Number Of Instrumental Variables Research Articles

Lightening the path to financial development: The power of electricity

AbstractThis paper examines the impact of access to electricity on financial development. In doing so, we use a number of instrumental variables (IV) approaches. Using panel data for 38 countries in Sub‐Saharan Africa over the period 2000–2018, the results suggest that more people having access to electricity can promote financial development. In addition, mobile phone and commercial bank branches diffusion serve as potential channels through which access to electricity affects financial development. Our results are robust to sample‐splitting and different estimation techniques. The results have important implications for policies in overcoming barriers to electricity access.

A Bayesian instrumental variable model for multinomial choice with correlated alternatives

Endogeneity and correlated alternatives are major concerns to be addressed in travel behavior analysis. However, these issues have rarely been dealt with simultaneously in advanced discrete choice models. This study proposes a multinomial probit model that incorporates the instrumental variable method, namely, a fully parametric instrumental variable model for a multinomial choice. The proposed model has the following three characteristics: (1) it allows binary and/or continuous endogenous variables; (2) it allows any number of instrumental variables in each alternative; and (3) it allows positive and/or negative correlations between any choice alternatives. For parameter estimation, we also propose a Bayesian Markov chain Monte Carlo algorithm that can be accommodated in more extended model structures. The simulation study demonstrates that the proposed model addresses endogeneity while allowing correlations between the choice alternatives. Meanwhile, the simulation also implies that the users need to pay attention to the setting of the prior distribution when an endogenous variable of interest is binary, even if the sample size is moderate. The proposed model will be a useful tool in disciplines in which both endogeneity and correlations between choice alternatives are major concerns.

Causal mediation analysis with multiple causally non-ordered and ordered mediators based on summarized genetic data.

Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power.

Mendelian Randomization Analysis Reveals Causal Effects of the Human Gut Microbiota on Abdominal Obesity.

Although recent studies have revealed an association between the composition of the gut microbiota and obesity, whether specific gut microbiota cause obesity has not been determined. The aim of this study is to determine the causal relationship between specific gut microbiota and abdominal obesity. Based on genome-wide association study (GWAS) summary statistics, we performed a 2-sample Mendelian randomization (MR) analysis to evaluate whether the gut microbiota affects abdominal obesity. Gut microbiota GWAS in 1126 twin pairs (age range, 18-89 years; 89% were females) from the TwinsUK study were used as exposure data. The primary outcome tested was trunk fat mass (TFM) GWAS in 492,805 participants (age range, 40-69 years; 54% were females) from the UK Biobank. The gut microbiota were classified at family, genus, and species levels. A feature was defined as a distinct family, genus, or species. MR analysis was mainly performed by an inverse variance-weighted test or Wald ratio test, depending on the number of instrumental variables (IVs) involved. A sensitivity analysis was performed on significant results by a weighted median test and a weighted genetic risk score (GRS) analysis. Results of MR analyses provided evidence of a causal association between 3 microbiota features and TFM, including 1 family [Lachnosiraceae; P=0.02; β = 0.001 (SEE, 4.28×10-4)], 1 genus [Bifidobacterium; P=5.0×10-9; β = -0.08 (SEE, 0.14)], and 1 species [Prausnitzii; P=0.03; β = -0.007 (SEE, 0.003)]. Both the weighted median test and GRS analysis successfully validated the association of the genetically predicted family, Lachnosiraceae (Pweighted median=0.03; PGRS=0.004). Our findings provided evidence of a causal association between gut microbiota and TFM in UK adults and identified specific bacteria taxa that may regulate the fat metabolism, thus offering new direction for the treatment of obesity.

Decreased serum pyridoxal levels in schizophrenia: meta-analysis and Mendelian randomization analysis.

Alterations in one-carbon metabolism have been associated with schizophrenia, and vitamin B6 is one of the key components in this pathway. We first conducted a case-control study of serum pyridoxal levels and schizophrenia in a large Japanese cohort (n = 1276). Subsequently, we conducted a meta-analysis of association studies (n = 2125). Second, we investigated whether rs4654748, which was identified in a genome-wide association study as a vitamin B6-related single nucleotide polymorphism, was genetically implicated in patients with schizophrenia in the Japanese population (n = 10 689). Finally, we assessed the effect of serum pyridoxal levels on schizophrenia risk using a Mendelian randomization (MR) approach. Serum pyridoxal levels were significantly lower in patients with schizophrenia than in controls, not only in our cohort, but also in the pooled data set of the meta-analysis of association studies (standardized mean difference -0.48, 95% confidence interval [CI] -0.57 to -0.39, p = 9.8 × 10-24). We failed to find a significant association between rs4654748 and schizophrenia. Furthermore, an MR analysis failed to find a causal relationship between pyridoxal levels and schizophrenia risk (odds ratio 0.99, 95% CI 0.65-1.51, p = 0.96). Food consumption and medications may have affected serum pyridoxal levels in our cross-sectional study. Sample size, number of instrumental variables and substantial heterogeneity among patients with schizophrenia are limitations of an MR analysis. We found decreased serum pyridoxal levels in patients with schizophrenia in this observational study. However, we failed to obtain data supporting a causal relationship between pyridoxal levels and schizophrenia risk using the MR approach.

