Endogenous Regressors Research Articles

Fintech, regtech, and financial development: evidence from China

This study investigates the influence of fintech on developments in China’s financial sector across 290 cities and 31 provinces between 2011 and 2018. Using a two-stage least squares instrumental variable regression approach and correcting for cross-sectional dependency, simultaneity, and endogeneity of regressors, the results establish a positive link between fintech and financial development. Our findings show that fintech supports financial sector development by enhancing access (loans), depth (deposits), and savings within China’s financial institutions. We also show that the emergence of fintech in the area of financial regulation (regulatory technology: regtech) can significantly improve financial development outcomes. Therefore, it is imperative for regulators to pursue policies that balance growth in the fintech sector while mitigating the associated risks. In addition, we use the difference-in-differences approach to show that policy measures such as interest rates liberalization also positively impacted financial development during the analysis period. In our conclusion, we suggest a policy framework for balanced fintech sector growth in developing countries.

Green growth & sustainability transition through information. Are the greener better informed? Evidence from European SMEs

The European Green Deal along with directives promoting Circular Economy support sustainability transition and foster green growth through developing appropriate funding. However, information on how to access such funding affects firms' decision to expand their business strategy. This paper investigates the effect of information about financing tools on the adoption of Circular Economy business activities by exploring whether the better-informed firms are ‘greener’ and what influences such decision through a switching endogenous regressor model to account for endogeneity and selectivity bias. Data on European SMEs is combined with country-specific characteristics and econometric results indicate that better informed firms are by 65 percentage points more likely to adopt an activity promoting Circular Economy, highlighting that awareness about funding tools is crucial for sustainability transition. Evidence advocates for mainstreaming information regarding funding sources to pave the way towards green growth. A rebound effect regarding the use of renewables is observed whilst evidence points towards the rejection of Porter Hypothesis. Policy makers should target in fostering a greener business environment for the firms that engage in Circular Economy practices through increased information on funding options. Findings are also pertinent to the ongoing discussion and policy agenda around acceleration of the transition to a greener European Economy.

Instrument-free inference under confined regressor endogeneity and mild regularity

The instrument-free approach adopts flexible bounds on the correlation between regressors and disturbances, instead of exploiting instruments presupposing their asymptotic uncorrelatedness with the model errors. Earlier findings on such instrument-free inference methods assumed the observations to be mesokurtic and independent and identically distributed. Adopting substantially weaker regularity, this alternative to Two-Stage Least-Squares (TSLS) is developed and simulated for general linear regression models, permitting time-dependent regressors with heterogeneous excess kurtosis. Replicating three prominent empirical studies TSLS is shown to be based on untenable exclusion restrictions, whereas instrument-free inference can arguably be more credible, while potentially producing narrower confidence intervals than (weak-instrument robust) TSLS.

A Robust Test for Weak Instruments with Multiple Endogenous Regressors

A Robust Test for Weak Instruments with Multiple Endogenous Regressors

How funding matters: Reinitiating of New Product Development and the moderating effect of extramural R&D

PURPOSE: We examine whether financial obstacles affect the probability of a firm undertaking previously suspended (or abandoned) innovation projects for new product development (NPD), and whether extramural R&D, as well as the breadth of the types of R&D collaboration (i.e., knowledge sources), moderate the relationship between financing constraints and the probability of restarting previously suspended NPD from selected South Asian economies. METHODOLOGY: This study controls for potential endogeneity in innovation propensity and finance access by employing a recursive bivariate probit model. We also adopt an instrumental variable approach by employing a probit model with continuous endogenous regressor to account for the potential endogeneity between the breadth of collaboration partners and innovation propensity. FINDINGS: Financial obstacles significantly impact previously suspended NPD. Furthermore, extramural R&D positively affects the probability of a firm undertaking NPD projects and attenuates the relationship between financing constraints and the likelihood of restarting abandoned/suspended NPD projects, suggesting that extramural R&D alleviates financing constraints, which increases the likelihood of NPD restarts. However, the breadth of collaborating partners is not positively associated with the probability of a firm restarting NPD. This is consistent with the view that extramural R&D with diverse sets of partners is exposed to the risks of the “two worlds paradox” arising from a firm’s collaboration with universities, research institutions, and consulting firms. IMPLICATIONS: The findings corroborate the view that firms must maintain a balance between their internal knowledge base and extramural R&D to optimize innovation outcomes. Nevertheless, extramural R&D reduces the reliance of financially constrained firms on resource requirements, improves access to financing, and enhances R&D productivity in NPD. ORIGINALITY AND VALUE: We provide the first firm-level and multi-country evidence of the importance of financial obstacles in the probability of reinitiating previously suspended NPD at the execution phase. Second, to the best of our knowledge, this is the first study to examine the relationship between inter-organizational R&D collaboration diversity and the probability of a firm reinitiating previously abandoned (or suspended) NPD.

