Unemployment duration models with non-stationary inflow and unobserved heterogeneity

This paper is concerned with econometric problems and methods involved when estimating duration models using data on uncompleted unemployment spells provided by standard labour force surveys. In particular, it considers how the model estimates are affected by the commonly applied assumption of stationary inflow rates when the true inflow rates are non-stationary and when different assumptions about unobserved heterogeneity are maintained.     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

The treatment of seasonality in error correction models as unobserved component: a case study for an Austrian consumption function

This paper deals with an alternative approach to treating seasonality in error correction models for consumption with a parsimonious parameterization as proposed by Harvey and Scott. We introduce an unobserved seasonal component into an error correction model for Austrian consumer expenditures on nondurables and services and compare the results with different approaches. The use of stochastic seasonal results in a definite improvement of the estimated model. First version received: October 1997/Final version received: May 2000     

Structural sign patterns and reduced form restrictions

This paper reconsiders the degree to which the sign patterns of hypothesized structural arrays limit the possible outcomes for the sign pattern of the corresponding estimated reduced form. The conditions under which any structural restrictions would apply were believed to be very narrow, rarely found to apply, and virtually never investigated. As a result, current practice does not test the structural hypothesis in terms of the comparison of the estimated reduced form and the permissible reduced form sign patterns. This paper shows that such tests are always possible. Namely, that the sign patterns of the hypothesized structural arrays always limit the sign patterns that can be taken on by the estimated reduced form. Given this, it is always possible to falsify a structural hypothesis based only upon the sign pattern proposed. Necessary conditions, algorithmic principles, and examples are provided to illustrate the analytic principle and the means of its application.     

A maximum likelihood approach to estimating dynamic models with unobserved independent variables

A maximum likelihood method to estimate the parameters of dynamic models containing unobserved independent variables is proposed. The approach is to maximize the likelihood of the residuals produced by the recursive Kalman filter equations applied to the model in state-space form. A simulation study is presented comparing the proposed method to the instrumental variable approach. An example using real data is given which estimates models of the Permanent-Income Hypothesis.     

An alternative identification of nonlinear dynamic panel data models with unobserved covariates

I provide the nonparametric identification of nonlinear dynamic panel data models. I relax the assumption of covariate evolution in Shiu and Hu (2013) by the results of Hu and Shum (2012). The assumptions include first-order Markov assumptions and a restriction on the evolution of the covariate.     

On the role of unobserved preference heterogeneity in discrete choice models of labour supply

The aim of this paper is to analyse the impact of unobserved preference heterogeneity in empirical applications of discrete choice models of labour supply. Typically, unobserved heterogeneity is estimated either with continuous or discrete mixture models. However, in order to avoid estimation difficulties, most of the empirical analysis assumes a relatively constrained mixture, standard examples being models where only few coefficients are allowed to vary with independent normal distributions or with discrete distributions with few mass points. We compare labour supply elasticities obtained with these typical specifications of unobserved heterogeneity with those from a more general model that we are able to estimate through an EM algorithm for the nonparametric estimation of mixed models. Results show that labour supply elasticities change significantly with respect to a basic model without unobserved heterogeneity only when the joint distribution of the varying tastes is left completely unspecified.     

Modelling export equations using an unobserved component model: the case of the Euro Area and its competitors

This article analyses developments in the external sector for the Euro Area and its major competitors and quantifies the dynamic contributions of the key determinants of trade to export volume behaviour. In addition to the traditional variables affecting export volumes, price and foreign demand, an unobserved component in the form of a time-varying trend enters the export equations to capture underlying non-price competitiveness. The structural modelling approach used within an error correction framework allows isolating the different sources of trade fluctuations and to better assess the contribution of each set of variables to export flows. The findings confirm that stochastic trends are present as a result of technical change and other exogenous factors driving export flows, and that a failure to account for these trends will lead to biased estimates of long-run price elasticities.     

Consumer allocation models: Choice of functional form

The functional form of consumer allocation models should be able to satisfy theoretical properties derived from the theory of consumer demand. The paper sketches four approaches that meet this condition. Of course, also empirical performance matters. Next to naive goodness-of-fit comparison, non-nested hypothesis testing can be employed. The latter technique is applied to a comparison of four versions of differential demand systems: the Rotterdam system, a version of the Almost Ideal Demand (AID) system, the CBS system and the NBR system. These systems are artificially nested in a more general model using scalar weights in contrast to Barten and McAleer (1991) who use matrix weights for this purpose. Annual data over the period 1921–1981 for The Netherlands for four major groups of consumer expenditure have been used for the empirical application. The CBS system dominates the others.     

Unemployment and the replacement ratio: Some reduced form estimates for the UK

Conventional estimates of the effects of the replacement ratio on unemployment treat output as exogenous. A reduced form approach is proposed which avoids this and results in a much reduced elasticity of unemployment with respect to the replacement ratio.     

