OLS Estimates Research Articles

Bounded Weighted Exponential Distribution with Applications

In this article, we try to supplement the distribution theory literature by incorporating a new bounded distribution, called the bounded weighted exponential (BWE) distribution in the (0, 1) intervals by transformation method. The proposed distribution exhibits decreasing and left-skewed unimodal density while the hazard rate can have increasing and bathtub shaped. Although our main focus is on the estimation from the frequentist point of view, in addition, we derive some useful structural and statistical properties of the proposed BWE distribution. We briefly describe three classical estimators namely, the maximum likelihood estimators (MLE), the ordinary least-squares estimators (LSE) and the weighted least-squares estimators (WLSE). Monte Carlo simulations are performed to compare performances of the proposed methods of estimation for both moderate and large samples. An application of the model is presented by re-analyzing a real data set and comparisons are made with the fit attained by some other well-known distributions for illustrative purposes.

Empirical analysis of crude oil consumption and price on Ghana’s economic growth

AbstractThe objective of this paper was to investigate the impact of crude oil consumption and oil price on the growth of the Ghanaian economy. It proceeded with annual time series data (1980-2016) sourced from World Development Indicator (WDI) and Energy Information Administration (EIA). All variables used in the study were integrated of order one as suggested by the Augmented Dickey-Fuller (ADF) test. Further, the Johansen Cointegration test suggested the existence of cointegration among the variables. The study used the OLS estimation procedure.The study found a positive and statistically significant relationship between oil price and economic growth in the long run. On the other hand, an inverse relationship was found between crude oil consumption and economic growth in the long run.Based on the findings the study recommends that the government diversify the economy to reduce the shock the economy might experience in times of oil price shocks. Further, risk management instruments like physical reserves and hedging against oil prices should also be employed.Also, the study recommends policies that encourage efficient consumption of crude oil, especially in the productive sectors like industry in order to trigger growth. This notwithstanding, the study recommends effective measures to mitigate the externalities associated with increased production and consumption of crude oil, such as the carbon tax.

Unpaid Internships and Equality of Opportunity: A Pseudo-Panel Analysis of UN Data

One of the most pressing arguments against unpaid internships is that they impose a barrier to upward economic mobility. Intern experience leads to better employment outcomes, yet only individuals from relatively advantaged backgrounds can afford an unpaid stint of three to six months, resulting in unequal access for individuals of more modest means. This could be particularly relevant in an international organization context, where individuals from a broad spectrum of nationalities are employed. Using a unique intern dataset, I test this hypothesis in a pseudo-panel linear probability model of paid versus unpaid internships, conditional on two proxies for equality of opportunity: parents education and developing country origin. I find that having more highly educated parents leads to a 30% higher chance of taking an unpaid internship, confirming the unequal access hypothesis; however, more surprisingly, I find that individuals from developing countries are much more likely to take an unpaid internship (75% higher) than their developed country counterparts. These results are robust to several different specifications, yet contradict cross-section OLS estimates, suggesting that the influence of unobserved time-invariant heterogeneity is substantial.

A MULTIVARIATE ANALYSIS BETWEEN RENEWABLE ENERGY, CARBON EMISSION AND ECONOMIC GROWTH: NEW EVIDENCES FROM SELECTED MIDDLE EAST AND NORTH AFRICA COUNTRIES

The paper investigated cross-cutting issues relating to renewable energy, carbon-emission and economic growth for a group of 8 MENA countries covering the period 1990-2018. Adopting a modified linear Cobb-Douglas production function, the study adopted the Fully-Modified and the Dynamic OLS estimation technique in examining the aforementioned relationship. Findings from the panel FMOLS and DOLS for the region confirm that a significant relationship exists between CO2 emission and economic growth and that renewable energy consumption triggers a significant effect on economic growth as well. Conversely, the panel of the FMOLS result reveals that while economic growth reacts positively from the effect of CO2 emission, CO2 emission reacts negatively from the effect of renewable energy consumption, as against the positive outcome between renewable energy consumption and CO2 emission as reported by the DOLS. This goes to point out that most economies within this region are yet to uncover best and appropriate policies which can control the regulation of renewable energy prices, that can help take into consideration the stability in economic growth structure and at the same time, mitigate the emission of Greenhouse Gases (GHG).

