Spurious Regression Research Articles

Identifying the multiple nexus between geopolitical risk, energy resilience, and carbon emissions: Evidence from global data

Under the frequent occurrence of extreme weather events and the increasingly complex geopolitical situation in recent years, how to balance the nexus between energy security, economic stability, and the achievement of sustainable development goals has gradually become the forefront of international attention. In light of this context, this paper examines the multiple nexus between geopolitical risks (GR), energy resilience (ER), and carbon emissions (PCO2) using annual panel data from 31 countries between 2000 and 2019. The second-generation unit root, cointegration tests, wavelet quantile correlation analysis, and other advanced statistical techniques are adopted to address methodological challenges effectively and avoid spurious regression. The findings consistently indicate the inhibitory effects of geopolitical risks and energy resilience on PCO2 across all quantiles and periods. And the restraining effect exhibits significant heterogeneity and asymmetry, particularly in non-emerging economies. Besides, the bidirectional causality among the three variables has been confirmed. Further, we confirm that the combined effects of energy resilience and geopolitical risks are particularly noteworthy for future carbon reduction efforts, especially in scenarios with low-ER and high-GR. In a short, the findings provide novel insights for global emissions reduction and offer specific policy recommendation to attain sustainable development goals.

Could Regression of Stationary Series Be Spurious?

Spurious regression, commonly associated with independent and (nearly) non-stationary time series, has been extensively studied. However, the potential for spurious outcomes in regression involving stationary time series remains largely unexplored, representing a gap in the literature. To address this gap, we propose that regression of stationary time series may yield spurious outcomes and conduct a comprehensive investigation to verify this conjecture. Additionally, we present a remedy algorithm to mitigate spurious effects and improve model interpretability. Through extensive simulations, we validate our conjecture and demonstrate the efficacy of the proposed remedy. A numerical analysis further illustrates the practical utility of our approach. This study offers a fresh perspective on spurious regression and provides a practical solution to enhance the reliability of regression analyses involving stationary time series data.

Analysis of factors influencing small-scale agricultural product prices from the perspective of the online public—a case study of China

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Therefore, this study takes scallions as an example and extracts the influencing factors of their prices based on the LDA-NLP-TVP-VAR model from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Panel Data Cointegration Testing with Structural Instabilities

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran’s common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings.

Market potential, spatial theories and spatial trends

ABSTRACT Previous literature on European regions has shown that structural estimation of New Economic Geography (NEG) wage-type equations obtains results similar to those obtained using old regional economics techniques. I show that this similarity is due to the presence of global spatial trends in the variables (first-order non-stationarity), which produce spurious regressions. Formal tests and graphical models confirm that any variable displaying a core–periphery spatial pattern produces similar predictions for European regional per capita income. Empirical tests of spatial theories should thus pay attention to the geographical features of the administrative units and the global spatial trends of the variables analysed.

Presence of Debt Overhung Effect in Pakistan: Empirical Evidence with ARDL Model

Many developing nations, including Pakistan, have struggled with the challenge of maintaining debt sustainability due to insufficient revenue generation to meet economic crises. The primary aim of this study is to find how external debt, debt servicing, and exports influence the economic growth of Pakistan, utilizing data from 2000 to 2020. Augmented Dickey-Fuller test (ADF) is applied to evade spurious regression, revealing that debt servicing is stationary at level. In contrast, GDP, external debt, and exports are stationary at their first difference. For this reason, the ARDL model has been used to find the long-run and short-run interdependence among external debt, debt servicing, exports, and GDP. The empirical findings of this study revealed a negative and significant impact of external debt on Gross Domestic Product (GDP), supported by the “Debt Overhung Effect”. Conversely, debt servicing has a positive but statistically insignificant impact on GDP. Exports have a positive and significant impact on GDP growth. Moreover, to confirm the long-term stability of our model Cumulative Sum of Recursive Residual (CUSUM) and CUSUM of square have been used. This research contributes novel insights to the work conducted by other researchers, shedding light on the intricate relationship among external debt, debt servicing, exports, and GDP growth of Pakistan. Additionally, by estimating the effect of exports on GDP, valuable insights into the country's debt repayment capacity are provided. Export earnings constitute a primary source of foreign currency inflow.

