Unit Roots In Time Series Research Articles

Does Tourism Development Influence the Economic Growth in Pakistan? Evidence from ARDL and Causality Approach

This study analyzes the influence of tourism development on economic growth (GDP) in the case of Pakistan's economy using the time series data covering the spanning from 1995-2022. In order to unveil reliable and robust results, a more appropriate time-series approach was applied for the current article. Time series unit root tests infer that all the variables have unit root problems at levels I(0), however, after taking the first difference I(1), all series turn into following the stationary process. Furthermore, (ARDL) bound test reveals that a long-term cointegration prevails among all analyzed variables. The Granger causality test is also utilized to check the causal relationship among the series. The long-run elasticity estimates examine a statistically significant and positive link between the sect tourism sector GDP growth in the case of Pakistan. Furthermore, political instability and inflation rate have an inverse influence on GDP growth, whereas, capital investment has a positively significant influence on GDP growth. In addition, it is concluded that tourism sector development helps to move upward shift in the economy of Pakistan by generating more profits for the country. Finally, it is suggested that goverment should have to focus on investing in the tourism sector and provide better facilities for tourists.

Testing hysteresis for the US and UK involuntary part-time employment

ABSTRACT In this paper, we test the persistence of involuntary part-time employment, making use of large historical series for the US and UK. We adopted a comprehensive macro-econometric approach, based on a battery of panel and time series unit root and stationarity tests, also allowing for flexible specifications as fractional integration and structural breaks in the series. For both countries, our results provide robust evidence on the existence of a long memory process and a structural break in the mean of the series in The Great Recession surroundings. Importantly, these findings suggest that relevant shocks affecting labour market outcomes (e.g. The Great Recession) are likely to have long-lasting structural effects on underemployment shares. Fortunately, this result also leaves room for designing effective labour market policies to counteract structural underemployment in the long-run. Finally, the micro-economic roots of this aggregate behaviour claim for further investigation.

Analyzing asymmetric ecological performance under structural change, technological innovation, and trade diversification: fresh insights from the USA.

The present research scrutinizes the influences of trade diversification, air transportation, technological innovation, and economic complexity on ecological footprint from 1990 to 2019. The findings of the both time series unit root (with and without structural break) tests confirm that none of a single variable is stationary more than the first difference. Furthermore, the Wald and nonlinear autoregressive distributed lag bound methods check asymmetry and long-term cointegration relationship between the intended variables, respectively. Moreover, this study uses the nonlinear autoregressive distributed lag model to estimate the short-run and long-run coefficients/elasticity of the ecological footprint function. Following the empirical evidence, the findings revealed that positive (negative) components in trade diversification curtail the ecological footprint in the long-run. In addition, a positive shock in air transportation leads to an increase in ecological footprint in the long-run. Nevertheless, a negative shock in air transportation exerts a significant and adverse influence on the level of ecological footprint in the long-run. Furthermore, a positive (negative) shock in technological developments significantly reduces environmental pollution in the US economy in the long-run. Besides, the outcomes from economic complexity discovered a positive shock will significantly overcome the pressure on the environment in the long-run. However, in the short-run, it is observed that negative shock in trade diversification will lead to increase the ecological footprint level in USA. Similarly, a positive shock in air transportation will lead to increase the pollution level in the short-run. In contrast, a negative shock in air transportation will lead to reduce the pressure on the environment in the short-run. Besides, in terms of policy realization, the present research recommends adopting trade synchronization, harmonic trade strategies, and investment in technological innovations to diminish the existing level of ecological footprint in the region. For sustainable development, this study put forward for instantaneously encouraging the expansion of the digital economy and reducing air pollution, accelerating the green transformation, and impelling the industrial agglomeration process in the USA.

Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Urban growth in the long term: Belgium, 1880–1970

We take advantage of a new data set on Belgian cities to test random growth, that is, Gibrat’s law. This unique data set provides annual population estimates for all Belgian municipalities (2680 cities) from 1880 to 1970. The use of panel data methodology and unit root tests can provide a precise test of Gibrat’s law (a unit root is equivalent to random growth). We run both time series and panel data unit root tests, thus obtaining strong support for random growth in the long term. Results hold when allowing for the presence of one and two structural breaks in the mean, with the timing of the breaks coinciding with some major historical events, such as the World Wars and the economic crisis of 1929–1933.

