General equilibrium analysis of the benefits of large transportation improvements

This paper considers three benefit measures of a large transportation improvement in a general equilibrium framework, i.e., the Marshall-Dupuit consumer's surplus, the. compensating variation, and the compensating surplus. First, we examine whether or not the measures can be reduced to the area to the left of a suitably defined transportation demand curve. Second, the measures are expressed as functions of various price and income elasticities which can be empirically estimated and we analyze factors affecting the magnitudes of the general equilibrium benefit measures. Third, the general equilibrium measures are compared with the partial equilibrium measures.     

Non-causality in bivariate binary time series

In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger non-causality and simultaneous independence can be tested by Likelihood Ratio or Wald tests. A specialized version of the model, aimed at testing Granger non-causality with bivariate discrete-time survival data is also discussed. The proposed tests are illustrated in two empirical applications.     

A simple,flexible estimator for count and other ordered discrete data

This paper examines a flexible way to model empirically discrete data outcomes using ‘hazard rate’ decompositions. It presents a general data‐generating mechanism based on potential outcomes to describe why the approach should work for almost any discrete distribution. Monte Carlo evidence indicates that these models estimate well the impacts of covariates on expected counts when the data follow a Poisson distribution. With data from more complex processes, these estimators continue to perform well. Since most economic count outcomes arise from occurrence‐dependent behavioral processes, using flexibly estimated distributions should reduce the dependence of results on convenient but invalid assumptions. Copyright © 2010 John Wiley & Sons, Ltd.     

Using a Demand System to Estimate Extended Input–Output Multipliers

The interface between household income and expenditure has always been considered to be a key component in the construction of input–output models. However, it can be argued that households are too often treated as if they were just another in dustry in the input–output table. In this paper, we seek to address this problem by developing a new modelling framework in which a micro demand system is used to estim ate the relationship between income and expenditure. This demand system is conjoined with an input–output table for the UK economy, and the system as a whole is solved as a computable general equilibrium model. Comparisons are made between the Jacobian multipliers generated by this model and those derived from a more traditional input–output model in which the income-expenditure linkage is estimated using static coefficients.     

How should monetary policy respond to changes in the relative price of oil? Considering supply and demand shocks

This paper examines optimal monetary policy in a New Keynesian model where supply and demand shocks affect the price of oil. Optimal policy fully stabilizes core inflation when wages are flexible. The nominal rate rises (falls) in response to the demand (supply) shock. With sticky wages core inflation falls (rises) in response to the demand (supply) shock. Impulse response functions from a VAR estimated with post-1986 U.S. data show minimal movement in core inflation in response to both shocks. The federal funds rate rises (falls) in response to the demand (supply) shock, consistent with the predictions from the theoretical model for policy that stabilizes core inflation.     

Capital budgeting with technology choice and demand fluctuations in a simple manufacturing model: Sample calculations and observations on output flexibility

In capital budgeting with technology choice and demand fluctuations errors can arise if managers unitize fixed costs and do not make proper allowance for expected idle capacity. In a standard-cost system, managers and accountants should use as the standard cost E(AC), the expected average cost, employing a procedure shown in the paper. Managers should compare E(AC) and the short-run-average-cost minimum, an output-flexibility indicator proposed by the author. The more output flexible is the equipment, the less of an increase in costs to a firm if there are wider fluctuations in output rates than planned.     

A flexible dynamic model of multiproduct technology for the West German economy

This paper tests symmetry and negativity of the Slutsky matrix for a system of demand functions derived from an aggregate model of multi product technology within a flexible dynamic framework. The model considers three inputs and three outputs, including imports and exports of intermediate goods. We derive a static demand model from a trans log cost function and specify the data generation process by a stationary ARX (1, 1) model. Results based on West German quarterly data indicate that the integrability conditions are not rejected when imposed on the ARX (1, 1) model, whereas they are rejected for all the less general dynamic models considered.     