Remittances and the future of African economies

AbstractAfrican nations have, with the passage of time, over‐relied on remittance inflow to augment domestic savings for growth and development. Although remittance—both as a viable investment financing option and as a growth complementary factor—helps drive growth and development, concerns of fading altruism exist. As regards altruistic giving, decreasing external financing options might overwhelm growth and development. We argue that the altruistic connection, which has been the bedrock of sending remittances to African countries, could eventually fade into oblivion. We assigned numerical weights to establish the influence of remittances on the future of African economies. We took cognizance of endogeneity of regressors and accounted for cross‐sectional dependence. We found a positive relationship between remittances and financial development besides the influence of exchange rate, technological change, inflation and population on the latter. African governments, donors, investment partners and the society at large should be concerned about fading altruistic connections from old and young African migrants. Altruism could be a springboard to the diversification of external capital sourcing and creation of a migrant policy that puts Africa at the forefront of development ahead of seeking increased worker remittances from abroad.

GMM estimation of a spatial autoregressive model with autoregressive disturbances and endogenous regressors

This paper considers the generalized method of moments (GMM) estimation of a spatial autoregressive (SAR) model with SAR disturbances, where we allow for endogenous regressors in addition to a spatial lag of the dependent variable. We do not assume any reduced form of the endogenous regressors, thus we allow for spatial dependence and heterogeneity in endogenous regressors, and allow for nonlinear relations between endogenous regressors and their instruments. Innovations in the model can be homoscedastic or heteroskedastic with unknown forms. We prove that GMM estimators with linear and quadratic moments are consistent and asymptotically normal. In the homoscedastic case, we derive the best linear and quadratic moments that can generate an optimal GMM estimator with the minimum asymptotic variance.

A wavelet method for panel models with jump discontinuities in the parameters

While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Our method adapts Haar wavelets to the structure of the observed variables in order to detect the change points of the parameters consistently. We also develop methods to address endogenous regressors within our modeling framework. The asymptotic property of our estimator is established. In our application, we examine the impact of algorithmic trading on standard measures of market quality such as liquidity and volatility over a time period that covers the financial meltdown that began in 2007. We are able to detect jumps in regression slope parameters automatically without using ad-hoc subsample selection criteria.

A simple test for stability of a spatial model

ABSTRACT This paper suggests a simple test to check if a spatial model’s specifications hold for the entire sample period, that is, to see if the model is stable. Our test is general in that it allows for possible unknown changes, one or more times, in the weighting matrix, in the exogenous variables and/or in the parameters. These changes may occur cross-sectionally or at unknown points in time. The test also allows for additional endogenous regressors, and the error terms are non-parametrically specified. However, if instabilities are detected, our test does not indicate which set of model components is responsible for it. Our Monte Carlo results suggest that the test has extremely high power for almost all the experiments considered. However, the size of the test is low for small sample sizes when additional endogenous regressors are part of the model.

European farmers’ responses to higher commodity prices: Cropland expansion or forestlands preservation?

