policies and interventions that minimize the emigration of skilled professionals rather than depending on remittances that do not offset the losses to the economy due to brain drain.

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