ENERGY CONSUMPTION AND ECONOMIC GROWTH: EVIDENCE FROM POST-COMMUNIST COUNTRIES

Current study aimed to explore the nexus between energy and economic growth in post-communist nations during 1995-2014. As an empirical model, the growth model is employed including economic complexity indicator as a measure of capabilities for exporting sophisticated products. We employ Pedroni's (1999) panel cointegration tests and Panel Dynamic OLS estimation to assess the long-term link between the variables. At 1% significance level, we confirm cointegration between energy consumption and economic growth. Panel Dynamic OLS results revealed that economic growth positively influences energy consumption in Post-Communist states. The Dumotrescu and Hurlin (2012) panel Granger causality test results discovered unidirectional causality running from economic growth to energy consumption. Economic growth causes energy consumption in post-communistic countries, confirming the conservation hypothesis.Keywords: energy, growth, Post-CommunistJEL Classifications: C23, O47, Q40DOI: https://doi.org/10.32479/ijeep.10003

Determinants of savings among non-Ricardian households in South Africa

PurposeThis paper examines the determinants of savings among low-income households, regarded as non-Ricardian households (NRHs), in South Africa. NRHs comprise low-income households largely depending on government welfare benefits for sustenance. This research investigates socio-economic factors determining savings pattern of low-income households in South Africa.Design/methodology/approachThe research makes use of the National Income Dynamics Study (NIDS) data set wave one to five. The longitudinal survey models are analysed in determining the socio-economic characteristics of NRH in South Africa. The estimators include Pooled ordinary least square (OLS), fixed and random effects methods.FindingsThe household grant contributes positively to the level of savings, but the savings level is still considerably low: majority of the low-income households have zero or negative savings. The average size of a NRH is about twice the size of the Ricardian, despite the NRHs’ debt burden impoverishing them.Research limitations/implicationsThe self-perpetuated poverty problem makes every factor in the vicious cycle both cause and effect of another factor, warranting reverse causality and threatening the reliability of Pooled OLS estimates for the research.Practical implicationsThe growing cost of government grant hinges on the increased level of inflation while largely depending on the number of households entering the low-income threshold.Social implicationsThe study recommends that the government creates a more enabling environment for NRHs to engage in productive activities. Also they create more low-skilled jobs and encourage reduction of birth rate among low-income households; this will reduce their expenditure and increase their level of savings and will assist in pulling them out of the vicious circle of poverty. Government can boost NRHs’ savings through increase in various grants.Originality/valueThe study makes significant contribution towards addressing the unfortunate situation of household savings among low-income brackets in South Africa. The research corroborates other studies on the effectiveness of the fiscal stimulus package to boost the welfare and savings condition of NRHs in South Africa. The result explicitly confirmed the redistribution policy of the grant to the low-income household. The grant has a significant positive effect on the savings pattern of the household. An increase in it beyond the poverty threshold could indeed break the vicious circle of poverty since the effect does not only stop at expenditure but also pass through to savings, which may ultimately boost investment. Further studies should continue the investigation of grant transmission channels to investment and income.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2019-0692

How measurement error affects inference in linear regression

Measurement error biases OLS results. When the measurement error variance in absolute or relative (reliability) form is known, adjustment is simple. We link the (known) estimators for these cases to GMM theory and provide simple derivations of their standard errors. Our focus is on the test statistics. We show monotonic relations between the t-statistics and R^2s of the (infeasible) estimator if there was no measurement error, the inconsistent OLS estimator, and the consistent estimator that corrects for measurement error and show the relation between the t-value and the magnitude of the assumed measurement error variance or reliability. We also discuss how standard errors can be computed when the measurement error variance or reliability is estimated, rather than known, and we indicate how the estimators generalize to the panel data context, where we have to deal with dependency among observations. By way of illustration, we estimate a hedonic wine price function for different values of the reliability of the proxy used for the wine quality variable.

