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.

Introduction to Symposium on Time Series Error Correction Methods in Political Science

2016 ◽  
Vol 24 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Janet Box-Steffensmeier ◽  
Agnar Freyr Helgason
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Correction ◽  
Political Science ◽  
Time Series Data ◽  
Series Data ◽  
Correction Model ◽  
Methodological Choices ◽  
Made In ◽  
General Error

In recent years, political science has seen a boom in the use of sophisticated methodological tools for time series analysis. One such tool is the general error correction model (GECM), originally introduced to political scientists in the pages of this journal over 20 years ago (Durr 1992; Ostrom and Smith 1992) and re-introduced by De Boef and Keele (2008), who advocate its use for a wider set of time series data than previously considered appropriate. Their article has proven quite influential, with numerous papers justifying their methodological choices with reference to De Boef and Keele's contribution.Grant and Lebo (2016) take issue with the increasing use of the GECM in political science and argue that the methodology is widely misused and abused by practitioners. Given the recent surge of research conducted using error correction methods, there is every reason to take their suggestions seriously and provide a fuller discussion of the points they raise in their paper. The present symposium serves such a role. It consists of Grant and Lebo's critique, a detailed response by Keele, Linn, and Webb (2016b), and shorter comments by Esarey (2016), Freeman (2016), and Helgason (2016). Finally, Lebo and Grant (2016) and Keele, Linn, and Webb (2016a) reflect on the contributions made in the symposium, as well as discuss outstanding issues.

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.

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.

scholarly journals The Long-Run and Short-Run Impacts of Investment, Export, Money Supply, and Inflation on Economic Growth In Indonesia

2019 ◽  
Vol 22 (1) ◽  
Author(s):  
Erni Panca Kurniasih
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Error Correction ◽  
Money Supply ◽  
Time Series Data ◽  
Error Correction Model ◽  
Series Data ◽  
Correction Model ◽  
Long Run ◽  
Short Term Investment

ABSTRACTThe development of investment and exports in Indonesia shows an increase, as well as money supply, while the inflation rate shows a decline, but this is not always followed by increasing economic growth. This study aims to explain the relationship between investment, export, money supply and inflation with the economic growth in Indonesia. The data used was time series data from the first quarter in 2001 to the fourth quarter in 2014 and was analyzed using multiple regression models with Error Correction Model (ECM) and classical assumptions. The study findings show that in short-term investment, export, money supply and inflation are not significant to economic growth. In long-run, investment has negative and significant effect on the economic growth, while export, money supply and inflation have positive and significant effect on the economic growth in Indonesia. Bank Indonesia must applied a tight money policy consistently to achieve the long-term inflation target ABSTRAKPerkembangan investasi dan ekspor di Indonesia menunjukkan peningkatan, demikian pula jumlah uang beredar, sementara tingkat inflasi menunjukkan penurunan, namun hal tersebut tidak selalu diikuti dengan meningkatnya pertumbuhan ekonomi. Studi ini bertujuan untuk menjelaskan hubungan antara investasi, ekspor neto, jumlah uang beredar dan inflasi terhadap pertumbuhan ekonomi di Indonesia. Data yang digunakan adalah data time series dari kuartal pertama tahun 2001 hingga kuartal keempat tahun 2014 dan dianalisa dengan menggunakan model regresi berganda dengan Error Correction Model (ECM). Hasil studi menunjukkan  bahwa investasi, ekspor, jumlah uang beredar dan inflasi tidak signifikan terhadap pertumbuhan ekonomi di Indonesia dalam jangka pendek. Investasi berpengaruh negatif dan signifikan terhadap pertumbuhan ekonomi di Indonesia dalam jangka panjang, sedangkan ekspor , jumlah uang beredar dan inflasi berpengaruh positif dan  signifikan terhadap pertumbuhan ekonomi di Indonesia. Bank Indonesia harus menerapkan kebijakan moneter yang ketat secara konsisten pada pencapaian sasaran inflasi jangka menenngah 

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.

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.