Estimation of SEs for heteroscedastic and cross-sectionally correlated data

This study develops SE estimators for heteroscedastic and cross-sectionally correlated data. The new estimators are a cross-sectional version of the White and Domowitz (1984 White, H and Domowitz, I. 1984. Nonlinear regression with dependent observations. Econometrica, 52: 143–61. [Crossref], [Web of Science ®] , [Google Scholar]) and Newey and West (1987 Newey, W and West, K. 1987. A simple positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 55: 703–8. [Crossref], [Web of Science ®] , [Google Scholar]) estimators, and therefore, consistent in the presence of heteroscedasticity and cross correlation of unknown form. Unlike the estimators in the literature, these estimators can control for cross correlation even for single-period cross-sectional data.     

Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML

Efficiency analysis is an important tool for evaluating firms' performance. This paper introduces a novel approach for measuring technical efficiency (TE) in the case of technologies with multiple outputs which deals with the endogeneity of outputs issue. The proposed approach uses Artificial Neural Networks (ANNs) and the method of Limited Information Maximum Likelihood (LIML). The validity of the proposed approach is illustrated by fitting it to a large US data set for all commercial banks in the 1989–2000 time span. Meanwhile, we compare the proposed approach to the single-equation Translog output distance function and the proposed approach was found to yield very satisfactory results, while dealing with the issue of the endogeneity of outputs.     

On the derivation of reduced forms of rational expectations models

This paper introduces a more intuitive and straightforward method to obtain the reduced forms of linear models containing expectations of the current endogenous variables formed rationally in various previous periods, besides the two proposed by Lucas and Aoki and Canzoneri. This method is then used, with the aid of some examples, to derive the conditions for complete (i.e., in mean and variance) policy ineffectiveness in this kind of models.     

Advertising in consumer allocation models: choice of functional form

This study investigates the empirical performance of four different functional forms for advertising-augmented consumer allocation models. The investigation is carried out within the context of the UK alcoholic drinks markets. The aims of the study are twofold: (i) to assess which of the four functional forms provides the best explanation of the data and may serve, therefore, as the most suitable framework for investigating advertising effects in these markets; and (ii) to consider whether the findings with regard to advertising effects are robust and consistent across model specifications. Advertising is found to have had no significant effect upon the ‘product composition’ or ‘level’ of total alcoholic drink consumption in the UK over the period from 1964 to 1996, and this result is robust with respect to variations in the specification of functional form. The consumption of alcoholic drink is affected by relative prices, total consumer budgeted expenditures and, to some extent, by autonomous shifts in tastes. The balance of the evidence from tests for functional form appeared to favour the Almost Ideal Demand system as a framework for investigating the influence of advertising and other factors on drink consumption.     

A convenient test of functional form for pooled econometric models

This paper demonstrates a parametric test of specification that can be used in validating econometric models employing pooled time series and cross-section data with fixed effects. This Lagrange multiplier test allows for the simultaneous testing of proper model functional form and the presence of nonspherical disturbances, using a combination of the Box-Cox transformation, the double-length regression of Davidson and MacKinnon, and the Bonferroni inducedt-test. Testing procedures are demonstrated using a model of long distance telephone demand in the United States. The illustrative model used is representative of models filed as direct testimony by telephone companies in administrative law proceedings, which usually require rigorous model validation and defenses of model results in a formal hearing room setting. The tests presented in this paper are useful to a wide variety of researchers who use pooled econometric models with fixed effects in their work.     

Stochastic continuous time growth models that allow for closed form solutions

We find a closed form solution that maximises the expected utility of an agent’s inter-temporal consumption subject to a stochastic technology, which is a linear combination of AK and Cobb–Douglas technologies. Additionally, we consider two cases of agent preferences: (i) Constant Relative Risk Aversion (CRRA) preferences, which treat optimal consumption as a linear function of capital, and (ii) Hyperbolic Absolute Risk Aversion (HARA) preferences, which treat optimal consumption as an affine function of capital. By establishing a minimum (subsistence) level of consumption in the HARA model, we are able to create a framework that more accurately represents real-world circumstances than previous studies have done. Furthermore, for both the CRRA and HARA cases we show the suitable, consistent stochastic differential equation which describes the capital dynamics. Finally, we perform a numerical simulation based on the CRRA case and calibrate US data for the HARA case.     

Bias correction and bootstrapping of error component models for panel data: Theory and applications

The wellknown Wallace-Hussain estimator is applied in pooled models with random individual effects, and the magnitude of the bias caused by the estimator is estimated by bootstrap methods. Furthermore, the significance of the bias is tested using an asymptotic test based on the bootstrap results.A preliminary version of this paper was presented at the Econometric Society European Meeting ESEM '88 in Bologna. The authors thank an anonymous referee for helpful comments. The data for one of the applications (the earnings function) are from the German Socio-Economic Panel and we thank the DIW and the Sfb 3 for the permission to use these data.     

Testing commodity futures market efficiency under time-varying risk premiums and heteroscedastic prices

We propose a novel test to measure market efficiency while estimating the time-varying risk premiums of commodity futures, given that the prices are heteroscedastic. The risk premium is estimated using a state-space model with a Kalman filter modified for heteroscedasticity. Using 79 commodity futures traded on 16 exchanges during the period 2000–2014 and a Monte Carlo simulation, we demonstrate that the proposal produces robust results compared with conventional approaches. The global financial crisis has improved the efficiency and affected the trading volumes of commodity futures, but it has had no effect on the average or the volatility of risk premiums.     

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.