The interconnection between commodity prices, agricultural expansion and forestland conservation by farmers has received little scholarly attention in existing literature. This paper analyses the influence of European farmers’ responsiveness to changes in agri-commodity prices on how they manage the trade-offs between agricultural expansion and forestland preservation. In so doing, we rely on a comprehensive dataset relating to European agricultural holdings over the period of 2008–2017, covering 128 regions, and use a recursive equation system that links commodity prices to economic output per area, and the latter to forestland privately owned by farmers. Controlling for farm characteristics, geographical spillovers as well as regressor endogeneity, we find results suggesting that there is indeed evidence of such a trade-off. Specifically, with increased prices, European agricultural holdings expand crop production (respectively agricultural land), which reversely conflicts with private forestland. Replicating the analysis on different subsamples confirms the robustness of these findings. Based on these results, we advocate for further incentive towards the preservation of forests privately owned by agricultural holdings.

Individuals' Financial Satisfaction and National Priority: A Global Perspective.

The purpose of this study is to examine the association between financial satisfaction and individuals’ preferences for various national priorities (including economic growth, national defense, freedom of speech, and having a beautiful nation). Using the sixth wave of World Value Survey (2010–2014) data for 60 countries, our Probit regression analyses show a positive and significant relationship between individuals’ financial satisfaction and individuals’ preferences for freedom of speech and having a beautiful nation. We also find that financial satisfaction is negatively associated with the priority of economic growth but not statistically related to the priority of national defense. These findings are robust when we use a Probit model with endogenous regressors and country-level data with a 2SLS estimator. The regression results also show that the preference for a stronger national defense is higher among individuals who are older, married, nationalistic, educated and have jobs in the public sector. Finally, we find that people with lower education and income and those with full-time employment and non-government jobs are more interested in the economic growth of their countries.

A control function approach to estimate panel data binary response model

We propose a new control function (CF) method to estimate a binary response model in a triangular system with multiple unobserved heterogeneities The CFs are the expected values of the heterogeneity terms in the reduced form equations conditional on the histories of the endogenous and the exogenous variables. The method requires weaker restrictions compared to CF methods with similar imposed structures. If the support of endogenous regressors is large, average partial effects are point-identified even when instruments are discrete. Bounds are provided when the support assumption is violated. An application and Monte Carlo experiments compare several alternative methods with ours.

An augmented Anderson–Hsiao estimator for dynamic short-T panels†

This article introduces the idea of self-instrumenting endogenous regressors in settings when the correlation between these regressors and the errors can be derived and used to bias-correct the moment conditions. The resulting bias-corrected moment conditions are less likely to be subject to the weak instrument problem and can be used on their own or in conjunction with other available moment conditions to obtain more efficient estimators. This approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. This article focuses on the latter, and proposes a new estimator for short T dynamic panels by augmenting Anderson and Hsiao (AAH) estimator with bias-corrected quadratic moment conditions in first differences which substantially improve the small sample performance of the AH estimator without sacrificing the generality of its underlying assumptions regarding the fixed effects, initial values, and heteroskedasticity of error terms. Using Monte-Carlo experiments it is shown that AAH estimator represents a substantial improvement over the AH estimator and more importantly it performs well even when compared to Arellano and Bond and Blundell and Bond (BB) estimators that are based on more restrictive assumptions, and continues to have satisfactory performance in cases where the standard GMM estimators are inconsistent. Finally, to decide between AAH and BB estimators we also propose a Hausman type test which is shown to work well when T is small and n sufficiently large.

Kinkyreg: Instrument-free inference for linear regression models with endogenous regressors

In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg command for kinky least-squares inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least-squares confidence bands can be more informative than confidence intervals obtained from instrumental-variables estimation, especially when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental-variables inference. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.