Estimation of collective household models with Engel curves

The structural consumer demand methods used to estimate the parameters of collective household models are typically either very restrictive and easy to implement or very general and difficult to estimate. In this paper, we provide a middle ground. We adapt the very general framework of [Browning, M., Chiappori, P.A., Lewbel, A., 2004. Estimating Consumption Economies of Scale, Adult Equivalence Scales, and Household Bargaining Power, Boston College Working Papers in Economics 588] by adding a simple restriction that recasts the empirical model from a highly nonlinear demand system with price variation to a slightly nonlinear Engel curve system. Our restriction has an interpretation in terms of the behaviour of household scale economies and is testable. Our method identifies the levels of (not just changes in) household resource shares, and a variant of equivalence scales called indifference scales. We apply our methodology to Canadian expenditure data.     

Analysis of binary data with the possibility of wrong ascertainment

Most genetic studies recruit high‐risk families, and the discoveries are based on non‐random selected groups. We must consider the consequences of this ascertainment process to apply the results of genetic research to the general population. In addition, in epidemiological studies, binary responses are often misclassified. We proposed a binary logistic regression model that provides a novel and flexible way to correct for misclassification in binary responses, taking into account the ascertainment issues. A hierarchical Bayesian analysis using Markov chain Monte Carlo method has been carried out to investigate the effect of covariates on disease status. The focus of this paper is to study the effect of classification errors and non‐random ascertainment on the estimates of the model parameters. An extensive simulation study indicated that the proposed model results in substantial improvement of the estimates. Two data sets have been revisited to illustrate the methodology.     

THE DEMAND FOR GASOLINE: EVIDENCE FROM HOUSEHOLD SURVEY DATA

In this paper we investigate the demand for gasoline in Canada using recent annual expenditure data from the Canadian Survey of Household Spending, over a 13‐year period from 1997 to 2009, on three expenditure categories in the transportation sector: gasoline, local transportation, and intercity transportation. In doing so, we use three of the most widely used locally flexible functional forms, the Almost Ideal Demand System (AIDS) of Deaton and Muellbauer (1980), the quadratic AIDS (QUAIDS) of Banks et al. (1997)—an extension of the simple AIDS model that can generate quadratic Engel curves—and the Minflex Laurent model of Barnett (1983), which can also generate quadratic Engel curves. We pay explicit attention to economic regularity, argue that unless regularity is attained by luck, flexible functional forms should always be estimated subject to regularity as suggested by Barnett (2002), and impose local curvature to produce inference consistent with neoclassical microeconomic theory. Our findings indicate that the curvature‐constrained Minflex Laurent model is the only model that is able to provide theoretically consistent estimates of the Canadian demand for gasoline. Our estimates show that the own‐price elasticity for gasoline demand in Canada is between ? 0.738 and ? 0.570 —less elastic than previously reported in the literature. Copyright © 2013 John Wiley & Sons, Ltd.     

By force of demand: Explaining cyclical fluctuations of international trade and government spending

This paper explores the role of demand shocks, as an alternative to productivity shocks, in driving both domestic and international business cycles within the international real business cycle (IRBC) framework. In addition to those well-documented domestic business cycle fluctuations (e.g., the volatility and cyclicality of output, consumption, investment, labor hours, and labor productivity) and international business cycle properties (e.g., the countercyclical net export and the comovement puzzle), this paper focuses on two additional stylized facts in the industrialized countries: the procyclical trade openness (the GDP fraction of trade volume) and the countercyclical government size (the GDP fraction of government spending). Using a parsimonious dynamic stochastic general equilibrium model, we show that the model׳s predictions under productivity shocks are not consistent with these facts. Instead, a demand-shock-driven model replicates the above facts while matching other domestic and international business cycle properties. An estimated version of the model confirms the quantitatively important impacts of demand shocks.     

Regression density estimation using smooth adaptive Gaussian mixtures

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.     

Shrinkage estimation of semiparametric multiplicative error models

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on the estimates can be chosen using different methodologies: Cross-Validation for dependent data or the recently proposed Focused Information Criterion. We illustrate such a methodology using a semiparametric autoregressive conditional duration model that decomposes the conditional expectations of durations into their dynamic (parametric) and diurnal (flexible) components. We use a shrinkage estimator that jointly estimates the parameters of the two components and controls the smoothness of the estimated flexible component. The results show that, from the forecasting perspective, an appropriate shrinkage strategy can significantly improve on the baseline maximum likelihood estimation.     