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 INTERAKSI ASPEK PERMODALAN, RISIKO PEMBIAYAAN, DAN INDIKATOR MAKROEKONOMI DALAM MEMPENGARUHI PROFITABILITAS BPRS DI INDONESIA PERIODE 2014-2018

2020 ◽  
Vol 5 (3) ◽  
pp. 401
Author(s):  
Feri Irawan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Error Correction ◽  
Error Correction Model ◽  
Series Data ◽  
Short Term ◽  
Correction Model ◽  
Long Run ◽  
Short And Long Term

<p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>This study aims to analyze the effect of capital aspects (CAR), financing risk (NPF) and macroeconomic variables including economic growth, inflation and the BI Rate on profitability (ROE) in the short and long term. By using time series data for the monthly period from 2013-2018 and the Error-Correction Model (ECM) and cointegration approach, it is found that CAR and NPF do not have a significant effect on ROE in the short and long term. Economic growth, inflation and the BI Rate in the short term do not have a significant effect on ROE, in the long run economic growth, inflation and the BI Rate have a significant effect on ROE. In the short term, economic growth, inflation and the BI Rate disturb the balance of profitability, but in the long run it returns to its equilibrium level. It is necessary to integrate the BPRS policy strategy in managing capital and risk with government policies related to economic growth and inflation.</em></p><p><em> </em></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>Penelitian bertujuan menganalisis pengaruh aspek permodalan (CAR), risiko pembiayaan (NPF) dan variabel makroekonomi yang meliputi pertumbuhan ekonomi, inflasi dam BI Rate  terhadap profitabilitas (ROE) dalam jangka pendek dan jangka panjang. Dengan menggunakan data time series periode bulanan dari tahun 2013-2018 dan pendekatan Error-Correction Model  (ECM) dan kointegrasi, ditemukan bahwa CAR dan NPF tidak berpengaruh secara signifikan terhadap ROE dalam jangka pendek dan jangka panjang. Pertumbuhan ekonomi, inflasi dan BI Rate dalam jangka pendek tidak berpengaruh signifikan terhadap ROE, dalam jangka panjang pertumbuhan ekonomi, inflasi dan BI Rate berpengaruh signfikan terhadap ROE. Pada jangka pendek, pertumbuhan ekonomi, inflasi dan BI Rate menggangu keseimbangan profitabilitas namun dalam jangka panjang kembali pada tingkat keseimbangannya. Diperlukan pengintegrasi strategi kebijakan BPRS dalam mengelola permodalan dan risiko dengan kebijakan pemerintah terkait dengan pertumbuhan ekonomi dan inflasi.</em><em></em></p><p align="right"> </p>

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 Analysis of the relationship between the unemployment rate and the contribution of the productive sectors to GDP in Sudan: using ARDL: تحليل العلاقة بين معدل البطالة ومساهمة القطاعات الإنتاجية بالناتج المحلي الإجمالي في السودان: باستخدام نماذج الانحدار الذاتي للإبطاء الموزع ARDL

2019 ◽  
Vol 3 (11) ◽  
Author(s):  
Yousif Saeed Ahmed Amin ,  Suha Seifeldin Noureldaim Ahmed
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Unemployment Rate ◽  
Error Correction Model ◽  
Correction Model ◽  
Industrial Sectors ◽  
Long Run ◽  
Augmented Dickey Fuller ◽  
The Relationship

The study aims to examine the relationship between the unemployment rate and the contribution of the productive sectors to gross domestic product (GDP) in Sudan. It is assumed that there is statistically significant relationship between the unemployment rate and the contribution of the agricultural, industrial and service sectors in the GDP. The variables were subjected to several econometrics tests, such as Augmented Dickey–Fuller test (ADF), Autoregressive Distributed- lagged (ARDL) and the Error Correction Model (ECM) to test the short- and long- term relationship between study variables. The results of the descriptive analysis indicate that the average of unemployment rate is (17.7%) exceeds the average growth rate (4.9%) more than three times. While the results of the econometrics tests, including Augmented Dickey–Fuller, confirmed that the time series of the contribution of the agricultural and the industrial sectors are integrated from the degree one, while the time series of the unemployment rate is stationary at the level. The bounds test for co- integration within the Autoregressive Distributed- lagged methodology results provided evidence of a long- run equilibrium relationship between the unemployment rate and the share of productive sectors in GDP. While the estimation results of the long- run parameters of the ARDL the model showed a negative correlation between the unemployment rate and the share of the industrial sectors in the gross domestic product, with a time lag of (4) time periods. While the results of the error correction model confirmed that the unemployment rate is adjusted to its equilibrium value in each time period by 4.4% of the remaining balance of the period with onetime lag.  According to the results, the study recommended restructuring the productive sectors of the Sudanese economy, increasing the investments directed towards them, developing them, raising their efficiency, absorptive and operational capacity through multiple strategies that seek to increase employment opportunities. In addition to improve the efficiency of Sudanese labor through the development of educational curricula, training programs and professional to improve the efficiency of the supply of labor and increasing the demand for them in a way that absorbs the increasing numbers in the workforce. In addition to adopts strategies that focus on transformational training in line with current and future markets need.

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