Income Distributive Effects in the Brazilian Economy

Studies aiming to evaluate the structural distribution of economic impacts usually treat consumption demand as an exogenous variable. In this case, the Leontief matrix multiplier lacks the multiplier process via the consumption function that one customarily finds in a Keynesian model. To regard the consumption as a fictitious production activity is not the appropriate procedure. Instead, the Keynesian consumption function is introduced at a disaggregated level. For that, a matrix multiplier was formulated in order to combine Leontief's propagation process with the Keynesian propagation process. This matrix includes the effects of endogenous changes in consumption demand. Based on the present production structure in Brazil, the results show how the propagation effect directs the induced income towards capitalists, depriving wage earners. The model also allows for evaluation of diverse effects of the propagation process according to income and consumption coefficients by sector.     

Technical Change in the Private Consumption Converter

The analysis of technological change is centered on the study of the evolution of technical coefficients in the input–output table. Complementary to this analysis, the household consumption expenditure matrix, relating consumption by commodities to consumption by purpose or by function, also incorporates some other aspects of technological change. Thus, the evolution in time of the coefficients of this consumption expenditure matrix will portray technological processes, implying substitutions between commodities to satisfy the different functions The substitution between consumption expenditure by functions is also to be taken into consideration, because it can influence, together with technological change, the use of commodities in the final demand. For Switzerland, a 1980–89 time series of household consumption expenditure matrices with 37 commodities and 58 functional consumption categories has been estimated using data from consumer expenditure surveys. In this paper, instruments generally applied to the analysis of changes in input–output technical coefficients are extended to these matrices, including methods that deal with biproportional processes of substitution.     

Spatial differentiation,divisible consumption and the pro-competitive effect of income

We analyze a spatial differentiation model with divisible consumption under one-stop shopping. Each consumer who visits only one store, chooses the quantities of the goods which maximize his/her utility function under the budget constraint (namely consumption expenditures must equal income minus transportation costs), choosing the store which provides him/her with the largest indirect utility. We derive the equilibrium price when the firms are located at the two extremities of Hotelling’s linear city and show that income increases have a pro-competitive effect.     

Input–Output Analysis,Labour Scarcity and Relative Price Endogeneity: Aggregate Demand Disturbances in a ‘Flex-price’ Leontief System

In this paper, we introduce scarcity into a conventional demand-driven input–output system to produce a ‘flex-price’ Leontief model. We retain the fixed technical coefficients but allow changes in relative prices to reflect variations in the real wage. Because the consumption coefficients increase with the real wage, the aggregate labour demand curve is found to be upward sloping. This produces conventional results, as long as the labour supply curve is perfectly elastic (horizontal). However, once we introduce labour scarcity, in the form of an upward sloping labour supply function, we derive the seemingly paradoxical result that the output and employment effects of a demand expansion are greater here than in conventional input–output. Through simulation, this result is found to be strongly dependent on the assumption of export exogeneity.     

Statistical properties of data stretching

This research examines the properties of an estimation procedure frequently used because observations on some variables are available only at higher levels of aggregation than others. When this occurs, data are often stretched by repeating observations on variables at higher levels of aggregation. We show that this procedure results in biased estimators of coefficients and error variances. Under some circumstances the estimation based on stretched data has a smaller covariance matrix than that based on aggregated data. Comparisons of mean squared errors depend on unknown coefficients.     

Smoothing demand by aggregation with respect to wealth

We suggest in this paper to treat the problem of smoothing demand by aggregation in a two-step procedure, corresponding to the two different constituents of consumption characteristics, wealth and preferences. Instead of imposing a manifold structure on preferences we exploit the nice structure of wealth-space. The first step of this procedure, aggregation with respect to wealth, is carried out. It is shown that, for any preference, aggregation with respect to wealth yields a mean demand which is almost everywhere C¹. Moreover, it is shown that for an important class of preferences, vanishing Gaussian curvature of indifference surfaces does not destroy differentiability of the mean demand function.     

A robust approach to estimating production functions: Replication of the ACF procedure

We study Ackerberg, Caves, and Frazer's (Econometrica, 2015, 83, 2411–2451; hereafter ACF) production function estimation method using Monte Carlo simulations. First, we replicate their results by following their procedure to confirm the existence of a spurious minimum in the estimation, as noted by ACF. In the population, or when sample sizes are sufficiently large, this “global” identification problem may not be a concern because the spurious minimum occurs only at extreme values of capital and labor coefficients. However, in finite samples, their estimator can produce estimates that may not be clearly distinguishable from the spurious ones. In our second experiment, we modify the ACF procedure and show that robust estimates can be obtained using additional lagged instruments or sequential search. We also provide some arguments for why such modifications help in the ACF setting.     

