Violent crime and punishment: an application of the LISREL model

Public anxiety over violent crime has forged a broad consensus to purchase more crime control. Recently, the President brought into law a federal anti-crime bill that will put an additional 100 000 police on the nation's streets. This paper presents empirical evidence on the deterrence effect of police presence. The principal finding is that police presence deters commission of violent crimes by increasing the risk of being punished for committing those crimes. This risk is modelled as a latent variable, because the probability and severity of punishment for committing a violent crime are generally communicated in an onconsistant fashion. The violent crime commission rate is also treated as a latent variable, because it is almost certainly afflicted with measurement error.     

Modelling milk purchasing behaviour with a panel data double-hurdle model

In this study, the double-hurdle model typically used in cross-sectional data is extended to panel data structures. The new double-hurdle model can account not only for the censored nature of commodity purchases, but also for the dynamics of the purchase process. In this model, a flexible error structure is assumed to account for state dependence and household-specific heterogeneity. In the empirical application for milk purchases, it is found that generic advertising increases the probability of market participation as well as the purchase quantity and incidence. Temporal dependence is also found in both purchase and participation equations.     

A Keynesian general equilibrium model with competitive firms and rational expectations

A Keynesian general equilibrium model is developed from neoclassical principles. The model is based on competitive firm behavior, and optimizing agents that form expectations rationally. Firms determine their product price to maximize expected profits. Non-neutrality results follow from micro foundations that view firms as committing to a price and output level before actual demand is observed. It follows that optimal output levels are in part determined by demand conditions. In the general equilibrium framework, increases in government spending lead to welfare-improving increases in aggregate output.I thank Tom Cosimano, Strat Douglas, Douglas Gale, Norm Miller, Nick Rowe, Geoffrey Woglom, and two anonymous referees for valuable comments. The responsibility for potential errors remains entirely my own.     

Invariance of conditional maximum utility

The consumer benefit in a discrete choice model is often measured by maximum utility. We characterize the conditional (on the chosen alternative) and the unconditional distribution of maximum utility. We show that among a wide class of distributions (independent with convex supports) of error terms, the Type I extreme-value distribution is the unique distribution which ensures that all the conditional distributions of maximum utility coincide. Moreover, we show that for i.i.d. (with convex support) error terms, the invariance of conditional expected maximum utility characterizes the multinomial logit model.     

Impact of unemployment on happiness in the United States

This study considers the level of demonstrated happiness and unhappiness, the latter measured by the conditional probability of committing suicide within groups that are facing a higher unemployment rate and those that are not. Using individual-level US data from 1989 to 2004, our findings indicate that individuals have lower rates of suicide or are ‘happy’ when they live in a state that has lower unemployment rate.     

The predictive value of subjective labour supply data: A partial-adjustment model with measurement error

This paper tests the predictive value of subjective labour supply data for adjustments in working hours over time. The idea is that if subjective labour supply data help to predict working hours, the subjective data must contain at least some information on individual labour supply preferences. In this paper, I formulate a partial-adjustment model that allows for measurement error in the observed variables. Applying estimation methods that are developed for dynamic panel data models, I find evidence for a predictive power of subjective labour supply data concerning desired working hours in the German Socio-Economic Panel 1988–1995.I wish to thank John Haisken-DeNew, Astrid Kunze, Markus Pannenberg, Winfried Pohlmeier, Frank Windmeijer, Rainer Winkelmann, my former colleagues at IZA and two anonymous referees for their valuable comments. The author gratefully acknowledges DIW for providing the data.First version received: December 2001/Final version received: December 2003     

Student demographics,school working conditions,and teacher mobility

I analyse how the inclusion of working condition data influences the estimated marginal effects of student demographics on teacher mobility. Using detailed administrative data on public schools, combined with unique data describing teachers’ perceptions of working conditions, I estimate a model characterizing the multinomial outcomes of teachers choosing to work at different schools. By comparing the estimated marginal effects of student characteristics with and without incorporating detailed working conditions, I find that excluding these additional data results in overestimating the effect of student characteristics on the probability a teacher moves to a different school, but has no influence on estimated effects for the probability of leaving teaching altogether.     

On Liu estimators for the logit regression model

This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of the commonly used maximum likelihood (ML) method becomes inflated when the explanatory variables of the regression model are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating it are proposed. It is shown by means of Monte Carlo simulations that the estimated MSE and mean absolute error (MAE) are lower for the proposed Liu estimator than those of the ML in the presence of multicollinearity. Finally the benefit of the Lie estimator is shown in an empirical application where different economic factors are used to explain the probability that municipalities have net increase of inhabitants.     