Understanding of Causes of Spurious Associations: Problems and Prospects

This paper contributes to the social science literature when analyzing survey or time series data social scientists use spurious regression without due consideration of its assumptions and the data structure. This results in misinterpretation and misleading conclusions about the population. The paper reviews basic statistical and econometrics literature which led to the development of modern time series analysis in the presence of spurious regression. It concludes that the term ‘Spurious’ was well known before the Granger and Yule’s work in time series context rather than cross-sectional data. The same reasons can produce spurious regression today and surely the solution doesn’t exist in the cointegration analysis. Social scientists and applied econometrician investigators need more serious thinking and care to avoid spurious regression, if it is necessary even if data is stationary or cross-sectional. In this study, we extended the Ghouse experiment which is based on simulated data by employing real-world data to assess the effectiveness of the newly proposed Ghouse Equation in comparison to conventional approaches. The findings demonstrate that the Ghouse Equation produces the lowest probability of spurious regression as compared to its counterparts. Moreover, in forecasting performance, Ghouse Equation outperformed its counterparts. These results highlight the Ghouse Equation as a valuable and better tool for econometric analysis for nonstationary time series.

Economic instruments for natural resource efficiency: The role of carbon taxation and fiscal policy

The research attempts to examine various connections between carbon taxation, economic development, policy framework (i.e., Fiscal and Monetary Policies), natural resources utilization and carbon emissions in the context of Chinese economy from 1980 to 2022. The study seeks correspondence relationships fiscal policies, monetary policy, and carbon dioxide emissions. This research addresses estimation and spurious regression issues with the application of cutting-edge nonlinear bootstrapping tools for testing purposes. The analysis reveals long-term integrated relationships between the variables considered and carbon emissions. In fact, it is very relevant to say that both sets of economic policies (monetary and fiscal) do have a positive impact upon carbon emissions. The application of nonlinear causality indicated environmental anxieties, as the findings from this test reveal a unidirectional causality lacking any reciprocal influences between consumption, economic productivity and resource utilization, and carbon emissions. Basically, energy policy is governed under the umbrella of fiscal policies. Similarly, the results also endorse by highlighting a one-sided effect of fiscal policies, thus accelerating environmental deterioration. These results confirm the destruction caused to the environment by economic policies, and a sense of real urgency for significant policy reforms.

Bias correction based on AR model in spurious regression

<abstract><p>The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10–50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales.</p></abstract>

Possible Reasons for Spurious Regression in the Econometric Model (Case Study of Imports in the United States of America)

Purpose: The research aims to identify statistical, econometric, and economic indicators to infer the problem of spurious regression. Theoretical framework: The research used a descriptive and econometric approach to identify the dimensions of this problem and the most likely causes of its emergence. Design/Methodology/Approach: The research estimated four equations, the first two are simple linear regressions (y, x1) and (y, x2), the third is a multiple linear regression equation (y, x1, x2), and the fourth is a double logarithmic regression equation for multiple linear regression. The results of the four equations were compared to assess the standard methods and explore spurious regression. Findings: The research concluded with several findings, one of which is that a high value of the coefficient of determination (R2) in the absence of significance of the parameters is an indication of the problem of multicollinearity, and hence the possibility of spurious regression. Research, Practical & Social implications: Spurious regression can lead to misleading conclusions about the relationships between economic variables, this can have negative consequences for economic policymaking and other decision-making processes. The research on spurious regression can help to improve the accuracy and reliability of economic analysis, which can benefit society as a whole. Originality/Value: The paper could provide recommendations for how to avoid spurious regression in econometric models of imports. This could include recommendations for data collection, data preparation, and model specification.