Are exporters more environmentally friendly? A re‐appraisal that uses China’s micro‐data

AbstractIs a firm's ability to export an important determinant of environmental performance? To answer this question, we construct a unique microdata set that merged two rich manufacturing firm‐level data sets for China for 2007. When combining this new data set with well‐received empirical specifications, we found that both export status and export intensity are associated with lower sulphur dioxide (SO2) emissions intensity. In addition to the traditional OLS estimation, we verified this association by using the propensity score matching method. Our findings show that the baseline result still holds. In short, exporters are more environmentally friendly than nonexporters, which is in line with previous evidence reported for developed economies. We further discuss potential mechanisms that explain the observed pattern and show that exporters realise higher abatement efforts compared to nonexporters. This study complements the literature in terms of providing China's microevidence on SO2 abatement efforts. It also serves as a first step towards a better understanding of the impact of trade on the environment, especially in developing countries.

Sensitivity of the Posterior Mean on the Prior Assumptions: An Application of the Ellipsoid Bound Theorem

This study examines the sensitivity of the posterior mean to change in the prior assumptions. Three plausible choices of prior which include informative, relative-non informative and non-informative priors are considered. The paper considers information level for a prior to cause a notable change in the Bayesian posterior point estimate. The study develops a framework for evaluating a bound for a robust posterior point estimate. The Ellipsoid Bound theorem is employed to derive the Ellipsoid Bound for an independent normal gamma prior distribution. The proposed modification ellipsoid bound for the large prior was establised by varrying different variance co-variance sizes for the independent normal gamma prior. This bound represents the range for the posterior mean when is insensitive and when it’s sensitive in both location and spread. The result shows that; for a large prior parameter value (greater than the OLS estimate) with a positive definite prior variance covariance matrix, and prior parameter values interval which contains the OLS estimate then, the posterior estimate will be less than both the OLS and the prior estimates. Similarly, if the lower bound of the prior parameter values range is greater than the OLS estimate then: The posterior estimate will be greater than the OLS estimate but smaller than the prior estimate. Furthermore, it is observed that no matter the degrees of confidence in the prior values, data information is powerful enough to modify it.

Unit root testing with slowly varying trends

A unit root test is proposed for time series with a general nonlinear deterministic trend component. It is shown that asymptotically the pooled OLS estimator of overlapping blocks filters out any trend component that satisfies some Lipschitz condition. Under both fixed‐b and small‐b block asymptotics, the limiting distribution of the t‐statistic for the unit root hypothesis is derived. Nuisance parameter corrections provide heteroskedasticity‐robust tests, and serial correlation is accounted for by pre‐whitening. A Monte Carlo study that considers slowly varying trends yields both good size and improved power results for the proposed tests when compared to conventional unit root tests.

Modeling I(2) Processes Using Vector Autoregressions Where the Lag Length Increases with the Sample Size

In this paper the theory on the estimation of vector autoregressive (VAR) models for I(2) processes is extended to the case of long VAR approximation of more general processes. Hereby the order of the autoregression is allowed to tend to infinity at a certain rate depending on the sample size. We deal with unrestricted OLS estimators (in the model formulated in levels as well as in vector error correction form) as well as with two stage estimation (2SI2) in the vector error correction model (VECM) formulation. Our main results are analogous to the I(1) case: We show that the long VAR approximation leads to consistent estimates of the long and short run dynamics. Furthermore, tests on the autoregressive coefficients follow standard asymptotics. The pseudo likelihood ratio tests on the cointegrating ranks (using the Gaussian likelihood) used in the 2SI2 algorithm show under the null hypothesis the same distributions as in the case of data generating processes following finite order VARs. The same holds true for the asymptotic distribution of the long run dynamics both in the unrestricted VECM estimation and the reduced rank regression in the 2SI2 algorithm. Building on these results we show that if the data is generated by an invertible VARMA process, the VAR approximation can be used in order to derive a consistent initial estimator for subsequent pseudo likelihood optimization in the VARMA model.

