Inference approaches for instrumental variable quantile regression

We consider asymptotic and finite sample confidence bounds in instrumental variables quantile regressions of wages on schooling with relatively weak instruments. We find practically important differences between the asymptotic and finite sample interval estimates.     

An asymptotic variance inequality for instrumental variable estimators signaling proportional bias increases

An asymptotic variance inequality for instrumental variable (IV) estimators is proposed, which suggests a critical variance that signals proportional increases in the bias of IV estimators through the augmentation of a set of instruments.     

Exact equivalence of instrumental variable estimators in an error component structural system

The paper establishes an equivalence result in the context of anm-equation error component structural system, whose disturbances have the usual three-component structure, and whose equations feature explanatory variables of the formz _i, z_t andz _it; the latter vary (respectively) only over individuals, only over time, and over both. Under the stochastic specification assumed, it is shown that the alternative instrumental variables (IV) estimators commonly used in the special cases of this system are all equivalent (numerically identical); the result is a generalization of the equivalences established previously for the special cases. In the single equation (m=1) context, the equivalence requires that the IV set contain variables of the formz _i and/orz _t, and further, in numbers determined by the ranks of (respectively) the individuals-mean and time-mean matrices of the instruments. If such an IV set is common to all equations, the equivalence also holds for the system under joint estimation. The result is used to recommend a couple of estimators for use in panel data, on grounds of computational simplicity.This is a revision of the December 1990 draft with the same title, and is a substantial revision of the April 1990 version entitled: Analysis of an error component structural system. This revision has benefited from comments received from a referee and a editor of this journal. I came to know from an anonymous reader that the equivalence criterion developed in my 1990 a article, used here and the two earlier versions, was infact obtained previously in an unpublished paper by Balestra (1988). Balestra's paper, which was made available to me by Badi Baltagi at the time of this revision, and subsequently by Balestra, considers the equivalence of ^b, ^c and one other estimator which differs from our ^a. Errors, if any, are my responsibility.     

Bootstrapping covariate stationarity tests for inflation rates

This paper investigates the stationarity properties of international inflation rates by bootstrapping two stationarity tests with covariates in Jansson (2004). When the asymptotic critical values are used, the two powerful tests are found to reject the null hypothesis less in the presence of a large negative moving-average (MA) error in inflation. To cope with this problem, a parametric bootstrap scheme is developed and then is investigated by a Monte Carlo study. The simulation results demonstrate that the bootstrap tests display a better control over the empirical rejection rates at finite samples. Furthermore, after applying these tests to the inflation in G-10 countries, we find that one of the two tests using bootstrap critical values yields inferences that differ from when using asymptotic ones, and as a whole, the bootstrap tests consistently provide strong evidence in support of mean reversion in inflation in most countries of the G-10.     

Testing of functional forms of regressions with lagged dependent variable and autocorrelated errors

This paper presents the test statistics for the Maximum Likelihood Ratio, the Lagrange Multiplier and the Wald tests for regressions that involve a lagged dependent variable, autocorrelated errors and the Box–Cox transformation. A computational procedure is suggested.     

Recursions for instrumental variables estimators

This paper presents simple recursion formulae for updating a broad family of instrumental variables estimators, and the associated residual sums of squares, as additional data become available.     

A Monte Carlo approach to compute the asymptotic standard errors of dynamic multipliers

A Monte Carlo method to compute asymptotic standard errors of dynamic multipliers is proposed. It is applied to Hein's Model I to find standard error of interim multipliers of taxes on nominal income.     

Bootstrapping Student Understanding of What is Going on in Econometrics

Econometrics is an intellectual game played by rules based on the sampling distribution concept. Most students in econometrics classes are uncomfortable because they do not know these rules and so do not understand what is going on in econometrics. This article contains some explanations for this phenomenon and suggestions for how this problem can be addressed. Instructors are encouraged to use explain-how-to-bootstrap exercises to promote student understanding of the rules of the game.     

Bootstrapping structural VARs: Avoiding a potential bias in confidence intervals for impulse response functions

Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural vector autoregression (SVAR) models has become standard practice in empirical macroeconomic research. The accuracy of such confidence intervals can deteriorate severely, however, if the bootstrap IRFs are biased. We document an apparently common source of bias in the estimation of the VAR error covariance matrix which can be easily reduced by a scale adjustment. This bias is generally unrecognized because it only affects the bootstrap estimates of the error variance, not the original OLS estimates. Nevertheless, as we illustrate here, analytically, with sampling experiments, and in an example from the literature, the bootstrap error variance bias can have significant distorting effects on bootstrap IRF confidence intervals. We also show that scale-adjusted bootstrap confidence intervals can be expected to exhibit improved coverage accuracy.     

