Subgraph Network Random Effects Error Components Models: Specification and Testing

This paper develops a subgraph random effects error components model for network data linear regression where the unit of observation is the node. In particular, it allows for link and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the coefficients’ variance-covariance matrix. It also proposes consistent estimators of the variance components using quadratic forms and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show that the tests have good performance in finite samples. It applies the proposed tests to the Call interbank market in Argentina.

Testing for random coefficient autoregressive and stochastic unit root models

The random coefficient autoregressive model has been utilized for modeling financial time series because it possesses features that are often observed in financial time series. When the mean of the random coefficient is one, it is called the stochastic unit root model. This paper proposes two Lagrange multiplier tests for the null hypotheses of random coefficient autoregressive and stochastic unit root models against a more general model. We apply our Lagrange multiplier tests to several stock index data, and find that the stochastic unit root model is rejected, whereas the random coefficient autoregressive model is not. This result indicates that it is important to check the validity of the stochastic unit root model prior to applying it to financial time series data, which may be better modeled by the random coefficient autoregressive model with the mean being not equal to one.

How the Mission Internalization Works? An Empirical Research

The objective of this paper is to analyse the process of the definition and deployment of a company’s mission, to obtain a better understanding of the employees’ role. On the basis of the literature investigating the dimensions of the internalization of a mission (leadership, importance, knowledge, co-workers’ engagement and implication), the paper proposes a model that shows the cause and effect relationships among these dimensions. A survey addressed to Spaniards was launched, and 400 valid responses were received. The data was analysed using Structural Equation Modelling (SEM) for an initial model that shows the causal relations among the dimensions for the internalization of a mission. An array of Lagrange multiplier tests suggested modifications for refining the model and proposed one with acceptable fit indices, where the last dimension to be accomplished is “Implication”. The findings show a direct effect between “Leadership” and “Implication”, and double mediation. On the one hand, there is second order mediation through “Knowledge” and “Importance”. On the other hand, there is mediation through “Co-workers’ engagement”. This sequencing among the five dimensions of the internalization of the mission gives new clues and evidence for managers that will help them to define and implement a successful mission statement.

Lagrange Multiplier Tests in Applied Research

Applied research requires the usage of the proper statistics for hypothesis testing. Constrained optimization problems provide a framework that enables the researcher to build a statistic that fits his data and hypothesis at hand. In this paper I show some of the necessary conditions to obtain a Lagrange Multiplier test as well as some popular applications in order to highlight the usefulness of the test when the researcher must rely in asymptotic theory and to help the reader in the construction of a test in applied work.

Tests for nonlinear restrictions under misspecified alternatives with an application to testing rational expectation hypotheses

Summary This paper develops generalized method of moments-based (GMM-based) Lagrange multiplier tests for nonlinear hypotheses that are robust to locally misspecified possibly nonlinear alternatives. The procedure is based on an initial consistent GMM estimator of the parameters under a given set of nonlinear restrictions. The new test for one particular set of nonlinear hypotheses is consistent and has correct asymptotic size independently of whether the other, also nonlinear hypotheses, are correct or locally misspecified. To illustrate the usefulness of our proposed tests we consider testing rational expectations hypotheses using U.S. data.

Testing for misspecification in the short-run component of GARCH-type models

In this article, a misspecification test in conditional volatility and GARCH-type models is presented. We propose a Lagrange Multiplier type test based on a Taylor expansion to distinguish between (G)ARCH models and unknown GARCH-type models. This new test can be seen as a general misspecification test of a large set of GARCH-type univariate models. It focuses on the short-term component of the volatility. We investigate the size and the power of this test through Monte Carlo experiments and we compare it to two other standard Lagrange Multiplier tests, which are more restrictive. We show the usefulness of our test with an illustrative empirical example based on daily exchange rate returns.

Capital productivity in industrialised economies: Evidence from error-correction model and lagrange multiplier tests

The paper re-examines the ?stylized facts? of the balanced growth in developed economies, looking specifically at capital productivity variable. The economic data is obtained from European Commission AMECO database, spanning 1961-2014 period. For a sample of 22 OECD economies, the paper applies univariate LM unit root tests with one or two structural breaks, and estimates error-correction and linear trend models with breaks. It is shown that diverse statistical patterns were present across economies and overall mixed evidence is provided as to the stability of capital productivity and balanced growth in general. Specifically, both upward and downward trends in capital productivity were present, while in several economies mean reversion and random walk patterns were observed. The data and results were largely in line with major theoretical explanations pertaining to capital productivity. With regard to determinants of the capital productivity movements, the structure of capital stock and the prices of capital goods were likely most salient.

Testing for Nonlinearity in Conditional Covariances

We propose two Lagrange multiplier tests for nonlinearity in conditional covariances in multivariate GARCH models. The null hypothesis is the scalar BEKK model in which covolatilities of time series are driven by a linear function of their own lags and lagged squared innovations. The alternative hypothesis is an extension of the model in which covolatilities are modeled by a nonlinear function of the lagged squared innovations, represented by an exponential or a logistic transition function. Moreover, on the same basis we develop two other tests that are robust to leverage effects. We investigate the size and power of these tests through Monte Carlo experiments, and we provide empirical illustrations in many of which cases these tests encourage the use of nonlinearity in conditional covariances.