OLS Estimates Research Articles

Does heat cause homicides? A meta-analysis

Several studies provide evidence that heat is positively associated with criminal activity. However, the empirical literature does not provide conclusive evidence about the effect of high temperature on homicides. I examine 156 estimates from 20 studies on the relationship between temperature and homicide rates. In particular, in this meta-analysis I study publication bias using linear and nonlinear techniques together with Bayesian model averaging to explain the heterogeneity in the estimates. After correcting estimates from the publication bias, I cannot conclude that there is a significant effect of temperature on homicide rates. Moreover, monthly data produce larger estimates. Conversely, studies using data from Asia or the OLS estimation method lead to smaller estimates.

Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects

Abstract This study proposes an econometric framework to interpret and empirically decompose the difference between instrumental variables (IV) and ordinary least squares (OLS) estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects that arises from endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS gap.

Impact of Macroeconomic Variables on Exchange Rate in South Africa

Fluctuation in exchange rate has continued to be a major treat to economic growth and maintaining its stability and competitiveness poses serious concern to government and all stakeholders. Despite series of policies and programmes undertaken by governments of various countries, exchange rate instability remains a menace. This paper evaluated the impact of some macroeconomics variables on South Africa’s Rand using annual time series data from 1970 to 2021. Multiple regression with OLS estimator was adopted for the data analysis and the results showed that exchange rate has positive relationship with interest rate while it has negative relationship with gross domestic product growth rate and net foreign trade. Although these individual relationships are not significant enough to influence the exchange rate but there is evidence the combine effect of these macroeconomic variables has significant influence on exchange rate in South Africa.

The impact of energy prices on inflation and economic growth in Mozambique: A wavelet approach and OLS estimator

AbstractThe paper is using the Continuous Wavelet Transform (CWT) and the OLS estimator to examine the relationship between energy prices (coal and gas), inflation and economic growth in Mozambique. Coal and gas are the primary commodity and source of revenue for the country. The research uses quarterly data from 2001 to 2022. Among other results, we found evidence of high coherence between macroeconomic variables (inflation and real GDP growth rate) and energy prices (thermal coal and natural gas prices) only for the frequency intervals between 1.5 and 4 years (short term). The results hold even after controlling for the real GDP growth and exchange rate; we concluded that only in the short term (1.5–4 years), there is high coherence between energy prices and inflation in the time interval between 2004 and 2012. This finding indicates no relation between energy prices, inflation and economic growth in Mozambique. Furthermore, the OLS estimates substantiate the wavelet result, showing no impact. In this context, we can conclude that there is probably a ‘Dutch disease’ or ‘resource curse’ that negates the positive effects of exploiting these resources on short‐ and long‐term economic growth.

Stein-Like Shrinkage Estimators for Coefficients of a Single-Equation in Simultaneous Equation Systems

Two stein-like shrinkage estimators are introduced to modify the 2SLS and the LIML estimators for coefficients of a single equation in a simultaneous system of equations. The proposed estimators are weighted averages of the 2SLS/LIML estimators and the OLS estimator. The shrinkage weight depends on the Wu-Hausman misspecification test statistic which evaluates the null of exogeneity against the alternative hypothesis of endogeneity. The approximate finite sample bias, mean squared errors, and density functions of the Stein-like shrinkage estimators are obtained using small-disturbance approximations. The dominance conditions of the Stein-like shrinkage estimators over the 2SLS/LIML estimator under the mean squared error and the concentration probability are obtained. The proposed method is further illustrated by simulation studies which demonstrate the good finite sample performance of the method, and is also applied to an empirical application of returns to education.

Ordinary least squares and instrumental-variables estimators for any outcome and heterogeneity

Given an exogenous treatment d and covariates x, an ordinary least-squares (OLS) estimator is often applied with a noncontinuous outcome y to find the effect of d, despite the fact that the OLS linear model is invalid. Also, when d is endogenous with an instrument z, an instrumental-variables estimator (IVE) is often applied, again despite the invalid linear model. Furthermore, the treatment effect is likely to be heterogeneous, say, µ1(x), not a constant as assumed in most linear models. Given these problems, the question is then what kind of effect the OLS and IVE actually estimate. Under some restrictive conditions such as a “saturated model”, the estimated effect is known to be a weighted average, say, E{ ω(x) µ1(x)}, but in general, OLS and the IVE applied to linear models with a noncontinuous outcome or heterogeneous effect fail to yield a weighted average of heterogeneous treatment effects. Recently, however, it has been found that E{ ω(x) µ1(x)} can be estimated by OLS and the IVE without those restrictive conditions if the “propensity-score residual” d − E( d| x) or the “instrument-score residual” z−E( z| x) is used. In this article, we review this recent development and provide a command for OLS and the IVE with the propensity- and instrument-score residuals, which are applicable to any outcome and any heterogeneous effect.