Migration and the wage and unemployment gaps between urban and non-urban sectors: A dynamic general equilibrium reinterpretation of the Harris–Todaro equilibrium

This paper offers a dynamic general equilibrium reinterpretation of the static partial migration equilibrium by Harris and Todaro [Harris, J., Todaro, M., 1970. Migration, unemployment and development; a two-sector analysis. American Economic Review 60, 126–142], under (i) flexible urban and rural wages and (ii) free mobility of workers and free entry of firms. The proposed model accounts for the set of stylized facts in developing countries: rural to urban migration and higher urban wages and unemployment.The model allows us to view the wage gap as a compensating differential for the negative amenities associated with job destruction and subsequent costly search on the consumption side, which can also be seen as a match-specific premium based on a sectoral productivity differential on the production side. Our model predicts the comovements among urban and non-urban wages and migration flows to the urban sector, an empirical regularity observed over the urbanization process of developing economies. Finally, we also conduct a welfare analysis.     

A firm level perspective on migration: the role of extra-EU workers in Italian manufacturing

A production-theory approach to migration is adopted in this paper to address the role of migrant workers from extra-EU countries in Italian manufacturing firms. The adoption of flexible functional forms to model firm-level technology lets us directly derive different measures of elasticity from the coefficients of the estimated production and cost functions. The use of foreign labour is shown to affect the industry composition in favour of low skill intensive sectors and the estimated cross demand elasticities confirm the complementarity between migrant and native workers found in previous studies. However, the two labour inputs prove to be substitutes in terms of the Morishima elasticity of substitution: in general, firms tend to increase the foreign labour intensity of production in response to a decline in migrants’ wage, while the migrant to domestic labour ratio responds to changes in the domestic workers’ wage only for firms in low skill intensive sectors.     

Decomposition of differences in distribution using quantile regression

This paper proposes a semiparametric estimator of distribution functions in the presence of covariates. The method is based on the estimation of the conditional distribution by quantile regression. The conditional distribution is then integrated over the range of covariates. Counterfactual distributions can be estimated, allowing the decomposition of changes in distribution into three factors: coefficients, covariates and residuals. Sources of changes in wage inequality in the USA between 1973 and 1989 are examined. Unlike most of the literature, we find that residuals account for only 20% of the explosion of inequality in the 80s.     

Treatment effect estimation with covariate measurement error

This paper investigates the effect that covariate measurement error has on a treatment effect analysis built on an unconfoundedness restriction in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate effects of measurement error for estimators of average treatment effects. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated covariates without employing additional information from validation data or instrumental variables. The results can be used in a sensitivity analysis to probe the potential effects of measurement error on the evaluation of treatment effects.     

Functional-coefficient cointegration models

This paper studies estimation and inference of functional coefficient cointegration models. The proposed model offers a more flexible structure of cointegration where the value of cointegrating coefficients may be affected by informative covariates and thus may vary over time. The model may be viewed as a stochastic cointegration model and includes the conventional cointegration model as a special case. The proposed new model provides a useful complement to the conventional fixed coefficient cointegration models. Both kernel and local polynomial estimators are investigated. Inference procedures for instability of cointegrating parameters and a test for cointegration are proposed based on the functional-coefficient estimates. Limiting distributions of the estimates and testing statistics are derived.     

An econometric procedure for estimation of a generalized systemic gravity model under incomplete information about the system

Gravity spatial interaction models have a long history and a wide scope of empirical applications, Alonso (1973) proposed a generalized model whose structure subsumes major versions of the gravity model in the literature as special cases. Little attention has been given to how the general model might be estimated, or if it is estimable at all. This paper develops an econometric procedure for situations where all places in a geographic system are not included in the observations. The general model and various constrained versions are empirically estimated on a sample of intermetropolitan migration data and compared.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.