Sibship size and educational attainment of Canadian baby boomers

PurposeThe present study assesses how sibship size affects child quality as measured by educational attainment.Design/methodology/approachThe data are from the Canadian General Social Surveys (GSS) of 1986, 1990, 1994 and 1995. The sample is restricted to the individuals born in Canada between 1946 and 1965, that is, the baby-boom generation. In addition to controlling for parental education, the sibship size is instrumented by a non-binary variable created based on the sex composition of the sibship. While most previous studies have pooled both genders, the present paper produces by gender estimatesFindingsThe OLS estimates are statistically significant, negative and moderately large for both male and female baby boomers. When the sibship size is instrumented, the estimates indicate that one additional sibling had reduced the educational attainment of male baby boomers by almost half a year. No causal effect for the sibship size is found for female baby boomers.Originality/valueThis is the first paper on the effects of sibship size on educational attainment, using Canadian data.

Least Quartic Regression Criterion to Evaluate Systematic Risk in the Presence of Co-Skewness and Co-Kurtosis

This article proposes a new method for the estimation of the parameters of a simple linear regression model which is based on the minimization of a quartic loss function. The aim is to extend the traditional methodology, based on the normality assumption, to also take into account higher moments and to provide a measure for situations where the phenomenon is characterized by strong non-Gaussian distribution like outliers, multimodality, skewness and kurtosis. Although the proposed method is very general, along with the description of the methodology, we examine its application to finance. In fact, in this field, the contribution of the co-moments in explaining the return-generating process is of paramount importance when evaluating the systematic risk of an asset within the framework of the Capital Asset Pricing Model. We also illustrate a Monte Carlo test of significance on the estimated slope parameter and an application of the method based on the top 300 market capitalization components of the STOXX® Europe 600. A comparison between the slope coefficients evaluated using the ordinary Least Squares (LS) approach and the new Least Quartic (LQ) technique shows that the perception of market risk exposure is best captured by the proposed estimator during market turmoil, and it seems to anticipate the market risk increase typical of these periods. Moreover, by analyzing the out-of-sample risk-adjusted returns we show that the proposed method outperforms the ordinary LS estimator in terms of the most common performance indices. Finally, a bootstrap analysis suggests that significantly different Sharpe ratios between LS and LQ yields and Value at Risk estimates can be considered more accurate in the LQ framework. This study adds insights into market analysis and helps in identifying more precisely potentially risky assets whose extreme behavior is strongly dependent on market behavior.

Indirect Inference Estimation of Spatial Autoregressions

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

Combining modified ridge-type and principal component regression estimators

The performance of ordinary least squares estimator (OLSE) when there is multicollinearity (MC) in a linear regression model becomes inefficient. The principal components regression and the modified ridge-type estimator have been proposed at a different time to handle the problem of MC. However, in this paper, we developed a new estimator by combining these two estimators and derived the necessary and sufficient condition for its superiority over other competing estimators. Furthermore, we establish the dominance of this new estimator over other estimators through a simulation study, and numerical example in terms of the estimated mean squared error.

Exploring district-level geographical variance of complete immunization coverage in India

Abstract India has been significantly progressed in full immunization care over the last few decades. Existing literature has not been unaddressed the potential spatial variations in relationships between full immunization coverage and its influence on socio-economic factors. The study aims to explore place-specific spatial dependencies and heterogeneities in the relationships of various factors on the district-level full immunization coverage in India. The study used a geocoded database for 640 districts of India, drawn from the 4th wave of the National Family Health Survey (NFHS) in 2015-16. Univariate Moran's I and LISA maps were used to confirm the spatial autocorrelation and geographical hotspots of the district-level full immunization coverage. Multivariate Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were employed to examine spatial relationships and decrypt location-based district-level analysis. The prevalence of full immunization care was 62 percent as per the national figure. The GWR results revealed that the relationships between the outcome and set of cofactors were significantly place-specific and spatially clustering in terms of their respective magnitude, direction, and differences in due to local characteristics across India. In terms of model performance and prediction accuracy, the GWR model was performing better over OLS estimates through comparisons of R2 and Akaike Information Criterion (AICC) in both models. Furthermore, the GWR model also improves the reliabilities of the relationships by reducing spatial autocorrelations. The findings suggest that the local GWR model has the potential to explain complexities in place-specific variations that could be ignored by OLS on the local causes of immunization coverage. Thus, appropriate intervention should be devised to safeguard child from vaccine-preventable diseases reduce the geographical heterogeneity of full immunization coverage across India. Key messages This study aims to explore place-specific spatial dependencies and heterogeneities in the relationships of various factors on the district-level full immunization coverage in India. To explain local complexities in place-specific variations that could be ignored by the traditional model on the local causes of full immunization coverage across India.

Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models

In classical linear regression analysis problems, the ordinary least-squares (OLS) estimation is the popular method to obtain the regression weights, given the essential assumptions are satisfied. However, often, in real-life studies, the response data and its associated explanatory variables do not meet the required conditions, in particular under multicollinearity, and hence results can be misleading. To overcome such problem, this paper introduces a novel generalized difference-based weighted mixed almost unbiased Liu estimator. The performance of this new estimator is evaluated against the classical estimators using the mean squared error. This is followed by an approach to select the Liu parameter and in this context, a non-stochastic weight is also considered. Monte Carlo simulation experiments are executed to assess the performance of the new estimator and subsequently,we illustrate its application to a real-life data example.

Welfare Implications of Solving the Distance Puzzle: Global Evidence from the Last Two Centuries

This paper theoretically shows that changes in the distance elasticity of trade can be connected to welfare changes that depend on bilateral distance measures and expenditure shares of countries. Empirical results based on international and domestic trade data from the last two centuries show that the negative effects of distance on trade have increased over time when zero trade observations are ignored in inconsistent OLS estimations, confirming the distance puzzle in the literature. The corresponding welfare implications suggest that the world economy has experienced a cumulative welfare loss (about 81%) due to this puzzle in the last two centuries. When the puzzle is solved by considering zero trade observations in PPML estimations, the tables turn such that there are significant welfare gains from trade (about 58%) during the same period due to the decreasing negative effects of distance on trade over time. Welfare gains from further reductions in the negative effects of distance are investigated as well, suggesting significant potential gains from trade in the future.

Kernel estimation of returns of retirement funds of employers based on monetary earnings (subscriptions and compensation) via regression discontinuity in Iraq

Regression Discontinuity (RD) means a study that exposes a definite group to the effect of a treatment. The uniqueness of this design lies in classifying the study population into two groups based on a specific threshold limit or regression point, and this point is determined in advance according to the terms of the study and its requirements. Thus , thinking was focused on finding a solution to the issue of workers retirement and trying to propose a scenario to attract the idea of granting an end-of-service reward to fill the gap ( discontinuity point) if it had not been granted. The regression discontinuity method has been used to study and to estimate the effect of the end -service reward on the cutoff of insured workers as well as the increase in revenues resulting from that. The research has showed that this reward has a clear effect on increasing revenues due to the regularity of workers in their work and their work continuity . It has also found that using Local Linear Smother (LLS) by using three models of bandwidth selection. Its results after the analysis in the Regression program have been as follows: The CCT (Calonico, Cattaneo & Titiunik) beamwidth gives the best performance followed by the local linear regression using the LK (Lembens and kalyanman) beamwidth. The real data has been used in sample size 71 represented in compensation as a variable of effectiveness (illustrative) X and the revenue as a result or an approved variable Y, while the results of the traditional OLS estimation method have not been good enough.

Bias testing, bias correction, and confounder selection using an instrumental variable model.

Instrumental variable (IV) analysis can be used to address bias due to unobserved confounding when estimating the causal effect of a treatment on an outcome of interest. However, if a proposed IV is correlated with unmeasured confounders and/or weakly correlated with the treatment, the standard IV estimator may be more biased than an ordinary least squares (OLS) estimator. Several methods have been proposed that compare the bias of the IV and OLS estimators relying on the belief that measured covariates can be used as proxies for the unmeasured confounder. Despite these developments, there is lack of discussion about approaches that can be used to formally test whether the IV estimator may be less biased than the OLS estimator. Thus, we have developed a testing framework to compare the bias and a criterion to select informative measured covariates for bias comparison and regression adjustment. We also have developed a bias-correction method, which allows one to use an invalid IV to correct the bias of the OLS or IV estimator. Numerical studies demonstrate that the proposed methods perform well with realistic sample sizes.