Resource dependence and enterprise ESG performance: an empirical study based on A-share listed companies

The impact of resource dependence on social economy and environment lacks empirical evidence at the micro level. This article uses data from A-share listed companies from 2011 to 2020 to construct an econometric model to empirically test the impact of resource dependence on ESG performance of enterprises. We find that the corporate ESG scores in regions with high resource dependence will decline. After a series of robustness tests such as replacing the dependent variable, controlling province time fixed effect, eliminating extreme effects, and eliminate provinces with high resource dependence, the conclusion of this article still holds. In addition, we alleviate the endogeneity problem caused by OLS estimation by constructing a dynamic panel model. Further analysis indicates that there are differences in the effect of resource dependence on enterprises sub-scores, with a significant negative impact on the environmental dimension and social dimension, and no significant impact on the governance dimension. It has a greater impact on the ESG score of SOEs and has no significant impact on non-SOEs. The empirical results of this paper enrich the research on the influencing factors of enterprise ESG performance, and further expand the research framework of the socio-economic consequences of enterprise resource dependence.

Does More Education Always Mean Higher Earnings? Evidence from Bangladesh

Essentially championed by the human capital theory, it is a widely accepted notion that the higher an individuals’ education level, the more likely they are to earn. However, is this true in the case of different groups of individuals across the type of education pursued, gender, and whether an individual grew up in a rural or urban area? This paper explores this question and tests whether there is a significant increase in earnings for every additional education level in the case of six particular groups of individuals: Individuals who studied in the general education stream, individuals who studied in the religious education stream, males, females, those who grew up in urban areas and those who grew up in rural areas. Using nationally representative data and ordinary least-squares estimation, this paper finds that individuals who studied in the religious stream and, in some cases, females, do not necessarily experience increased labor market earnings with an increase in their education level. Speculating on possible reasons behind these findings, I argue for criticalities and nuances to be better considered when assessing the change in earnings across education in a particular context.

The Student Evaluation of Teaching Premium for Clinical Faculty in Economics

This article uses student evaluation of teaching (SET) data for 947 faculty members affiliated with 90 U.S. colleges and universities to study the presence of a teaching quality rating premium for clinical economics faculty relative to traditional tenure-track economics faculty. Based on OLS estimation, we find this difference ranges between 3.9% and 4.8% and is robust to different econometric model specifications. Moreover, the average treatment effect from a propensity score weighting approach suggests that the difference ranges between 5.8% and 6.1%. Lastly, our analysis produces an institutional ranking of economics departments based on department-level SETs. Overall, our findings are encouraging signs for the hiring and retention of clinical faculty in economics departments.

African exchange rates amidst the COVID-19 pandemic

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The study investigates the impact of COVID-19 on African exchange rates, employing a panel dataset over the period 2016-2022 from 13 African economies and employing pooled OLS estimator. The findings reveal a noteworthy positive correlation between COVID-19 levels and exchange rates within the region. However, this association necessitates deeper exploration due to its multifaceted implications, including economic disruptions, government interventions, global market interconnections, regional stability perceptions, and investor behavior. Economic indicators, such as GDP, inflation rate, real interest rate, and unemployment rate, significantly influence exchange rates. While GDP positively impacts exchange rates through economic growth, investor confidence, and trade strength, controlled inflation fosters investor confidence and export competitiveness, positively affecting exchange rates. Moreover, a pronounced positive impact of the real interest rate indicates economic stability, attracting investment and contributing to currency appreciation. Conversely, high unemployment negatively affects exchange rates, signaling economic weaknesses and eroding investor confidence. These findings emphasize nuanced policy responses for managing health crisis-induced economic disruptions and suggest future research directions focusing on specific country-level analyses and multifaceted factors influencing currency valuations within African economies.</span></p>

Equivalent analysis of different estimations under a multivariate general linear model

<p>This article explores the mathematical and statistical performances and connections of the two well-known ordinary least-squares estimators (OLSEs) and best linear unbiased estimators (BLUEs) of unknown parameter matrices in the context of a multivariate general linear model (MGLM) for regression, both of which are defined under two different optimality criteria. Tian and Zhang <sup>[<xref ref-type="bibr" rid="b38">38</xref>]</sup> once collected a series of existing and novel identifying conditions for OLSEs to be BLUEs under general linear models: <italic>On connections among OLSEs and BLUEs of whole and partial parameters under a general linear model, Stat. Probabil. Lett., 112 (2016), 105–112</italic>. In this paper, we show how to extend this kind of results to multivariate general linear models. We shall give a direct algebraic procedure to derive explicit formulas for calculating the OLSEs and BLUEs of parameter spaces in a given MGLM, discuss the relationships between OLSEs and BLUEs of parameter matrices in the MGLM, establish many algebraic equalities related to the equivalence of OLSEs and BLUEs, and give various intrinsic statistical interpretations about the equivalence of OLSEs and BLUEs of parameter matrices in a given MGLM using some matrix analysis tools concerning ranks, ranges, and generalized inverses of matrices.</p>