Spurious Regressions and Panel IV Estimation: Revisiting the Causes of Conflict

Abstract The long-recognised spurious regression problem can lead to mistaken inference in panel instrumental variable estimation. Spurious correlations arising from correlated cycles in finite time horizons can make irrelevant instruments appear strong with signable consequences for estimated instrumental variable coefficients, or interfere with valid inference of causal effects from instrumental variable coefficients estimated using relevant instruments. The inclusion of time fixed effects in interacted specifications does not always resolve these problems. We demonstrate these concerns by revisiting recent studies of the causal origins of conflict. We offer diagnostic and corrective recommendations for avoiding the pitfalls arising from time series exhibiting persistence.

The popularity function: a spurious regression? The case of Austria

AbstractIn this paper we apply the unit root and cointegration methodology as well as other methods of modern econometric time series analysis to estimate popularity functions for the Austrian parties in power since the mid-1970s. We find only very rare evidence for economic variables influencing the popularity of the main political parties in the federal government, thereby challenging previous studies that claimed to have established such influences. When considering the determinants of the popularity of the incumbent party (the sum of the popularities of the parties in government), the unemployment rate turns out to have a negative short-run influence. The robustness of this result is checked by various methods, demonstrating the usefulness of the applied methodology to clarify such issues.

Causal Nexus Analysis of FDI, Trade Openness, Labor Force, Financial Development, and Economic Growth in Laos: An Empirical Study (2000-2018)

This research study explores the intricate web of causal relationships among foreign direct investment (FDI), trade openness, labor force participation, financial development, and economic growth in Laos. The analysis employs time series data spanning the period from 2000 to 2018, utilizing the advanced technique of bounds testing through the Auto Regressive Distributed Lag (ARDL) co-integration approach. Before delving into the econometric framework, it is imperative to ascertain the stationarity of the entire dataset to prevent the occurrence of spurious regression issues. To achieve this, we conducted the Augmented Dickey-Fuller (ADF) unit root test. The results of the unit root test indicate that the variables under consideration exhibit stationarity at both the level and first difference, providing a strong foundation for the subsequent ARDL modeling. The study's findings shed light on the intricate relationships between labor force participation, FDI, financial development, and economic growth. Notably, the research highlights a positive correlation among these variables, suggesting that an increase in labor force participation, FDI inflows, and financial development serves as a catalyst for heightened economic growth in Laos. However, an intriguing aspect of our findings is the negative impact of trade openness on economic growth, both in the short-run and long-run, a phenomenon warranting further investigation. This study contributes valuable insights to the understanding of Laos's economic dynamics and offers important implications for policymakers and stakeholders seeking to foster sustainable economic growth in the region.

İnsan Hakları Ekonomik Büyümeyi Artırır mı? Gelişmekte Olan Ülkelerden Panel Kanıt

This study attempts to examine the impact of human rights and economic growth by utilizing two measures of economic growth for developing countries and an unbalanced data spanning from 1961-2017. The hypothesis of the study asserts that improvement in human right protection score increases economic growth in an economy. Firstly I conducted panel unit root test to see if the variables are stationary. After verifying the stationarity of variables at levels (i.e., I(0)) based on panel unit root tests, empirical estimations were conducted without the fear of potential spurious regression problem. According to the estimation results, in parallel to prior expectations, positive statistically significant effect on economic growth was identified for the variables of human right protection, physical capital investment, education investment, openness whereas negative statistically significant effect on economic growth was identified for the variable of inflation in all models estimated. Estimation results indicate that countries trying to reach to higher economic growth, besides the other determinants of economic growth, must pay attention to human right protection and implement policies that prioritizing human right protection.

