Median‐voter Equilibria in the Neoclassical Growth Model under Aggregation*

We study a dynamic version of Meltzer and Richard's median‐voter model where agents differ in wealth. Taxes are proportional to income and are redistributed as equal lump‐sum transfers. Voting occurs every period and each consumer votes for the tax that maximizes his welfare. We characterize time‐consistent Markov‐perfect equilibria twofold. First, restricting utility classes, we show that the economy's aggregate state is mean and median wealth. Second, we derive the median‐voter's first‐order condition interpreting it as a tradeoff between distortions and net wealth transfers. Our method for solving the steady state relies on a polynomial expansion around the steady state.     

MC's for MCMC'ists

We develop a minimum amount of theory of Markov chains at as low a level of abstraction as possible in order to prove two fundamental probability laws for standard Markov chain Monte Carlo algorithms:
1. The law of large numbers explains why the algorithm works: it states that the empirical means calculated from the samples converge towards their "true" expected values, viz. expectations with respect to the invariant distribution of the associated Markov chain (=the target distribution of the simulation).
2. The central limit theorem expresses the deviations of the empirical means from their expected values in terms of asymptotically normally distributed random variables. We also present a formula and an estimator for the associated variance.     

A useful tool for forecasting the Euro-area business cycle phases

Based on a novel extension of existing multivariate Markov-switching models, we provide the reader with a useful tool for analyzing current business conditions and making predictions about the future state of the Euro-area economy in real time. Apart from the Industrial Production Index, we find that the European Commission Industrial Confidence Indicator, which is issued with no delay, is very useful for constructing the real-time predictions.     

Stationary measures for some Markov chain models in ecology and economics

Summary. Let continuous, exists in for x in . Let be an i.i.d. sequence from F and X₀ be a nonnegative random variable independent of . Let be the Markov chain generated by the iteration of random maps by . Such Markov chains arise in population ecology and growth models in economics. This paper studies the existence of nondegenerate stationary measures for {X_n}. A set of necessary conditions and two sets of sufficient conditions are provided. There are some convergence results also. The present paper is a generalization of the work on random logistics maps by Athreya and Dai (2000).Received: 20 March 2002, Revised: 4 December 2002, JEL Classification Numbers: C22, D9.The author wishes to thank Professor Mukul Majumdar and the referees for several useful suggestions.     

Infant-industry tariff protection with pressure groups

This paper analyzes the increasing tariff protection in the Spanish iron and steel industry over the first third of the 20th Century. Learning effects are explicitly included to model a dynamic game of trade liberalization. The government chooses the tariff level while firms decide how much to produce each period. Firms' production decisions determine their future cost levels. Assuming that learning reduces only fixed costs, the dynamic game may be solved in closed form, so that the optimality and time consistency of the actual policy can be evaluated. Furthermore, the model is used to measure the relative importance of producers and consumers on the government's equilibrium tariff strategy. The model is calibrated for year 1913 and it is shown that the existence of important, unexploited, dynamic economies of scale may have justified high tariff levels at that time. In addition the results also show that the Spanish iron and steel producers behaved more competitively than what is commonly assumed, and that the government's protection policy was not significantly conditioned by steel producers.     

平滑转换自回归模型的平稳性问题研究

根据时间序列宽平稳的定义,本文认为,平滑转换自回归模型的序列不是宽平稳序列,利用ADF统计量检验其平稳性是没有意义的;其次,依据马尔科夫链的遍历性,我们认为,STAR模型的序列是严平稳序列,且通过对模型系数的联合取值的限制保证了模型的平稳性。以一阶对数平滑转换自回归模型为例,其平稳的条件是,β与r符号相反,且|β+r|<1,β可以等于1,也可以绝对值小于1。     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.     

Can an Unemployment Insurance System Generate Multiple Natural Rates?

This article studies some macroeconomic consequences of the financing of an unemployment insurance scheme. Under a balanced-budget rule, when both taxes and unemployment benefits are proportional to wages, the existence of multiple natural rates of unemployment is a generic property of the matching model. Government can lead the economy to a high equilibrium by fixing the rate of tax on wages and then setting the replacement ratio so that its expenditure matches its receipts.     

Generalized densities of order statistics

Let X ₁, ... , X _n be independent identically distributed random variables with distribution F . We derive expressions for generalized joint 'densities' of order statistics of X ₁, ... , X _n , for arbitrary distributions F , in terms of Radon–Nikodym derivatives with respect to product measures based on F . We then give formulae for conditional distributions of order statistics and use them to derive results concerning Markov properties of order statistics, formulae for distributions of trimmed sums, and other useful representations. Our approach leads to simple and natural expressions which appear not to have been given before.