OLS Estimates Research Articles

Assess the Economic and Environmental Impacts of the Energy Transition in Selected Asian Economies

Energy transition and green innovation have appeared as new hopes for environmental impact due to human activity, which has destroyed biodiversity and increased environmental degradation. Therefore, developed and emerging economies are focusing on green innovation and energy transition to tackle the environmental impact. Thus, this study was initiated to provoke a meaningful relationship between energy transition, economic growth, trade, green innovation, and good governance to measure the role of concerning factors in achieving environmental sustainability. For this objective, dynamic econometric approaches such as cointegration, heteroskedastic OLS estimation using GMM (HOLS-GMM), AMG, and Driscoll–Kraay were implemented to estimate the Asian dataset between 1990 and 2022. The result indicates that concerning factors have a significant influence on environmental impact. The findings specify that a 1% rise in the energy transition and green innovation will influence the environment by 0.0517% and 3.051%, respectively. Further, AMG and Driscoll–Kraay validate the findings of HOLS-GMM. The robust tests indicate that the factors, which are concerning, significantly impact environmental sustainability. Consequently, the energy transition, trade, and green innovation significantly contribute to attaining ecological sustainability in the long term, and the Sustainable Development Theory prevails in the economy. Thus, innovative policy implications, including energy transition, green innovation, trade, and economic growth, are required to make Asia prominent in achieving environmental sustainability via implementing sustainable and green technologies and clean energy sources.

From rationality to identity: the impact of using community health services for the aged on the well-being of older adults in China.

To better understand the outcome benefits of community health services for the aged (CHSA) and provide bottom-up identity for development, this study examined the impact of using CHSA on well-being of older adults from both objective and subjective dimensions. Data from 1,411 people aged 60+ in Shaanxi, China was used in this study. The OLS estimate was used to analyze the impact of using CHSA on well-being of older adults. Then, the instrumental variable estimate was further hired to examine associations among variables to address the potential endogeneity concerns. The heterogeneity among disabled and non-disabled group was also estimated. For objective well-being, the mental health (β = 0.44) and financial well-being (β = 0.59) of older people using CHSA were significantly higher than those not using any service. The impact on objective well-being were more significant in non-disabled group. For subjective well-being, life meaning (β = 0.54) and security (β = 0.72) were positively associated with CHSA utilization, while independence index (β = -0.33) was negatively related with CHSA utilization. The subjective well-being of disabled group using services increased more than non-disabled group. Findings underscore the positive, albeit group-selective, role of CHSA in improving well-being of older people in China and imply the necessity of high-quality development of CHSA and the targeted differentiation strategy.

Does Longer Tenure Mean Greater Ability? Deep-Rooted Politics and Migration in Rural China

This study examines the political motivations behind changes in labor migration rates in Chinese villages. Using data from the China village survey, we estimate the impact of the tenure gap between the village committee director and party branch secretary on village labor migration rates. We find that within the dual power structure of villages, a longer political tenure for the secretary relative to the director significantly suppresses village labor migration rates, while a shorter tenure significantly promotes them. IV estimation supports the causal interpretation found by OLS estimation. This mechanism of causality is associated with vested interests stemming from ambiguous ownership, having a director or secretary with a longer political tenure tends to bias these interests towards villagers or township authorities. Other accompanying effects of the tenure gap should not be ignored, including the amplification effect of the non-laboring population, the feedback effect of labor, and the shrinkage effect of outsiders. This causal relationship is heterogeneous, the dual role analysis shows that the structure and stance of village leaders will affect the strength of the causal relationship, and the regional heterogeneity analysis shows that economic factors that drive village labor migration may be more important than political factors.

Capping Study Permits: Evaluating the Impact of International Students on Real Rent Growth in Canada

Housing affordability has become a critical policy issue in Canada as shelter costs have dramatically increased. This study examines the impact of international students on the growth of real rents in Canadian census metropolitan areas (CMAs) from 2004 to 2018. Using study permit data as a proxy for international student numbers, we establish a causal link between the influx of international students and real rent growth. To address endogeneity and control for CMA-specific factors, we employ an Ordinary Least Squares (OLS) model and a shift-share instrument. Our OLS estimates suggest that international student growth accounts for 1.15 percent of real rent growth. Further, using a two-stage least-squares (2SLS) model for robustness, we find that a 1 percent increase in per capita study permit growth (“dppr”) leads to a 1.31 percent increase in real rent growth (“grr”). These findings are significant for policy discussions on housing affordability and the potential impact of the 2024 study permit cap.