Iterative algorithm for non parametric estimation of the instrumental variables quantiles

This paper proposes a simple algorithm for the numerical computation of the non parametric IV quantile estimation. This algorithm is based on the Landweber iterations for solving a nonlinear integral equation. The paper is illustrated by numerical simulations.     

Semi-non-parametric estimates of substitution for Canadian monetary assets

We estimate the dynamic Fourier expenditure system to obtain consistent estimates of short-run and long-run Morishima elasticities of substitution for Canadian liquid assets. We argue that the variability of the estimated elasticities and evidence of less than perfect substitution between monetary assets interferes with the successful use of simple-sum aggregates and traditional log-linear money-demand functions.
Calibrations semi-non-paramétriques de la substitution pour des actifs monétaires canadiens . Les auteurs calibrent le système dynamique de dépenses à la Fourier pour obtenir des estimations cohérentes des élasticités de substitution à la Morishima à court et à long termes pour des actifs monétaires canadiens. Ils suggèrent que la variabilité des élasticités estimées et la constatation que la substitution n'est pas parfaite entre les actifs monétaires rendent difficile un usage heureux des agrégats de simple somme et des fonctions log-linéaires de demande de monnaie.     

Scoring run-off paradoxes for variable electorates

   Summary. A no-show paradox occurs each time a single voter or a group of voters can manipulate the outcome by not participating to the election process. Among other voting procedures, the scoring run-off methods, which eliminate progressively the alternatives on the basis of scoring rules, suffer from this flaw. We here estimate how frequent this paradox is for three candidate elections under the classical Impartial Culture and Impartial Anonymous Culture assumptions, for different population sizes. The conditions under which this paradox occurs are also described, as well as the relationships with manipulations for a fixed number of voters. Received: October 21, 1999; revised version: January 12, 2000     

A quantile regression approach for estimating panel data models using instrumental variables

We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.     

Viewpoint: An extended class of instrumental variables for the estimation of causal effects

Abstract We examine how structural systems can yield observed variables instrumental in identifying and estimating causal effects. We provide an exhaustive characterization of potentially identifying conditional exogeneity relationships and demonstrate how structural relations determine exogeneity and exclusion restrictions that yield moment conditions supporting identification. This provides a comprehensive framework for constructing instruments and covariates. We introduce notions of conditioning and conditional extended instrumental variables (XIVs). These permit identification but need not be traditional instruments, as they may be endogenous. We distinguish between observed XIVs and proxies for unobserved XIVs. A main message is the importance of sufficiently specifying causal relations governing the unobservables.     

Bass model estimates for broadband diffusion in European countries

The paper presents a broadband diffusion analysis for European OECD member countries. The Bass diffusion model is used to estimate the total number of potential adopters, the coefficient of innovation and the coefficient of imitation. The analysis shows that if the present trends continue broadband services will not reach the 100% penetration rate in the near future. Based on these findings, the question of a possible revitalization is discussed with a focus on those segments of the population which are slower to adopt broadband. A survey of households in rural areas provides preliminary investigation of reasons for continuing non-adoption, such as social habits and lack of both knowledge and interest.     

Nonlinear regression for unit root models with autoregressive errors

This paper shows that the nonlinear least squares estimator for unit root models has the limiting distribution free of nuisance parameters and is more efficient than the augmented Dickey–Fuller estimator when the sum of coefficients for lagged variables is negative.     

Determinants of innovation behaviour and investment estimates for west-german manufacturing firms

In this paper, the determinants of innovation behaviour and investment are explored with a large micro-data panel from West-German manufacturing firms. The estimates are discussed within a microeconomic model with monopolistic competition, demand uncertainty and a delayed adjustment of capacities and the production technology. The estimates reveal positive firm-size effects which hint towards scale economies associated with innovations. Market power promotes innovations but not investment, and exporters innovate more but exhibit less investment expenditures. Finally, excess demand promotes innovations. This indicates a complementarity of innovations and investment and hints towards permanent productivity effects of temporary demand shocks.     

Efficient specification tests for limited dependent variable models

The procedure of Jarque and Bera (1980a, b), consisting of the application of the Lagrange Multiplier (LM) test to the Pearson Family of distributions, is used to derive efficient normality and/or homoscedasticity tests for limited dependent variable (LDV) models.