Partially varying coefficient instrumental variables models

In this article, we study a new class of semiparametric instrumental variables models, in which the structural function has a partially varying coefficient functional form. Under this specification, the model is linear in the endogenous/exogenous components with unknown constant or functional coefficients. As a result, the ill‐posed inverse problem in a general non‐parametric model with continuous endogenous variables can be avoided. We propose a three‐step estimation procedure for estimating both constant and functional coefficients and establish their asymptotic properties such as consistency and asymptotic normality. We develop consistent estimators for their error variances. We demonstrate that the constant coefficient estimators achieve the optimal ‐convergence rate, and the functional coefficient estimators are oracle. In addition, efficiency issue of the parameter estimation is discussed and a simple efficient estimator is proposed. The proposed procedure is illustrated via a Monte Carlo simulation and an application to returns to education.     

Nonparametric tests of collectively rational consumption behavior: An integer programming procedure

We present an IP-based nonparametric (revealed preference) testing procedure for rational consumption behavior in terms of a general collective model, which includes consumption externalities and public consumption. An empirical application to data drawn from the Russia Longitudinal Monitoring Survey (RLMS) demonstrates the practical usefulness of the procedure. Finally, we present extensions of the testing procedure to evaluate the goodness-of-fit (accounting for optimization error as well as measurement error) of the collective model subject to testing.     

Bootstrapping realized multivariate volatility measures

We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as realized regression, covariance and correlation coefficients. We show that the finite sample performance of the bootstrap is superior to the existing first-order asymptotic theory. Nevertheless, and contrary to the existing results in the bootstrap literature for regression models subject to error heteroskedasticity, the Edgeworth expansion for the pairs bootstrap that we develop here shows that this method is not second-order accurate. We argue that this is due to the fact that the conditional mean parameters of realized regression models are heterogeneous under stochastic volatility.     

Semiparametric estimation of consumer demand systems in real expenditure

Microdata concerning consumer demand typically show considerable variation in real expenditures, but very little variation in prices. We propose a semiparametric strategy for the consumer demand problem in which expenditure share equations are estimated nonparametrically in the real expenditure direction and estimated parametrically (with fixed or varying coefficients) in price directions. In our model, Engel curves are unrestricted: demands may have any rank. Because the demand model is derived from a cost function, it may be restricted to satisfy integrability and used for consumer surplus calculations. Since real expenditure is unobserved, but rather estimated under the model, we face a semiparametric model with a nonparametrically generated regressor. We show efficient convergence rates for parametric and nonparametric components. We illustrate the feasibility of our proposed strategy using Canadian expenditure and price data: Engel curves display curvature which cannot be encompassed by standard parametric models. We also find that the rationality restriction of Slutsky symmetry is rejected in the fixed‐coefficients model, but not in the varying‐coefficients model. Copyright © 2009 John Wiley & Sons, Ltd.     

A closed-form solution for the continuous-time consumption model with endogenous labor income

In this paper, we study the consumption, labor supply, and portfolio decisions of an infinitely lived individual who receives a certain wage rate and income from investment into a risky asset and a risk-free bond. Uncertainty about labor income arises endogenously, because labor supply evolves randomly over time in response to changes in financial wealth. We derive closed-form solutions for optimal consumption, labor supply and investment strategy. We find that deferring the retirement age stimulates optimal consumption over time and discourages optimal labor supply during the working life. We also find explicitly that optimal portfolio allocation becomes more ‘conservative’ when the individual approaches his prescribed retirement age. The effects of risk-aversion coefficients on optimal decisions are examined.     

Identifying Structural Breaks in Cointegrated Vector Autoregressive Models*

This article suggests an alternative formulation of the cointegrated vector autoregressive (VAR) model such that the coefficients for the deterministic terms have straightforward interpretations. These coefficients can be interpreted as growth rates and cointegration mean level coefficients and express long‐run properties of the model. For example, the growth rate coefficients tell us how much to expect (unconditionally) the variables in the system to grow from one period to the next, representing the underlying (steady state) growth in the variables. The estimation of the proposed formulation is made operationally in GRaM, which is a program for Ox Professional. GRaM can be used for analysing structural breaks when the deterministic terms include shift dummies and broken trends. By applying a formulation with interpretable deterministic components, different types of structural breaks can be identified. Shifts in both intercepts and growth rates, or combinations of these, can be tested for. The ability to distinguish between different types of structural breaks makes the procedure superior compared with alternative procedures. Furthermore, the procedure utilizes the information more efficiently than alternative procedures. Finally, interpretable coefficients of different types of structural breaks can be identified.     