Addressing Autocorrelation, Multicollinearity, and Heavy-Tail Errors in the Linear Regression Model

The most popular estimator for estimating parameters of linear regression models is the Ordinary Least Squares (OLS) Estimator. The OLS is considered the best linear unbiased estimator when certain assumptions are not violated. However, when autocorrelation, multicollinearity, and heavy-tail error are jointly present in the dataset, the OLS estimator is inefficient and imprecise. In this paper, we developed an estimator of linear regression model parameters that jointly handle multicollinearity, autocorrelation, and heavy tail errors. The new estimator, LADHLKL, was derived by combining the Hildreth-Lu (HL), the Kibria Lukman (KL), and the Least Absolute Deviation (LAD) estimators. The LADHLKL poses both the characteristics of the LAD, HL, and KL estimators which makes it resistant to both problems. We examined the properties of the proposed estimator and compared its performance with other existing estimators in terms of mean square error. An application to real-life data and simulation study revealed that the proposed estimator dominates other estimators in all the considered conditions in terms of mean square error.

Do employee-friendly practices affect inventory leanness?

ABSTRACT We examine the impact of employee-friendly practices (i.e. employment quality, training and development, and employee turnover) on inventory leanness. We use the Empirical Leanness Indicator (ELI) as a proxy for inventory leanness. We employ OLS estimates to estimate our regressions while controlling for country, industry, and year-fixed effects and clustering standard errors at the firm level. We use alternative approaches to address endogeneity issues, such as the instrumental variable and the Heckman two-stage approaches. Our results suggest that employee-friendly practices improve inventory leanness. Our findings suggest that firms should invest in employee-friendly practices. This policy implication is of paramount importance for firms with more employees working in roles involving inventory (as opposed to automation or non-labour methods) and in environments with high dynamism and complexity. We also suggest that reputation mediates the relationship between employee-friendly practices and inventory leanness.

A Practical Guide to Weak Instruments

We survey the weak instrumental variables (IV) literature with the aim of giving simple advice to applied researchers. This literature focuses heavily on the problem of size inflation in two-stage least squares (2SLS) two-tailed t-tests that arises if instruments are weak. A common standard for acceptable instrument strength is a first-stage F of 10, which renders this size inflation modest. However, 2SLS suffers from other important problems that exist at much higher levels of instrument strength. In particular, 2SLS standard errors tend to be artificially small in samples where the 2SLS estimate is close to ordinary least squares (OLS). This power asymmetry means the t-test has inflated power to detect false positive effects when the OLS bias is positive. The Anderson-Rubin (AR) test avoids this problem and should be used in lieu of the t-test even with strong instruments. We illustrate the practical importance of this issue in IV papers published in the American Economic Review from 2011 to 2023. Use of the AR test often reverses t-test results. In particular, IV estimates that are close to OLS and significant according to the t-test are often insignificant according to AR. We also show that for first-stage F in the 10–20 range there is a high probability that OLS estimates will be closer to the truth than 2SLS. Hence we advocate a higher standard of instrument strength in applied work.

Parsing the Performance of Stock Market Indices Amidst COVID-19 and Russia-Ukraine War: A Comparative Study of DSEX, Nifty-50, and KSE-100

The primary objective of this investigation is to assess the performance of the benchmark indices in the stock markets of Bangladesh, India, and Pakistan, denoted by DSEX (Dhaka Stock Exchange Broad Index), Nifty-50 (National Stock Exchange Fifty), and KSE-100 (Karachi Stock Exchange 100 Index), respectively. Data is meticulously gathered across distinct periods, namely the pre-COVID, the COVID-19, and the concurrent COVID-19 era along with the Russia-Ukraine War period, and amid the consequent global ongoing financial crises. The study encompasses information spanning from July 2, 2013 to May 9, 2023, employing descriptive statistics, where both mean returns and risk-adjusted returns were calculated consecutively. The evaluation of diversification among the indices during the specified periods is conducted through descriptive statistics and Ordinary Least Squares (OLS) estimation. The findings of this study indicate that all the indices exhibited negative mean returns during the COVID-19 phase. Furthermore, both mean returns and risk-adjusted returns of the indices witnessed substantial increments amid the inflationary environment concurrent with the COVID-19 and Russia-Ukraine War periods. OLS estimation results suggest an absence of a discernible diversification relationship among the indices. In summary, our results underscore the dynamic nature of index prices across diverse temporal and global crisis scenarios. These implications offer valuable insights for investors and stockholders, enabling them to discern hedging opportunities and identify diversifiable indices within the global stock markets across disparate periods and crises.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