An Almost Unbiased Regression Estimator: Theoretical Comparison and Numerical Comparison in Portland Cement Data

Multicollinearity is the problem when there is linear dependency among the independent variables. The Ordinary least squares estimator (OLSE) that is commonly adopted is not suitable for the linear regression model when the independent variables are correlated. This is due to the high variance in OLSE and hence the accuracy of OLSE reduces in the presence of multicollinearity. Hence, the estimator named k-almost unbiased regression estimator (KAURE) was proposed as an alternative to OLSE in this paper. KAURE was developed by using the definition of an almost unbiased estimator to further reduce the bias of Liu-type estimator-special case (LTESC). The properties of KAURE including bias, variance-covariance and mean squared error (MSE) were derived. Theoretical comparison and real-life data comparison were carried out to evaluate the performance of the KAURE based on the MSE criterion. The application of the real-life data supported the theoretical comparison that showed the superiority of KAURE over OLSE and LTESC. The results revealed that KAURE could be considered as an alternative estimator for the linear regression model to combat the problem of multicollinearity.

Investigating the link between green finance, environmental orientation, and carbon neutrality: A panel study of the metal extraction sector

In the natural resources exploitation sector, metal industry is considered as one of the major carbon emitters. In this context, this study aims to explore the role of green finance and environmental orientation in improving the carbon neutrality of the firms operating in this sector. The data was collected from two large economies' metal sectors: the People's Republic of China (PRC) and India. Five years of data from 2015 to 2019 was collected from 98 firms from financial statements and other sources. Given the nature of the data, panel cointegration was employed to analyze the developed model. Keeping in view the findings of these three diagnostic tests, it was clear that our model did not fully meet the assumptions to apply a normal panel. It led us to apply a panel-corrected standard error (PCSE) approach, which accounts for the violations of assumptions (autocorrelations, heteroscedasticity, and cross-sectional dependence) as identified in the results of diagnostic tests. The PCSE approach the can preserve the OLS parameter estimates while adjusting the standard errors (SE) to counter the issues found in OLS estimation. These results explain that the provision of green finance can help improve the carbon neutrality of firms in the metal sector of the PRC. Further, the results on the impact of environmental orientation on carbon neutrality are also significant, showing environmental orientation's instrumental role in improving carbon neutrality. Further, the interaction role of environmental orientation in the association between green finance and carbon neutrality is tested creating an interaction term by multiplying both variables. The results show a statistically significant role of environmental orientation in the association between green finance and carbon neutrality. These findings coefficient that, in the case of the PRC, the joint effect of green finance and environmental orientation is statistically significant. The results on the impact of environmental orientation are also significant, confirming its instrumental role in improving carbon neutrality. In short, our findings, suggest that by embracing such an environmental orientation, organizations can position themselves as leaders in carbon neutrality and contribute to a more sustainable future.

Monetary union in Southeast Asia: An assessment of the optimum currency area theory*

AbstractThis paper applies optimum currency area (OCA) theory to Southeast Asia to assess the potential for a monetary union in that region. We construct a panel of 10 Southeast Asian countries over the period 1990–2018 and apply the dynamic OLS estimator to quantify the costs and benefits of a potential OCA. A possible monetary union in Southeast Asia is vulnerable to asymmetric shocks, although the degree of specialisation has decreased and the openness index increased. Estimates confirm that both OCA criteria favour an increase in the region's income and FDI inflows and a reduction of its unemployment rate. However, inadequate labour mobility and stagnating intra‐trade shares are major obstacles in this regard, but if addressed they could benefit economic development and GDP per capita in the ASEAN region. We conclude that Southeast Asia is far from being an optimum currency area, but if it were to form a common currency area, the loss of economic sovereignty and its costs would be more than offset by the resulting monetary efficiency gain.