A comparative study on p value combination tests for unit roots in multiple time series

This note offers a Monte Carlo investigation of the performance of nine p value combination methods for unit root testing in multiple time series using a novel approach to simulation. Rather than on time series, simulations are based on random draws of Dickey-Fuller p values under the null and under local alternatives. This makes it possible to investigate the properties of the different combiners under ideal conditions in which small sample effects and possible model misspecification issues have no role. The results show that no combination approach delivers a uniformly best test in the presence of independent p values. However, the probit combination method and the sum method on average outperform the other combiners within the unit root setting. The performance of the combination tests is generally unsatisfactory in the presence of cross-correlated p values.

Stability of profits and earnings management in the transport sector of Visegrad countries

Research background: Business profit and its stable development are key performance indicators. Many enterprises performed earnings manipulation, either upward or downward, according to the current business and macroeconomic situation, as well as time. These activities may interrupt the stationarity of time series. This article focuses on the transport enterprises, and the assessment of bonds in their earnings. Purpose of the article: The target of the article was to identify the occurrence of non-stationary and its unit root in the EBITDA of transport enterprises for each country in V4 during the period of 2010?2019. Methods: The stationarity and unit roots in time series were tested by the Kwiatkowski, Phillips, Schmidt, and Shin tests and the Augmented Dickey-Fuller based on the samples of 470 Slovak, 405 Czech, 774 Polish, and 1,056 Hungarian. The behavior of earnings manipulation (the first cause of non-stationarity) was indicated by the Modified Jones model. Additional causes for non-stationarity were confirmed by the regression analysis, including factors such as the GDP, unemployment rate, average monthly gross wage, and the Ease of doing business index. Findings & value added: The non-stationarity in the time series of EBITDA was disclosed for each country in the V4 region. Earnings management was discovered to be the cause of this erratic development. Thus, the value-added for the authorities and auditors is to show the association between non-stationary and creative accounting. In addition, purposeful downward manipulation in the transport sector occurs, not upward, which is typical in general. The methodology used in the study may be applied cross-sectorally in emerging countries. The labelling of specific macroeconomic variables depending on the country offers enterprises the opportunity to focus on factors with a crucial influence on their existence and activities.

FİNANSAL GELİŞMENİN YENİ YÖNLERİNİ ARAYIŞTA TÜRKİYE ÖRNEĞİ: EKONOMIK BÜYÜME İLİŞKİSİ

This study was conducted to determine the relationship between economic growth and financial development in Turkey for the period 1998:Q1 to 2021:Q1. For this purpose, following traditional practices, economic growth is monitored using the real gross domestic products’ changes. Financial development(FD) of Turkey’s monitored by using two aspects of financial development that are found equally important. These aspects are ‘financial institutions development’(FID) and ‘financial markets development’(FMD). Within the scope of the study, which is designed as a time series study, unit root and stationarity process tests were performed. Finding the series stationary, vector autoregressive model (VAR) is proposed. After lag length selection with the stable model, impulse response(IR) analyses were performed. After the IR analyses variance decomposition was performed using the Cholesky method. Both the economic development and financial development are considered to be important for Turkey and they have a relationship. The variables used in the study were found to be representative but not enough to conduct political or financial decisions. With the digital transformation, new variables should also be searched to represent the financial development and economic growth.

IS THE REPORTS-BASED MEASURE OF UNCERTAINTY STATIONARY? EVIDENCE FROM A NEW PANEL RESIDUAL AUGMENTED LEAST SQUARES UNIT ROOT TEST

To avoid spurious inferences, researchers analyzing the dimensions of uncertainty need to determine whether it is nonstationary. The degree of persistence of uncertainty also indicates the duration of the negative impact of an uncertainty shock on the economy. We use a new panel residual augmented least squares unit root test that allows for heterogeneous structural breaks in both intercepts and slopes of a series to determine the degree of persistence of the reports-based measure of uncertainty and whether it is nonstationary for 143 countries. This group of countries accounts for 99% of the world’s gross domestic product (GDP). To assess the robustness of our results, we also use recently developed univariate time-series unit root tests that allow for structural breaks and panel unit root tests that accommodate cross-sectional dependence and nonlinearity. Furthermore, an autoregressive wild bootstrap approach is utilized to examine the stationarity of the series. The results are virtually unambiguous in indicating that the reports-based measure of uncertainty is stationary in all countries considered. The results also suggest that uncertainty has a negative impact on the growth rate of GDP. The policy implications of the results are also discussed.

ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN INDIA: CAUSALITY ANALYSIS

Energy is an indispensable part of the growth process for every country. Cost effective, non- threatening energy consumption is very important. Electricity is that form of energy which is used extensively both in the commercial sector and noncommercial sector. The fast growth of industrialization, urbanization, population growth and the modern agricultural sector has driven the demand for electricity to a much higher level as electricity is the core of production process. This study aims to investigate the relationship between electricity consumption and economic growth in India over the period 1991-2018. Per capita GDP is taken as proxy variable for economic growth. The study uses time series analysis and Unit root analysis, Johansen Co-integration test and Granger Causality test have been applied for analysis. It is found that there is a causal relationship between electricity consumption and economic growth but not vice versa. The granger causality test shows that there is a unidirectional relationship between electricity consumption and economic growth

Causality of urbanization, carbon dioxide, and economic growth with life expectancy: a perspective of Bangladesh

The paper has investigated the impact of urbanization, carbon dioxide, and economic growth on life expectancy for Bangladesh from 1972 to 2014. Using time series unit root test we find that all variables are stationary at order of integration one. The Granger causality test supports that there is a bidirectional relationship between urbanization, carbon dioxide, and life expectancy, while economic growth has no causal association with life expectancy for Bangladesh.

Bootstrap seasonal unit root test under periodic variation

Both seasonal unit roots and periodic variation can be prevalent in seasonal data. In the testing of seasonal unit roots under periodic variation, the validity of the existing methods, such as the HEGY test, remains unknown. The behavior of the augmented HEGY test and the unaugmented HEGY test under periodic variation is analyzed. It turns out that the asymptotic null distributions of the HEGY statistics testing the single roots at 1 or −1 when there is periodic variation are identical to the asymptotic null distributions when there is no periodic variation. On the other hand, the asymptotic null distributions of the statistics testing any coexistence of roots at 1, −1,i, or −i when there is periodic variation are non-standard and are different from the asymptotic null distributions when there is no periodic variation. Therefore, when periodic variation exists, HEGY tests are not directly applicable to the joint tests for any concurrence of seasonal unit roots. As a remedy, bootstrap is proposed; in particular, the augmented HEGY test with seasonal independent and identically distributed (iid) bootstrap and the unaugmented HEGY test with seasonal block bootstrap are implemented. The consistency of these bootstrap procedures is established. The finite-sample behavior of these bootstrap tests is illustrated via simulation and prevails over their competitors’. Finally, these bootstrap tests are applied to detect the seasonal unit roots in various economic time series.

Структурные сдвиги и тестирование на единичный корень

his review discusses methods of testing for a unit root in a time series in the presence of structural breaks. Separately the methods for break date estimation under uncertainty over the order of integration are considered. The review covers a large number of recently developed testing methods that are robust to various types of data uncertainty, including initial conditions and time-varying volatility. The survey introduces the reader to practical application of modern testing methods together with an understanding of the development of these methods.

SUSTAINABILITY OF ‘NET ERRORS AND OMISSIONS’ OF BALANCE OF PAYMENTS – WITH GLOBAL RESULTS

This study farewells the previous studies by (re-)examining the sustainability of ‘net errors and omissions’ (NEO) for 98 countries with sample periods between 1966 and 2016. The 11 time-series unit root (stationary) tests suggest that all sample countries have their sustainable NEO. The panel unit root tests support this finding. This study also explores that the income group does not determine the ‘fitness’ of NEO sustainability, but the institutional quality does.

A Simple Unit Root Test For Near Double-Integrated Time Series

We consider the problem of testing for unit root in time series where the error term of the series is near unit root. As the error term approaches unit root, existing tests no longer retain reasonable small sample properties. We introduce a test statistic that is well-behaved in small sample under such condition. Empirical applications reject the unit root null hypothesis for macroeconomic series, such as unemployment and consumer prices, where conventional unit root tests have been unable to do so.

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

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.

Unit Root Tests for Dependent Micropanels

This paper proposes a new test for the null hypothesis of panel unit roots for micropanels with short time dimensions (T) and large cross-sections (N). There are several distinctive features of this test. First, the test is based on a panel AR(1) model allowing for cross-sectional dependency, which is introduced by a factor structure of the initial condition. Second, the test employs the panel AR(1) model with AR(1) coefficients that are heterogeneous for finite N. Third, the test can be used both for the alternative hypothesis of stationarity and for that of explosive roots. Fourth, the test does not use the AR(1) coefficient estimator. The effectiveness of the test rests on the fact that the initial condition has permanent effects on the trajectory of a time series in the presence of a unit root. To measure the effects of the initial condition, the present paper employs cross-sectional regressions using the first time-series observations as a regressor and the last as a dependent variable. If there is a unit root in every individual time series, the coefficient of the regressor is equal to one. The t-ratios for the coefficient are this paper’s test statistics and have a standard normal distribution in the limit. The t-ratios are based on the OLS estimator and the instrumental variables estimator that uses reshuffled regressors as instruments. The test proposed in this paper makes it possible to test for a unit root even at T = 2 as long as N is large. Simulation results show that test statistics have reasonable empirical size and power. The test is applied to college graduates’ monthly real wage in South Korea. The number of time-series observations for this data is only two. The null hypothesis of a unit root is rejected against the alternative of stationarity.

