Inference on an extended Roy model,with an application to schooling decisions in France

This paper considers the identification and estimation of an extension of Roy’s model (1951) of sectoral choice, which includes a non-pecuniary component in the selection equation and allows for uncertainty on potential earnings. We focus on the identification of the non-pecuniary component, which is key to disentangling the relative importance of monetary incentives versus preferences in the context of sorting across sectors. By making the most of the structure of the selection equation, we show that this component is point identified from the knowledge of the covariate effects on earnings, as soon as one covariate is continuous. Notably, and in contrast to most results on the identification of Roy models, this implies that identification can be achieved without any exclusion restriction nor large support condition on the covariates. As a by-product, bounds are obtained on the distribution of the ex ante   monetary returns. We propose a three-stage semiparametric estimation procedure for this model, which yields root-n$n$ consistent and asymptotically normal estimators. Finally, we apply our results to the educational context, by providing new evidence from French data that non-pecuniary factors are a key determinant of higher education attendance decisions.     

Indirect inference with time series observed with error

We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. When the condition for identification is satisfied, the structural and measurement error parameters can be consistently estimated. The issues of identification and misspecification of measurement error are discussed in detail. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure noise.     

A financial accelerator in the business sector of a macroeconometric model of a small open economy

We have incorporated a financial accelerator mechanism operating through investments in the business sector in a dynamic macroeconometric model of the Norwegian economy. In this new and amended model aggregated credit and equity prices are determined simultaneously in a system characterized by a two-directional contemporaneous causal link, which has been designed and estimated by a new procedure for simultaneous structural model design. Combined with a mechanism where credit and asset prices are mutually influenced by real investments, this creates a financial accelerator amplified by a credit-asset price spiral. Simulations illustrate how the introduction of a financial accelerator significantly reinforces and extends the economic cycles in projections and forecasts, in particular when confronted by a severe shock. Furthermore, monetary policy has a markedly stronger effect in the short and medium term, while the impact of fiscal policy is affected to a relatively small degree as it is more remotely linked to financial markets.     

Endogenous wage dispersion in a search-matching model

In this paper, we extend a job search-matching model with firm-specific investments in training, originally developed by Mortensen (Equilibrium Unemployment with Wage Posting: Burdett–Mortensen Meet Pissarides. In: Bunzel et al. (Eds.), Panel Data and Structural Labour Market Models, Amsterdam, North-Holland, 281–292), to allow for different offer arrival rates in employment and unemployment. The extension allows for extra flexibility when the model is confronted with real data. We subsequently estimate the parameters of the model on Danish labour market data using a structural nonparametric estimation procedure. We find that the model provides a good characterization of some empirical features of the labor market.     

Identification-commitment inventory (ICI model): confirmatory factor analysis and construct validity

The aim of this study is to confirm the factorial structure of the Identification-Commitment Inventory (ICI) developed within the frame of the Human System Audit (HSA) (Quijano et al. in Revist Psicol Soc Apl 10(2):27–61, 2000; Pap Psicól Revist Col Of Psicó 29:92–106, 2008). Commitment and identification are understood by the HSA at an individual level as part of the quality of human processes and resources in an organization; and therefore as antecedents of important organizational outcomes, such as personnel turnover intentions, organizational citizenship behavior, etc. (Meyer et al. in J Org Behav 27:665–683, 2006). The theoretical integrative model which underlies ICI Quijano et al. (2000) was tested in a sample (N = 625) of workers in a Spanish public hospital. Confirmatory factor analysis through structural equation modeling was performed. Elliptical least square solution was chosen as estimator procedure on account of non-normal distribution of the variables. The results confirm the goodness of fit of an integrative model, which underlies the relation between Commitment and Identification, although each one is operatively different.     

A method for estimating the timing interval in a linear econometric model, with an application to Taylor's model of staggered contracts

This paper describes and implements a procedure for estimating the timing interval in any linear econometric model. The procedure is applied to Taylor's model of staggered contracts using annual averaged price and output data. The fit of the version of Taylor's model with serially uncorrelated disturbances improves as the timing interval of the model is reduced.     

A semi-parametric Bayesian approach to the instrumental variable problem

We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable equations errors. Our implementation of the Dirichlet process prior uses a normal distribution as a base model. It can therefore be interpreted as modeling the unknown joint distribution with a mixture of normal distributions with a variable number of mixture components. We demonstrate that this procedure is both feasible and sensible using actual and simulated data. Sampling experiments compare inferences from the non-parametric Bayesian procedure with those based on procedures from the recent literature on weak instrument asymptotics. When errors are non-normal, our procedure is more efficient than standard Bayesian or classical methods.     