Effect of Agricultural Funding on Agricultural Output in Nigeria (1990-2020)

This study examined the effect of agricultural funding on agricultural growth in Nigeria spanning from 1990-2020. The specific objectives were to determine the effect of public capital expenditure on agricultural output in Nigeria, determine the effect of public recurrent expenditure on agricultural output in Nigeria, determine the effect of agricultural credit guarantee scheme on agricultural output in Nigeria, and the effect of Foreign Agricultural Grant on agricultural output in Nigeria. This study adopted the quasi-experimental research design. Secondary data were used in this study. Data used were gotten mainly from the publications of Central Bank of Nigeria (CBN) namely; Statistical Bulletin. The method adopted for analyzing is the Augmented Dickey Fuller test (ADF). Pre-estimation diagnostics tests were employed to check for stationarity of the data to prevent spurious regression analysis. From the regression result, it could be seen that only agricultural credit guaranteed scheme fund (ACGSF) was significant in influencing agricultural output in the period under study (1990-2020) as the probability values of its t-statistics were less than 5%, while public capital expenditure on agriculture (PCEXA), Foreign agricultural grant (FAG) and public recurrent expenditure on agriculture (PREXA) did not have any significant influence on agricultural output as the probability values of their t-statistics were greater than 5%. From the result, ACGSF had a positive and significant effect on agricultural output, meaning that a one-unit increase in funding to the ACGSF brought about (0.357068), this might be due to the ACGSF disbursed directly to farmers therefore its positive influence on agriculture output. On the contrary, FAG, PCEXA and PREXA had no significant effect on agricultural output within the period under study, this might be because these funds were not used for the purposes which were earmarked for. The study concluded that foreign agr

Integrasi Pasar Jangka Pendek Ayam Broiler di Indonesia

One of the fast-growing livestock subsectors is the poultry sector. This livestock subsector produces broiler chicken meat as a strategic commodity in addition to beef, milk, and eggs. The level of broiler chicken meat consumption is the highest order, followed by native chicken consumption, then beef. Broiler meat is a rapidly growing strategic commodity. This study uses ECM model analysis to determine the short-term market integration in broiler chickens—time series data on broiler chicken prices at the producer level between January 2015 and December 2020. This analysis's first step is testing broiler chicken meat producer price data stationarity in 28 provinces. Data that are not stationary will be heteroscedastic (have autocorrelation). In addition, time series data that are not stationary can also produce spurious regression, a condition in which two or more variables have a causality relationship, but there is no theory or logic underlying the direct causality relationship between two or more of these variables. According to the findings of this investigation, there is no complete integration. According to the study's findings, the weak flow of information in the market is suspected, so the information transmitted is not perfect. This shows that the broiler meat market in Indonesia leads to imperfect competition. The broiler's construction meat commodity market leads to oligopsony. Government intervention is required to establish broiler meat price control policies with the primary consideration of focusing on the price leader market that will be transmitted in harmony with other regions to make the broiler meat market structure more competitive.

Factors affecting groundnut output in Andhra Pradesh: co-integration and error-correction modeling

Groundnut is an important oilseed crop of India. The area under groundnut in India was maximum (8.0 million ha), however the production was less due to the lowest yield (938 kg/ha) among all Asian countries. Thus it becomes more important to identify the factors affecting groundnut productivity which is directly related to the farmer's income, sustainable supply and the price stabilization. Unless until the factors affecting groundnut productivity are known, the corrective measures can not be initiated. In this paper efforts have been made to examine whether producer's price, area cultivated, fertilizer applied and rainfall have an important effect on groundnut production in Andhra Pradesh using co-integration and error correction modeling. Co-integration and error correction modeling tend to solve spurious regression results obtained from the analysis of macro-economic data and also establish an equilibrium long-run relationship which enables one to carry out a valid inference of the explanatory variables that are responsible for affecting the output of the crop. A stationary test was performed which revealed only rainfall series was stationary at level, while other series become stationary at first differencing applying the unit root test. Johansen co-integration and error correction procedure was adopted which indicates the existence of five co-integrating vectors at 1% level of significance, hence rejecting the null hypothesis of no co-integrating vector. Further, a parsimonious error correction model was applied. The statistical significance of error correction model for groundnut validates the existence of an equilibrium relationship among the variables. The results, therefore, indicate the combined effect of area, fertilizer, rainfall and price jointly affect the output of groundnut in Andhra Pradesh. Therefore, a favorable price policy, assured irrigation and timely supply of fertilizers at remunerative price would help the farmers to allocate more area under groundnut. These measures are necessary to enhance the productivity as well as income of the farmers and also supply of groundnut in the benefit of consumers.