The pitfall of instrumental variables in big data: What the rule of thumb can't give you

ABSTRACTBackground: Instrumental variables (IVs) have become much easier to find in the “Big data era” which has increased the number of applications of the Two-Stage Least Squares model (TSLS). With the increased availability of IVs, the possibility that these IVs are weak has increased. Prior work has suggested a ‘rule of thumb’ that IVs with a first stage F statistic at least ten will avoid a relative bias in point estimates greater than 10%. We investigated whether or not this threshold was also an efficient guarantee of low false rejection rates of the null hypothesis test in TSLS applications with many IVs.Objective: To test how the ‘rule of thumb’ for weak instruments performs in predicting low false rejection rates in the TSLS model when the number of IVs is large.Method: We used a Monte Carlo approach to create 28 original data sets for different models with the number of IVs varying from 3 to 30. For each model, we generated 2000 observations for each iteration and conducted 50,000 iterations to reach convergence in rejection rates. The point estimate was set to 0, and probabilities of rejecting this hypothesis were recorded for each model as a measurement of false rejection rate. The relationship between the endogenous variable and IVs was carefully adjusted to let the F statistics for the first stage model equal ten, thus simulating the ‘rule of thumb.’Results: We found that the false rejection rates (type I errors) increased when the number of IVs in the TSLS model increased while holding the F statistics for the first stage model equal to 10. The false rejection rate exceeds 10% when TLSL has 24 IVs and exceed 15% when TLSL has 30 IVs.Conclusion: When more instrumental variables were applied in the model, the ‘rule of thumb’ was no longer an efficient guarantee for good performance in hypothesis testing. A more restricted margin for F statistics is recommended to replace the ‘rule of thumb,’ especially when the number of instrumental variables is large.

On the Interpretation of Instrumental Variables in the Presence of Specification Errors: A Reply

The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist.

Two-Step Lasso Estimation of the Spatial Weights Matrix

The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T.

Iraq in the Social Sciences: Testing the Limits of Research

This article argues that the study of Iraq has been critically hampered by the opacity of the regime and the country's inaccessibility for field research, a staple technique for effective social science research. In lieu of these methods, scholars of Iraq have adopted many practices akin to those used in Cold War Sovietology, relying on a number of instrumental variables that are observable remotely. These include taking the discursive turn in examining the regime's public rhetoric and the historical turn in examining the state's colonial antecedents. Despite its inherent policy importance and possible contributions to theories about state formation, ethnicity, and natural resource dependence, the study of Iraq has long been relegated to the margins of social scientific inquiry. Since the removal of the Ba'ath regime in 2003, however, a plethora of new studies have appeared, making fuller use of the methodological toolkit. Still, Iraq's changing political relationship with the United States and the continuation of political instability may spell an end to the brief burgeoning of Iraq studies.

Approximate Distributions of the Likelihood Ratio Statistic in a Structural Equation with Many Instruments

This paper studies properties of the likelihood ratio (LR) tests associated with the limited information maximum likelihood (LIML) estimators in a structural form estimation when the number of instrumental variables is large. Two types of asymptotic theories are developed to approximate the distribution of the likelihood ratio (LR) statistic under the null hypothesis H0 : β = β0: a (large sample) asymptotic expansion and a large-Kn asymptotic theory. Size comparisons of two modified LR tests based on these two asymptotics are made with Moreira’s conditional likelihood ratio (CLR) test and the large K t-test.

The Estimation of Simultaneous Equation Models with Lagged Endogenous Variables and First Order Serially Correlated Errors

In this paper various methods for the estimation of simultaneous equation models with lagged endogenous variables and first order serially correlated errors are discussed. The methods differ in the number of instrumental variables used. The asymptotic and small sample properties of the various methods are compared, and the variables which must be included as instruments to insure consistent estimates are derived. A suggestion on how to estimate the approximate covariance matrix of the estimators is made.