Unit Roots in Time Series with Changepoints

Many financial time series are nonstationary and are modeled as ARIMA processes; they are integrated processes (I(n)) which can be made stationary (I(0)) via differencing n times. I(1) processes have a unit root in the autoregressive polynomial. Using OLS with unit root processes often leads to spurious results; a cointegration analysis should be used instead. Unit root tests (URT) decrease spurious cointegration. The Augmented Dickey Fuller (ADF) URT fails to reject a false null hypothesis of a unit root under the presence of structural changes in intercept and/or linear trend. The Zivot and Andrews (ZA) (1992) URT was designed for unknown breaks, but not under the null hypothesis. Lee and Strazicich (2003) argued the ZA URT was biased towards stationarity with breaks and proposed a new URT with breaks in the null. When an ARMA(p,q) process with trend and/or drift that is to be tested for unit roots and has changepoints in trend and/or intercept two approaches that can be taken: One approach is to use a unit root test that is robust to changepoints. In this paper we consider two of these URT's, the Lee-Strazicich URT and the Hybrid Bai-Perron ZA URT(Herranz, 2016.) The other approach we consider is to remove the deterministic components with changepoints using the Bai-Perron breakpoint detection method (1998, 2003), and then use a standard unit root test such as ADF in each segment. This approach does not assume that the entire time series being tested is all I(1) or I(0), as is the case with standard unit root tests. Performances of the tests were compared under various scenarios involving changepoints via simulation studies. Another type of model for breaks, the Self-Exciting-Threshold-Autoregressive (SETAR) model is also discussed.

Unit Roots in Economic and Financial Time Series: A Re-Evaluation at the Decision-Based Significance Levels

This paper re-evaluates key past results of unit root tests, emphasizing that the use of a conventional level of significance is not in general optimal due to the test having low power. The decision-based significance levels for popular unit root tests, chosen using the line of enlightened judgement under a symmetric loss function, are found to be much higher than conventional ones. We also propose simple calibration rules for the decision-based significance levels for a range of unit root tests. At the decision-based significance levels, many time series in Nelson and Plosser’s (1982) (extended) data set are judged to be trend-stationary, including real income variables, employment variables and money stock. We also find that nearly all real exchange rates covered in Elliott and Pesavento’s (2006) study are stationary; and that most of the real interest rates covered in Rapach and Weber’s (2004) study are stationary. In addition, using a specific loss function, the U.S. nominal interest rate is found to be stationary under economically sensible values of relative loss and prior belief for the null hypothesis.

Structural breaks and unit root in macroeconomic time series: evidence from Nigeria

The discourse on the properties of macroeconomic time series has received a considerable interest in recent literature. This is because the presence of unit in a realization of a stochastic process implies that shocks to the time series have a persistent effect with policy implications. Hence, this paper investigates the unit root properties of ten Nigerian macroeconomic time series using quarterly data from 1981-2015. For comparison, first we apply the conventional augmented Dickey-Fuller unit root test to examine the null of a unit root in the ten macroeconomic series, and we proceed to examine the unit root properties using the Lagrange Multiplier (LM) endogenous unit root tests that account for the presence of one-break and two-break as proposed by Lee and Strazicich (2003, 2013). On employing the augmented Dickey-Fuller test that does not account for structural breaks, our empirical results indicate that the unit root null hypothesis cannot be rejected for nine of the ten series considered in the study. However, on utilizing the Lagrange Multiplier (LM) endogenous one and two structural breaks test, we reject the unit root null in favour of the one and two-break stationary alternative for six of the ten series (60% rejection) considered in our study. These results imply that unit root tests that do not account sufficiently for the presence of structural breaks lead to misleading inference. These findings have important implications for the macroeconomic policy-making, modeling and forecasting of the Nigerian economy. We therefore, recommend that structural breaks should be taken into account in the econometric analysis of Nigerian macroeconomic variables.