On the estimation of ridge penalty in linear regression: Simulation and application

According to existing literature, the ordinary least squares (OLS) estimators are not the best in presence of multicollinearity. The inability of OLS estimators against multicollinearity has paved the way for the development of various ridge type estimators for circumventing the problem of multicollinearity. In this paper improved two-parameter ridge (ITPR) estimators are proposed. A simulation study is used to evaluate the performance of proposed estimators based on minimum mean squared error (MSE) criterion. The simulative results reveal that, based on minimum MSE, ITPR2 was the most efficient estimator compared to the considered estimators in the study. Finally, a real-life dataset is analyzed to demonstrate the applications of the proposed estimators and also checked their efficacy for mitigation of multicollinearity.

An aggregation-consistent implementation of the Hamilton filter

ABSTRACT I propose a modified implementation of the popular Hamilton filter, to make the cyclical component extracted from an aggregate variable consistent with the aggregation of the cyclical components extracted from its underlying variables. This procedure is helpful in many circumstances, for instance when dealing with a variable that comes from a definition or when the empirical relationship is based on an equilibrium condition of a growth model. The procedure consists of the following steps: 1) build the aggregate variable, 2) run the Hamilton filter regression on the aggregate variable and store the related OLS estimates, 3) use these estimated parameters to predict the trends of all the underlying variables, 4) rescale the constant terms to obtain mean-zero cyclical components that are aggregation-consistent. I consider two applications, exploiting U.S. and Canadian data. The former is based on the GDP expenditure components, while the latter on the GDP of its Provinces and Territories. I find sizable differences between the cyclical components of aggregate GDP computed with and without the adjustment, making it a valuable procedure for both assessing the output gap and validating empirically DSGE models.

Multilevel meta-analysis of single-case experimental designs using robust variance estimation.

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Information and vaccine hesitancy: The role of broadband Internet.

We examine the effect of Internet diffusion on the uptake of an important public health intervention: the measles, mumps and rubella (MMR) vaccine. We study England between 2000 and 2011 when Internet diffusion spread rapidly and there was a high profile medical article (falsely) linking the MMR vaccine to autism. OLS estimates suggest Internet diffusion led to an increase in vaccination rates. This result is reversed after allowing for endogeneity of Internet access. The effect of Internet diffusion is sizable. A one standard deviation increase in Internet penetration led to around a 20% decrease in vaccination rates. Localities characterized by higher proportions of high skilled individuals and lower deprivation levels had a larger response to Internet diffusion. These findings are consistent with higher skilled and less-deprived parents responding faster to false information that the vaccine could lead to autism.

THE EFFECT OF FOREIGN DIRECT INVESTMENT ON THE ECONOMIC DEVELOPMENT OF NIGERIA

The study examined the effect of Foreign Direct Investment (FDI) on development in Nigeria. The ultimate goal of developing countries is to attain development. Most studies on the effect of Foreign Direct Investment on the development of the Nigerian economy used growth proxied by GPP as a measure of development. This study however uses the Human Development Index (HDI) which is a complementary measure of development as it considers the three aspect of development (health, knowledge and standard of living). Current data from secondary data source. were used Secondary data were collected and OLS estimation method was used for the analysis of data. The result shows that the stock of FDI inflow has a positive and statistically significant effect on Economic development in Nigeria. Therefore to experience sustainable development, government should aim at providing infrastructure and conducive trade and investment environment for both foreign and local investors.