IMPACT OF POPULATION GROWTH ON POVERTY IN NIGERIA

This study investigates the impact of population growth on poverty rate in Nigeria over the period: 1986 to 2021. To achieve this objective, time series data was collected mainly from National Population Commission (NPC) and National Bureau of Statistics (NBS). The data was tested for stationarity using Augmented Dickey Fuller (ADF) and Phillips Perron (PP) techniques. The results indicate the nonstationary of the variables. That is, both population growth and poverty rate time series are integrated of order one I(1). The study tested for cointegration using Johensen cointegration and the result indicates the presence of cointegration among the variables of study. Considering the integration and cointegration nature of the data, Dynamic Ordinary Least Square was estimated to avoid the possible problems of spurious regression and serial correlation. The result revealed a significant negative long-run impact of population growth on poverty in in Nigeria. Based on the empirical findings this study recommends that both government, households, private sector and nongovernmental organisations should focus in building productive population in Nigeria.

Tax Reforms, Structural Breaks, and Dynamics of Tax Revenue in Fiji

ABSTRACT: The changing dynamics of tax revenue and its responses to domestic and external shocks play a pivotal role in the discussion of the sustainability of public finance in small developing economies. However, the empirical literature on the response of tax revenue to various shocks in developing economies in the Pacific is sparse. Despite several tax reforms over the last decade, the spending plans of the Fijian government since the 2017/2018 budget announcements have stimulated frequent public discussions regarding the sustainability of public debt. Against this background, the main purpose of this paper is to investigate the response of tax revenue in Fiji - a small developing island economy, to external and domestic shocks using historical annual data covering more than five decades. This study uses unit root tests that account for single and multiple endogenous structural breaks and finds that tax revenue in Fiji is a non-stationary process, I (1) with break dates coinciding with tax reforms, general elections, and political coups. The findings reveal that tax reforms and political developments are possible important sources of shocks to tax revenue and have important policy implications. First, shocks to tax revenue will have persistent effects, with the effects of adverse shocks being transmitted to other related variables and sectors of the Fijian economy. Second, historical values of tax revenue cannot be used to formulate forecasts of tax revenue and policymakers should consider other determinants of tax revenue (for example, the share of the agriculture sector, trade volume, political stability, education, life expectancy, infant mortality rates, age composition of the population, urbanization, inflation rate, and corruption) for forecasting purposes. The evidence of non-stationarity indicates that future studies on tax revenue need to carefully reflect on the modeling technique to avoid spurious regressions. Furthermore, our findings reveal that temporary fiscal measures will not have a permanent effect on tax revenue and sustainable revenue growth requires focusing on long-term (rather than short-sighted) reforms. Thus, our findings underscore the importance of evidence-based policy reforms to improve the resilience, efficiency, and sustainability of the tax system in Fiji to shelter it from future adverse shocks.

Methodological problems in studies on the Taylor rule

This paper raises concerns about the methodological approaches commonly adopted in typical Taylor rule studies. We find that many empirical studies on the Taylor rule do not follow the required econometric procedures. These studies ignore the presence of unit roots, cointegration, and serial correlation in their tests and estimation. The Taylor rule equation is typically estimated in levels. We show that the Taylor rule can be an unbalanced regression that involves a mixture of I(0) and I(1) variables. Spurious regressions may occur if the variables are not cointegrated and the Taylor rule equation is estimated using variables in levels. In addition, empirical models of the Taylor rule commonly include lags of the dependent variable, and equation residuals are serially correlated. The presence of lagged dependent variables and serially correlated residuals will cause biased and inconsistent least squares estimators. To illustrate our arguments, we re-examine two recent papers to point out the econometric problems that are general in typical Taylor rule studies. We show that an inadequate analysis of the time series properties of the individual series and diagnostic checks of the estimated equations can often lead to invalid conclusions about the empirical validity of the Taylor rule. We demonstrate how autoregressive distributed lag methods can overcome these issues and how the equation can be estimated efficiently.