A multidimensional spatial lag panel data model with spatial moving average nested random effects errors

This paper focuses on a three-dimensional model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag panel data model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment data for regions nested within countries.     

How useful is yet another data-driven bandwidth in long-run variance estimation?: A simulation study on cointegrating regressions

This paper investigates how bandwidth choice rules in long-run variance estimation affect finite-sample performance of efficient estimators for cointegrating regression models. Monte Carlo results indicate that Hirukawa's (2010) bandwidth choice rule contributes bias reduction in the estimators.     

Asymptotic Variance–Covariance Matrices of Two-Stage Estimators in the Presence of Continuous and Binary Dependent Variables with an Empirical Application

Journal of Quantitative Economics - We develop a general procedure to derive the asymptotic variance–covariance matrices of several two-stage estimators that can be used to estimate...     

Finnish GNP-series 1954/I–1990/IV: Small shock persistance or trend stationarity? Some evidence with variance ratio estimates

The “shock persistance” of Finnish adjusted quarterly real GNP series in logarithms from 1954/QI to 1990/QIV is analyzed using variance ratio estimators. The results indicate that the random walk component of the series is not big. The small sample properties of variance ratio estimators are studied using empirical distribution derived from simulations. The persistence measures calculated via the ARIMA modelling of the lnGNP_t series are biased upwards. The sampling properties show that the simple random walk model is not an alternative model for the lnGNP. A trend stationary alternative, an AR(2) process, gives almost the same “shock persistence” measures as the assumed unit root processes.     

Optimality of the Stein-type Estimator for Variance

This paper considers the optimality of the Stein-type estimator of the disturbance variance among the class of pre-test estimators after the pre-test for a linear hypothesis on coefficients. The Stein-type estimator is a member in this class with a critical value, say c_s . It is known that the Stein-type estimator is the best in the subclass which consists of the pre- test estimators with smaller critical values than c_s . It is analytically shown that an extension of the optimality to a larger class is not possible.     

Robust random effects tests for two-way error component models with panel data

In this paper, two test statistics are constructed respectively for individual and time effects in linear panel data models by comparing estimators of the variance of the idiosyncratic error at different robust levels. The resultant tests are one-sided, and asymptotically normally distributed under the null hypothesis. Power study shows that the tests can detect local alternatives that differ from the null hypothesis at the parametric rate. Due to the first difference and orthogonal transformations used in the construction of variance estimators of the idiosyncratic error, the two proposed tests are robust to the presence of one effect and the possible correlation between the covariates and the error components when the other one is tested. Monte Carlo simulations are carried out to provide evidence on the finite sample properties of the tests.     

Practical guidelines for the estimation and inference of a dynamic logistic model with fixed-effects

This paper shows how to simply compute one of the estimators proposed by Honoré and Kyriazidou (2000), as well as its variance, through a reshaping of the original dataset that is then used in a weighted logistic regression with clustering.     

State dependence,serial correlation and heterogeneity in the union membership dynamics

In order to distinguish the true and spurious state dependence from the complicated dynamics of union membership, the simulation estimators incorporating the lagged dependent variables, unobserved individual heterogeneity and correlations among the errors are implemented in this article to study union membership dynamics. It is found that the true state dependence of union membership under multivariate t assumption is much higher than the standard dynamic panel probit estimators which are under multivariate normal assumptions. On the other hand, the spurious state dependence (the variance of the unobserved individual heterogeneity) is estimated to be higher when using the standard dynamic panel probit estimators than under multivariate t assumption. Moreover, blacks and married men are found to have higher union membership true state dependence than whites and unmarried men.     

Sustainable society—Ethics and economic growth : Stivers, Robert L., 225 pp.

The issue of forming a composition of estimators, which can be forecast from quantitative predictive models or expert opinions, is discussed and a case made for linear combinations. Two methods are presented, one aimed at an optimal, minimum variance composition; and the other, at utilizing linear weights with a directly meaningful probabilistic interpretation. Bayesian estimation methods are used in both cases.     

The maximum number of parameters for the Hausman test when the estimators are from different sets of equations

Hausman (1978) developed a widely-used model specification test that has passed the test of time. In this paper, we show that the asymptotic variance of the difference of the two estimators can be a singular matrix. Three illustrative examples are used, namely an exogeneity test for the linear regression model, a test for the Box–Cox transformation, and a test for sample selection bias.     

The combined estimator approach to model transferability and updating

The idea of transferability is to employ in model estimation, fitted model parameters computed from a different data set. Thecombined estimator approach to the transferability problem is expressed as a linear combination of the unbiased direct estimators on the two data sets. The major gain is in variance reduction. The combined estimator is shown to have superior accuracy, in a Mean Square Error sense, to a unbiased direct estimator whenever the transfer bias is relatively small. A test that indicates if the combined estimator is superior to the direct estimator is provided. Variances of the direct estimators are assumed to be known. Monte Carlo experiments are performed to assess the quality of the approximations. The results show that the approximations used are highly conservative. An empirical example of the combined estimator applied to a discrete choice problem is presented.     

Services sector and economic growth in India

This study examines the long-run equilibrium and short-run dynamic relationship between services sector and Gross Domestic Product (GDP) and between services and nonservices sectors in India. The model is estimated using the optimal single-equation and the maximum-likelihood system estimators. All the estimators consistently suggest the cointegrating relationship between services sector and GDP as well as between services and nonservices sectors. The estimates of long-run elasticity parameters are statistically significant and dimensionally consistent across the estimators. The conventional Cumulative Sum (CUSUM) and the new CUSUM and Moving Sum (MOSUM) tests suggest the stability of the equilibrium residuals and reinforce the cointegrating relationship between the model series. The error correction model provides some support for unidirectional Granger-causality from services sector to GDP. The impulse response and variance decomposition analyses instead suggest the bidirectional causality between services sector and GDP and between services and nonservices sectors. The stable growth of services sector is essentially crucial to absorb the adverse effects of exogenous weather shocks in agriculture and industry and provide resilience to the economy.     