Identification structure of linear structural models

In this paper identification problems in linear structural models are discussed. A special but practically important form of these problems, the structural identifiability, is defined. Thereafter it is shown that this problem can be studied — without empirical data — by a set system defined on the set of free parameters of a model. This set system is the identification structure, or the matroid, of the model. It gives insight into the dependence of unidentified parameters. The advantages of this approach in research planning and in correcting nonidentifiable models is demonstrated. Finally we introduce the FORTRAN program LISRAN, which is designed to analyze a given linear structural model and to provide its identification structure.     

Inference and prediction in a multiple-structural-break model

This paper develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. The model has several desirable features. First, the number of regimes is not fixed but is treated as a random variable. Second, the model adopts a hierarchical prior for regime coefficients, which allows for the coefficients of one regime to contain information about coefficients of other regimes. Third, the regime coefficients can be integrated analytically in the posterior density; as a consequence the posterior simulator is fast and reliable. An application to US real GDP quarterly growth rates links groups of regimes to specific historical periods and provides forecasts of future growth rates.     

Labour adjustment costs: Estimation of a dynamic discrete choice model using panel data for Greek manufacturing firms

In this paper we estimate a dynamic structural model of employment at firm level. Our dataset consists of a balanced panel of 2790 Greek manufacturing firms. The empirical evidence of this dataset stresses three important stylized facts: (a) there are periods in which firms decide not to change their labour input, (b) there are periods of large employment changes (lumpy nature of labour adjustment) and (c) the commonality is employment spikes to be followed by smooth and low employment growth periods. Following Cooper and Haltiwanger [Cooper, R.W. and Haltiwanger, J. “On the Nature of Capital Adjustment Costs”, Review of Economic Studies, 2006; 73(3); 611–633], we consider a dynamic discrete choice model of a general specification of adjustment costs including convex, non-convex and “disruption of production” components. We use a method of simulated moments procedure to estimate the structural parameters. Our results indicate considerable fixed costs in the Greek employment adjustment.     

Prosumer's commitment model: The case of a biodiesel cooperative

Consumers are becoming more aware of their role in the propagation of environmental issues, the transition from passive consumer to prosumer is emerging, and it is of interest to know the commitment factors that leads the biodiesel prosumer to act in favor of an organization which they belong in order to achieve one goal in common and contribute to local sustainable development. In this research, the prosumer is the one who participates as a member and consumer of a biodiesel cooperative. The prosumer's commitment is made up of two components: (i) nonactive or affective, identification with the organization, and (ii) active or participatory, action towards certain goals. The objectives of the investigation are to evaluate the biodiesel prosumer's commitment and know its causes and effects. The hypotheses on the causes and results of the prosumer's commitment are postulated in a structural equation model (SEM), whose theoretical basis corresponds to the double role of the prosumer: member and client of the cooperative. The SEM is evaluated from the perspective of the prosumer based on mixed methodology and partial least square (PLS). The proposed SEM explains 78% (R² = 0.78) of the variability of the contribution of the biodiesel cooperative to local sustainable development, where this contribution is directly and significantly impacted by the two components of the commitment. This case study identifies dimensions that lead the biodiesel prosumer to commit in the cocreation and consumption of renewable fuel, which gives more opportunities for long-term survival of this initiative in a competitive market.     

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model’s goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.     

Fuzzy item response model: a new approach to generate membership function to score psychological measurement

The aim of this study is to propose an new approach, fuzzy item response model (FIRM), which combines item response theory (IRT) and fuzzy set theory, in the educational or psychological measurement. Applying FIRM to improve the predictive validity of psychological measurement is verified. We set up a detailed procedure for the FIRM and apply it to a valuable empirical study with Beck Depression Inventory-II (Chinese version) administrated on outpatient diagnosed as depression was given. The results showed the correct classification of depression based on FIRM scoring was 80.3% while that of raw score was only 73.2%. That is, via FIRM scoring, 7.9% of the erroneous judgments of depression inferred from self-reported inventory were reduced. It is also suggested that considerable cost concerning prevention and cure of depression might be reduced via FIRM.     

Instrumental variable estimation of nonseparable models

There are many environments where knowledge of a structural relationship is required to answer questions of interest. Also, nonseparability of a structural disturbance is a key feature of many models. Here, we consider nonparametric identification and estimation of a model that is monotonic in a nonseparable scalar disturbance, which disturbance is independent of instruments. This model leads to conditional quantile restrictions. We give local identification conditions for the structural equations from those quantile restrictions. We find that a modified completeness condition is sufficient for local identification. We also consider estimation via a nonparametric minimum distance estimator. The estimator minimizes the sum of squares of predicted values from a nonparametric regression of the quantile residual on the instruments. We show consistency of this estimator.     