Why do Spanish households separate their e-waste for proper disposal? An econometric analysis

Improving e-waste separate collection rates is of the utmost importance to achieve the environmental targets set in the Sustainable Development Goals. Therefore, this paper aims to identify the factors influencing the intentions of Spanish households to separate their e-waste for proper disposal. To this end, we carry out an econometric analysis revealing that the preoccupation with environmental issues is an endogenous regressor, so a bivariate model is required to avoid inconsistent results. The analysis shows that environmental concern and the origin of the individuals are relevant factors that influence the e-waste separation decision. Additionally, we observe an interaction between age and city size, so the effect of one of these factors on the probability of separating e-waste depends on the other factor. Another important result is that several socio-economic variables and knowledge of environmental issues only indirectly affect attitudes, by way of environmental concern. In summary, this study offers a useful methodology to policymakers who have to deal with e-waste management, allowing them to identify the priority groups on which should be focused, as well as to design specific measures tailored to their characteristics.

Control variables approach to estimate semiparametric models of mismeasured endogenous regressors with an application to U.K. twin data

We study the identification and estimation of semiparametric models with mismeasured endogenous regressors using control variables that ensure the conditional covariance restriction on endogenous regressors and unobserved causes. We provide a set of sufficient conditions for identification, which control for both endogeneity and measurement error. We propose a sieve-based estimator and derive its asymptotic properties. Given the sieve approximation, our proposed estimator is easy to implement as weighted least squares. Monte Carlo simulations illustrate that our proposed estimator performs well in the finite samples. In an empirical application, we estimate the return to education on earnings using U.K. twin data, in which self-reported education is potentially measured with error and is also correlated with unobserved factors. Our approach utilizes the twin’s reported education as a control variable to obtain consistent estimates. We find that a one-year increase in education leads to an 11% increase in hourly wage. The estimate is significantly higher than those from OLS and IV approaches which are potentially biased. The application underscores that our proposed estimator is useful to correct for both endogeneity and measurement error in estimating returns to education.

Efficient semiparametric copula estimation of regression models with endogeneity

An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.

Models for Value-added Investigations of Teaching Styles Data

This paper considers models of educational data where a value added analysis is required. These models are multilevel in nature and contain endogenous regressors. Multivariate models are considered so as to simulta neously model results from different subject areas. Path models and factor models are considered as types of model that can be used to overcome the problem of endogeneity. Estimation methods available in MLwiN and EQS are used. The use of a factor model with EQS is shown to give estimates of the effects of teaching styles that have smaller standard errors than any other method studied.

Semiparametric estimation of Nelson–Olson simultaneous Tobit model

In this article we study the semiparametric estimation of the Tobit model simultaneously jointed with an endogenous regressor. The model usually is estimated by maximum likelihood method and two-stage estimation under the normality assumption of the error terms. The possible misspecification motivates our semiparametric study. We first construct a semiparametric estimator for the parameters in the reduced form equations and prove its consistency and asymptotical normality. Then the parameters in the structural models are recovered by system minimum distance estimator. The simulation shows that our estimator performs well in different designs in finite samples. Finally, an empirical application is given to show the usefulness of the estimator.

Connectivity and growth: Financial centres in investment banking networks

We investigate the effect of urban network connectivity on the growth of financial centres. While existing research recognises the importance of network connectivity to firms, clusters as well as city regions, large-sample empirical evidence is currently scarce, particularly in the context of financial services. We contribute to this debate by studying underwriting of equity and debt securities, which represent some of the core activities of financial centres. We operationalise our analysis using a proprietary dataset collated from Dealogic Equity Capital Market and Debt Capital Market databases covering over 1.7 million interactions of investment banks with issuers across 540 cities globally during the 1993–2016 period. We estimate our regression equations using the system generalised method of moments estimator, which allows us to obtain consistent coefficient estimates on potentially endogenous regressors, including network connectivity variables. We identify a clear pattern of a positive association between network centrality of financial centres and their growth. We distinguish between intracity and intercity network connectivity and find that financial centres with a larger number of intercity network ties and assortative intracity networks grow faster, while intracity network density does not appear to affect financial centre growth. Our results on intercity network ties are broadly consistent with established knowledge of cluster networks. In contrast, our findings on financial centres' intracity networks contradict previous research that suggests that dense and disassortative intracluster networks aid economic performance of clusters.