On Efficient Estimation and Correct Inference in Models with Generated Regressors: a General Approach

Two approaches have been developed for deriving the properties of efficiency and consistency of standard errors of two step estimators of linear models containing current or lagged unobserved expectations of a single variable. One method is based on the derivatives of the likelihood function and information matrix, while the other uses the true covariance matrix of the disturbance vector when unknown parameters or variables are replaced by corresponding estimates. In this paper, the second approach is extended to cases where the structural equation is nonlinear and the model contains expectations of more than one variable or expectations of future variables. The properties of a frequently used estimator to deal with missing observations problems, a model involving a variance as an explanatory variable, and a recently developed estimator for autoregressive moving average models can be easily derived using the results of the paper. Methods for improving the efficiency of two step estimators are outlined.
JEL Classification Number: C13     

Efficiency of two-step estimators for censored systems of equations: Shonkwiler and Yen reconsidered

This study analyses a parametric estimator for a system of equations with limited dependent variables that was recently proposed. Its performance is compared with those of alternative estimation procedures using Monte Carlo methods. The comparison shows that this new estimator is less efficient for a wide range of parameter regions than multivariate generalizations of the classical Heckman model. This result can be explained by its variance depending on the squared conditional mean of the dependent variables. Additionally, it turns out that within the class of generalized Heckman estimators, rather simple ones display the best performance.     

Data-dependent selection of the lag truncation parameter in unit root tests of the Phillips-Perron type

The Andrews (Econometrica, 1991, 59, 817–858) plug-in method of heteroscedastic and autocovariance consistent covariance matrix estimation is used to construct estimators of the long-run variance parameter for use in Phillips-Perron unit root tests. This allows the lag truncation parameter to be data dependent. Monte Carlo size and power estimates are obtained suggesting that this apparently natural approach does not provide significant improvements in test performance.     

Spatial econometric Monte Carlo studies: raising the bar

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification. A major point is that practitioners rely on scalar summary measures of direct and indirect effects estimates to interpret the impact of changes in explanatory variables on the dependent variable of interest. We argue that these should be the focus of Monte Carlo experiments. Since effects estimates reflect a nonlinear function of both \(\beta \) and \(\rho \), past studies’ focus exclusively on \(\beta \) and \(\rho \) parameter estimates may not provide useful information regarding statistical properties of effects estimates produced by alternative estimators. Since effects estimates have recently become the focus of inference regarding the significance of (scalar summary) direct and indirect impacts arising from changes in the explanatory variables, empirical measures of dispersion produced by simulating draws from the (estimated) variance–covariance matrix of the parameters \(\beta \) and \(\rho \) should be part of the Monte Carlo study. An implication is that differences in the quality of estimated variance–covariance matrices arising from alternative estimators also plays a role in determining the accuracy of inference. An applied illustration is used to demonstrate how these issues can impact conclusions regarding the performance of alternative estimators.     

Using inverted Engel curves to estimate material standards of living in a household

This article demonstrates how to estimate latent total consumption expenditure or material standard of living in households by inverting estimated Engel curves. While the conventional estimator, total purchase expenditure, is unbiased for latent total household consumption expenditure, it is not variance minimizing since it is an un-weighted sum. In two stages, this article derives a variance-minimizing, unbiased estimator by first estimating and inverting Engel curves; then combining the estimators from the inverted Engel curves. The employed latent variable method allows for utilization of non-expenditure relations. The suggested method may help improve the accuracy in studies of consumption inequality and tax evasion.      

Efficient estimation of macroeconomic equations with unobservable states

Macroeconomic equations, such as the consumption Euler equation, New Keynesian Phillips curve, and Taylor rule, are regularly estimated on an individual basis. However, such relations also jointly determine equilibrium, which may contain unobservable states. This paper shows how to utilize such an equilibrium model to improve the efficiency of individual estimators. In comparison with existing related approaches, this simple framework lends itself naturally to modern medium scale dynamic stochastic general equilibrium models. Not only does the derived estimator exhibit smaller asymptotic variance than equation-by-equation GMM, it also tends to be less prone to small sample distortions from weak identification.     

Improving the Performance of a Long‐Run Variance Ratio Test for a Unit Root

Cai and Shintani (2006, Econometric Theory, 22, 347–372) considered the impact of introducing an inconsistent long‐run variance estimator when constructing a class of kernel‐based ratio tests for testing non‐stationarity in the series. They found that the quotient of two estimators with different rates of convergence under the null and the alternative hypotheses may lead to a test having an interesting size and power trade‐off. This paper develops modified versions of this test, presents new asymptotic results and tabulates critical values. The finite sample performance is explored through Monte Carlo simulations. The results show that the modifications proposed lead to more powerful unit root tests.     

Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches

The study examines the roles of experience and education in explaining the increase in wage inequality among Philippine male workers between 1988 and 1995. It also provides a methodological approach to the analysis of wage inequality by combining non-parametric methods with semiparametric additive models, using the variance accounting framework. Non-parametric density estimators allow flexibility in dealing with distributional inference while additive models yield marginal effects estimates under minimal assumptions on the functional specification of the wage–schooling and wage–experience relationships. The results show that much of the inequality increase from 1988 to 1995 was caused by greater variabilities in returns to schooling and experience among 1995 workers. The rise of the p90/p10 percentile ratio was caused by greater return variabilities on schooling and experience in the 90th percentile.