Latent class path model of intention to move house

This paper proposes a latent class path model to analyse the intention to move house as a function of residential satisfaction, which in turn is influenced by the gap between residential aspirations and reality. Different from the existing literature, which assumes the same structural dependencies apply to all residents with different socio-demographic profiles, we allow for different unobserved classes with different structural dependencies. Class membership is a function of the socio-demographics of individuals. Using data from eight renovated historical blocks in two Chinese cities, the differences influencing the intention to move house between two latent classes are captured. In one class the intention to move is significantly influenced by housing and environmental satisfaction, but this dependency is not found in the other class. The impact of the residential gap on satisfaction also differs between the two classes. Class membership is found to be mainly attributed to having property rights or not and whether respondents are supporting the elderly. Policy and planning implications are provided considering the different residential interests and backgrounds of residents.     

Using Non-linear Models for a Complexity Approach to Psychological Well-being

Psychological well-being in adolescence is an increasing field of study. The literature identifies a large number of dimensions of psychological well-being. However, even when considering all these dimensions, the explanatory power of most models is rather low. Complexity theories can be a productive alternative, at the theoretical but especially the methodological level, to the limitations more traditional approaches to psychological well-being have. In this paper, we suggest a structural equation modelling approach to complexity that focuses on the non-linearity property. Given the large number of dimensions, the model is estimated in two steps as described by Jöreskog [(2000) Latent Variable Scores and Their Uses. Lincolnwood IL: Scientific Software International] First, a confirmatory factor analysis is fitted and Anderson and Rubin’s factor scores are saved. Then all possible products and squared terms of the factor scores are computed and are used as predictors of the dependent variable using an ordered logit model. The results from a sample of 968 Catalan adolescents show that a non-linear model including interaction effects among the eight dimensions, age and gender, has a higher explanatory power to predict satisfaction with life as a whole, compared to a linear model. Important consequences for the study of psychological well-being in adolescence emerge from the methodological procedure we have followed, which can be used to study any type of complex psychological and psychosocial phenomenon.     

Weak identification robust tests in an instrumental quantile model

We develop a testing procedure that is robust to identification quality in an instrumental quantile model. In order to reduce the computational burden, a multi-step approach is taken, and a two-step Anderson–Rubin (AR) statistic is considered. We then propose an orthogonal decomposition of the AR statistic, where the null distribution of each component does not depend on the assumption of a full rank of the Jacobian. Power experiments are conducted, and inferences on returns to schooling using the Angrist and Krueger data are considered as an empirical example.     

Some results on analytic optimal control solution of a simple rational expectations model

In this note, we correct a technical error in a well-known stabilization analysis in the context of a simple macroeconomic model, formulated as an optimal control problem. This error originated in a slip in an unpublished but widely circulated paper by D.A. Livesey, suggesting a procedure for obtaining a solution to a class of optimal control problems frequently encountered in a stabilization literature. We then propose an ad hoc solution method which makes clear the intuition of the solution to the particular model to which the Livesey method was applied, and of the larger class of models to which it belongs. Our solution is so formulated that the contrast between it and the Livesey result is made transparent.     

Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags

In this paper we investigate a spatial Durbin error model with finite distributed lags and consider the Bayesian MCMC estimation of the model with a smoothness prior. We study also the corresponding Bayesian model selection procedure for the spatial Durbin error model, the spatial autoregressive model and the matrix exponential spatial specification model. We derive expressions of the marginal likelihood of the three models, which greatly simplify the model selection procedure. Simulation results suggest that the Bayesian estimates of high order spatial distributed lag coefficients are more precise than the maximum likelihood estimates. When the data is generated with a general declining pattern or a unimodal pattern for lag coefficients, the spatial Durbin error model can better capture the pattern than the SAR and the MESS models in most cases. We apply the procedure to study the effect of right to work (RTW) laws on manufacturing employment.     

Identification in a model of sorting with social externalities and the causes of urban segregation

This paper discusses nonparametric identification in a model of sorting in which location choices depend on the location choices of other agents as well as prices and exogenous location characteristics. In this model, demand slopes and hence preferences are not identifiable without further restrictions because of the absence of independent variation of endogenous composition and exogenous location characteristics. Several solutions of this problem are presented and applied to data on neighborhoods in US cities. These solutions use exclusion restrictions, based on either subgroup demand shifters, the spatial structure of externalities, or the dynamics of prices and composition in response to an amenity shock. The empirical results consistently suggest the presence of strong social externalities, that is a dependence of location choices on neighborhood composition.