A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market

A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market

Relationship Between Export and Economic Growth: Evidence from West African Countries

The export-led growth hypothesis (ELGH) postulates that export is a major driver of economic growth. This study tests this hypothesis and further analyses the determinants of exports in the case of West African countries. An annual panel data spanning from 2008 to 2018 was used. Findings from the system GMM and OLS estimations validate the ELGH in West Africa. The results also reveal that foreign direct investment, employment, remittances, land area and infrastructure are significant boosters of export while population, real effective exchange rate and taxes on international trade are detrimental to export performance in the region. The study recommends the relaxation of taxes especially on international trade to encourage businesses that produce to feed the export sectors, provide an enabling environment for businesses and also attract foreign investors. JEL Codes: F14, F21, O55

Unbiased Proxy Calibration

The linear calibration model is a powerful statistical tool that can be utilized to predict an unknown response variable, Y, through observations of a proxy or predictor variable, X. Since calibration involves estimation of regression model parameters on the basis of a limited amount of noisy data, an unbiased calibration slope estimation is of utmost importance. This can be achieved by means of state-of-the-art, data-driven statistical techniques. The present paper shows that weighted least-squares for both variables estimation (WLSXY) is able to deliver unbiased slope estimations under heteroscedasticity. In the case of homoscedasticity, besides WLSXY, ordinary least-squares (OLS) estimation with bias correction (OLSBC) also performs well. For achieving unbiasedness, it is further necessary to take the correct regression direction (i.e., of Y on X) into account. The present paper introduces a pairwise moving block bootstrap resampling approach for obtaining accurate estimation confidence intervals (CIs) under real-world climate conditions (i.e., non-Gaussian distributional shapes and autocorrelations in the noise components). A Monte Carlo simulation experiment confirms the feasibility and validity of this approach. The parameter estimates and bootstrap replications serve to predict the response with CIs. The methodological approach to unbiased calibration is illustrated for a paired time series dataset of sea-surface temperature and coral oxygen isotopic composition. Fortran software with implementation of OLSBC and WLSXY accompanies this paper.

OLS-Based Research on the Impact of Green Finance on Green Development——Taking Beijing as an Example

The Green Credit Policy (GCP) and the Green Finance have brought tremendous influence to the promotion of industry. Green development is an approach to economic growth that considers both economic benefits and environmental protection, but because it requires significant investment to build a green industry, the recovery process is time-consuming and fraught with risk, so there is insufficient incentive for urban green development. Through specialized financing services for environmental protection firms and high-polluting companies, green finance may support the green transformation of high-polluting enterprises. This paper uses OLS estimation to evaluate the Green Development effect of GCP. Green development is significantly impacted by GCP. And Green Financial Level(gfl)’s index has a strong positive covariance with Green Development Level(gdl). Based on these results, we suggest that: The Chinese Government should promote GCP and make further efforts to perfect our industrial structure. Nations, including China, are actively promoting green finance to foster sustainable economic growth and protect the environment. China's efforts in developing green finance are crucial for realizing its ecological civilization. Green finance is a key aspect of promoting sustainable development. It is hoped that the Chinese government will offer stronger support in this field by establishing effective systems and regulations. Encouraging more financial institutions to participate in green finance will ultimately lead to sustainable economic and environmental progress.

THE BEST LINEAR UNBIASED ESTIMATORS OF REGRESSION COEFFICIENTS IN A MULTIVARIATE GROWTH-CURVE MODEL. A COORDINATE-FREE APPROACH

The problems of the existence of the best linear unbiased estimators (BLUE) and of their equality to the ordinary least squares estimators (OLSE) of the expected value of the observations are treated in a coordinate-free approach using a multivariate growth-curve model. It will be proved that the alternative forms of the necessary and sufficient conditions used in solving these problems are independent of the between-individuals design matrix of the model.

The Significance of Corporate Governance: Insights from the Banking Industry in Kosovo

This research investigates the influence of corporate governance (CG) practices and specific financial indicators on the performance of Kosovo's banking sector from 2015 to 2022. The study uses secondary data from nine commercial banks and employs the Ordinary Least Squares (OLS) estimation method to assess their impact on Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM). The findings highlight the significant impact of these variables on the banking sector's performance, with varying degrees of influence depending on the performance metric. Notably, NIM emerges as the most effective metric for evaluating a bank's overall performance, emphasizing the importance of managing interest margins effectively in the Kosovo market. These results have broader implications for discussions on governance structures within financial institutions and policy formulation. In Kosovo's banking sector, corporate governance has become pivotal for operational success and sustainability. This research offers valuable insights for stakeholders and policymakers by analyzing CG practices and their effects on key financial performance metrics.The dataset spans from 2015 to 2022, ensuring a comprehensive assessment of trends and developments within the banking sector. By employing the OLS estimation method, this study sheds light on the intricate relationship between corporate governance practices, financial indicators, and the sector's overall performance. In summary, this research comprehensively explores the interplay between corporate governance practices, financial indicators, and Kosovo's banking sector performance. The identification of NIM as a key performance metric underscores the importance of effective interest margin management. These findings have far-reaching implications, prompting critical discussions on governance structures within Kosovo's financial institutions for policymakers and industry leaders.