Effect of Climate Change on Nigerian Economic Sustainability

Climate change is seen as a statistical variation that persists for an extended period, frequently for as long as a decade or more. Moreover, the issue of climate change which has gained global attention poses a serious threat to developing economy like Nigeria, which is characterized by widespread poverty as a result of economic instability. It is against this backdrop that this research is aimed at investigating the effect of climate change on Nigerian economic sustainability. The research made use of Autoregressive Distributive Lag (ARDL)/bond test approach and OLS estimation technique, while data for the period of 1990-2020 was collected. Changes in average temperature and carbon emission were used to capture climate change, while variation in exchange rate and agricultural production were used as control variables. The result of the analysis showed that the goodness-of-fit (R-Square) is 0.998. This means that 99.8% of the changes in the dependent variable (GDP) can be explained by the changes in the independent variables (CEM, AGRIC, EXR, TEMP). The annual speed of adjustment from short run to long run relationship is 34%. At F-statistic = 195.8052 and P value = 0.000, the model is statistically significant at 1% level. The results of the analysis further demonstrated that both in long-run and short-run, carbon emissions adversely affect Nigerian economic sustainability. Additionally, average atmospheric temperature was significantly related to sustainability of Nigerian economy in the short run. It was concluded that environmental stakeholders as well as Nigerian government should develop and enforce policies to reduce carbon emissions and forest depletion. Also, efforts should be made by government towards ensuring that policies that are environmentally friendly are made that can encourage agricultural production in order to reduce import of agricultural produce, thereby boosting economic growth.

MSE superiority of the unrestricted Stein-rule estimator in a regression model with a possible structural break

This paper investigates the estimation of a linear regression model with a possible structural break at a known point. We analytically derive the exact formulae of the MSE for the restricted SR and PSR estimators, which shrinks the OLS estimator toward the restriction of no structural break. We compare the MSE performance of restricted/unrestricted SR, PSR, and least squared estimators. We analytically show that the unrestricted SR estimator can have a smaller MSE than the restricted SR estimator even when the restriction is correct. Further, our numerical results show that the unrestricted PSR estimator has the best MSE performance over a wide region of parameter space. These results indicate that the use of the unrestricted PSR estimator is recommended even when a structural break may not exist, although the unrestricted PSR estimator does not take the possibility of no structural break into consideration.

Tác động của khủng hoảng tài chính đến đa dạng hóa thu nhập của ngân hàng

Using data of 3,695 commercial banks from 142 countries over the period 2002- 2021, our OLS estimates with multiple fixed effects and two-stage least squares approach provide evidence that banking crisis significantly increases the level of bank income diversification, especially large banks. As the business model of small banks focuses on traditional lending activities, income diversification of small banks is not affected by banking crisis. Currency crisis and sovereign debt crisis are not significant drivers of bank income diversification. Tightening regulations on bank activity restrictions and capital stringency helps to cushion the impact of financial crises on bank income diversification. Our findings provide a comprehensive view of the impact of financial crisis on bank income diversification as well as the roles of some bank regulations in times of financial crisis. Some policy implications are also provided for policymakers to implement appropriate policies that reduce bank risktaking during periods of financial crisis.

The causal effect of genetic variants linked to cognitive and non-cognitive skills on education and labor market outcomes

We estimate the effect of genetic variants that are associated with differences in cognitive and non-cognitive skills on labor market and education outcomes by linking genetic data from individuals in the Swedish Twin Registry to government registry data. Genes are fixed over the life cycle and genetic differences between full siblings are random, making it possible to establish the causal effects of within-family genetic variation. We show that polygenic indices associated with cognitive skills and personality traits significantly affect income, occupation, and educational attainment. By comparing estimates that use only within-family variation to OLS estimates with and without socioeconomic controls, our results also provide indications of the degree of (residual) confounding, which can be useful for research conducted in datasets that do not contain sibling pairs. Overall, our results indicate that education and labor market outcomes are partially the result of a genetic lottery.

The local to unity dynamic Tobit model

This paper considers highly persistent time series that are subject to nonlinearities in the form of censoring or an occasionally binding constraint, such as are regularly encountered in macroeconomics. A tractable candidate model for such series is the dynamic Tobit with a root local to unity. We show that this model generates a process that converges weakly to a non-standard limiting process, that is constrained (regulated) to be positive. Surprisingly, despite the presence of censoring, the OLS estimators of the model parameters are consistent. We show that this allows OLS-based inferences to be drawn on the overall persistence of the process (as measured by the sum of the autoregressive coefficients), and for the null of a unit root to be tested in the presence of censoring. Our simulations illustrate that the conventional ADF test substantially over-rejects when the data is generated by a dynamic Tobit with a unit root, whereas our proposed test is correctly sized. We provide an application of our methods to testing for a unit root in the Swiss franc/euro exchange rate, during a period when this was subject to an occasionally binding lower bound.

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.