A two-stage procedure for partially identified models

This paper studies a two-stage procedure for estimating partially identified models, based on Chernozhukov, Hong, and Tamer’s (2007) theory of set estimation and inference. We consider the case where a sub-vector of parameters or their identified set can be estimated separately from the rest, possibly subject to a priori restrictions. Our procedure constructs the second-stage set estimator and confidence set by taking appropriate level sets of a criterion function, using a first-stage estimator to impose restrictions on the parameter of interest. We give conditions under which the two-stage set estimator is a set-valued random element that is measurable in an appropriate sense. We also establish the consistency of the two-stage set estimator.     

Predictive regression under various degrees of persistence and robust long-horizon regression

The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robust chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon predictive regression. Existing methods in this model, which rely on simulated critical values by inverting tests under local to unity conditions, cannot be easily extended beyond the scalar regressor case or to wider autoregressive parametrizations. In contrast, the methods developed here lead to standard chi-square tests, allow for multivariate regressors, and include predictive processes whose roots may lie in a wide vicinity of unity. As such they have many potential applications in predictive regression. In addition to asymptotics under the null hypothesis of no predictability, the paper investigates validity under the alternative, showing how balance in the regression may be achieved through the use of localizing coefficients and developing local asymptotic power properties under such alternatives. These results help to explain some of the empirical difficulties that have been encountered in establishing predictability of stock returns.     

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.     

带多个变点的逐段连续线性分位数回归模型及应用

研究目标：建立具有多个变点的逐段连续线性分位数回归模型（Continuous Piecewise Linear Quantile Regression with Multiple Change Points,CPLQR）。研究方法：先通过LASSO和广义贝叶斯信息准则确定变点个数,再通过线性化技巧来估计变点的位置与回归系数。研究发现：新方法能够同时确定变点个数、估计变点位置和回归系数,而且具有较强的稳健性;应用该方法于年龄和身体质量指数之间关系,进一步证实了模型的实用性。研究创新：新方法能够处理多个变点的问题,通过LASSO和广义贝叶斯信息准则确定变点数目,避免了主观判断的弊端;借助线性化技巧,解决了目标函数在变点处不可导问题。研究价值：本文结果将为分析经济、金融、医药和生物等学科中存在结构变化的数据提供强有力的研究工具。     

Tax Discounting in a High‐debt Economy*

This paper estimates a consumption function for Belgium that allows for government debt discounting and for the overall discounting of the future (reflecting the consumers’ planning horizon or precautionary savings). It also allows for substitutability or complementarity effects from government expenditures. Results suggest that consumers do take into account (future) government activity. Ricardian Equivalence is rejected however, since we cannot reject a relatively short planning horizon or a precautionary savings motive for the consumers. We use bootstrapped distributions for inference since the instrumental variables estimators used may have non‐standard distributions. This procedure also helps to tackle potential endogeneity and sample size problems.     

Semiparametric quantile regression estimation in dynamic models with partially varying coefficients

We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both parametric and nonparametric functional coefficients are proposed. In particular, we propose a three stage semiparametric procedure. Both consistency and asymptotic normality of the proposed estimators are derived. We demonstrate that the parametric estimators are root-n$n$ consistent and the estimation of the functional coefficients is oracle. In addition, efficiency of parameter estimation is discussed and a simple efficient estimator is proposed. A simple and easily implemented test for the hypothesis of a varying-coefficient is proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimators.     

Health insurance and savings over the life cycle—a semiparametric smooth coefficient estimation

Individuals save for future uncertain health care expenses. This is less efficient than pooling health risk through insurance. The provision of comprehensive health insurance may raise welfare by providing the missing market to smooth out consumption through the life cycle. We employ a semiparametric smooth coefficient model to examine the effects of the introduction of the National Health Insurance in Taiwan in 1995 on savings and consumption over the life cycle. The idea is to estimate the coefficients of health insurance which vary with age. Our results suggest that younger households are more sensitive to the risk reductions, and that they demonstrate a greater response in the reduction of their precautionary saving. Copyright © 2004 John Wiley & Sons, Ltd.