A note on asymmetric and mixed strategy equilibria in the search-theoretic model of fiat money

Summary. The simple search-theoretic model of fiat money has three symmetric Nash equilibria: all agents accept money with probability 1; all agents accept money with probability 0; and all agents accept money with probability y in (0,1). Here I construct an asymmetric pure strategy equilibrium, payoff-equivalent to the symmetric mixed strategy equilibrium, where a fraction N in (0,1) of agents always accept money and 1-N never accept money. Counter to what has been conjectured previously, I find N > y. I also introduce evolutionary dynamics, and show that the economy converges to monetary exchange iff the initial proportion of agents accepting money exceeds N. Received: September 10, 1997; revised version: April 24, 1998     

A market test for natural monopoly in local exchange

Is the incumbent local exchange carrier (LEC) a natural monopolist? The analysis indicates that the LEC cost function is not supportable: LECs appear to be either nonsustainable natural monopolies or non natural monopolies over the set of services they currently offer. However, recent technological changes suggest that whether LECs are natural monopolies over their current offerings is immaterial for policy making, because competitors are enjoying economies of scope in offering both LEC and non LEC services. Based on these results, deregulation of LECs is discussed in a Type I/Type II error framework.     

Moran's I test of spatial panel data model — Based on bootstrap method

Under the condition of the finite sample or the unknown distributed error term, testing for spatial dependence in panel data models is an unresolved problem in spatial econometrics. In this paper, a fast double bootstrap (FDB) method is used to construct bootstrap Moran's I tests for Moran's I test in spatial panel data models, and Monte Carlo simulation experiments are used to prove the effectiveness from two aspects including size distortion and power. The experiment results show that, in asymptotic Moran's I test, there is serious size distortion, which could be rectified in bootstrap Moran's I test.     

Optimal Universal and Categorical Benefit Provision with Classification Errors and Imperfect Enforcement

We determine the optimal combination of a universal benefit and categorical benefit when individuals differ in their ability to work and, if able to work, their productivity. The categorical benefit is conditioned ex ante on applicants being unable to work and ex post on recipients not working. The awards test makes Type I/II errors. If the ex post condition is (i) not enforced, the optimal categorical benefit is positive only if the awards test has discriminatory power, while maximum welfare falls with both error propensities; but if (ii) fully enforced, the optimal categorical benefit is positive always and maximum welfare can increase with the Type II error propensity.     

Comments on ‘Forecasting with a real-time data set for macroeconomists’

The effects of data revision as removing measurement error on forecast model building is examined. I show various effects on model building using revised or instead real-time data. These effects include lag length selection and measured persistence. I then argue that in practice that one should include the entire dataset available (real-time and past revised data) in forecast construction.     

An error correcting model of farmland prices

Granger has demonstrated that if two variables are integrated of order one and are conitegrated, they can be modelled as having been generated by an error correction mechanism. Cointegration and error correction systems allow long-run components of variables to obey equilibrium constraints while short-run components are allowed to have flexible dynamic specification. Using data from five Corn Belt States in the US, we show that farmland prices and cash rents are each integrated of order one and are model is estimated. The error correction term is found to be significant in both equations, indicating Granger causality running in both directions. Historical simulation of the model is performed. The price equation peroformed better although both equations simulate reasonably well.     

Breaking the law when others do: A model of law enforcement with neighborhood externalities

A standard assumption in the economics of law enforcement is that the probability of a violator being punished depends only on the resources devoted to enforcement. However, it is often true that the productivity of enforcement resources decreases with the number of violators. In this paper, an individual who violates the law provides a positive externality for other offenders because the probability of being punished decreases with the number of individuals violating the law. This externality explains the existence of correlation between individuals’ decisions to break a law. The model evaluates the implications when determining the socially optimal enforcement expenditure, focusing specifically on the case of neighborhood crime. In particular, using a parametrized functional form, I show that neighborhood externalities will enhance or impede enforcement, depending on the crime rate.     

Errors in self-reported earnings: The role of previous earnings volatility and individual characteristics

I report the measurement error in self-reported earnings for a developing country using a novel data set. The data set consists of two cross-sections of the Federated States of Micronesia (FSM) wage and salary sectors; additionally, a subset of the two cross-sections may be linked to create a panel. Administrative data from FSM Social Security office are matched to the FSM Census data for the wage and salary sectors. I find that the error in annual self-reported earnings is centered on zero. Additionally, I find strong evidence for mean reversion in the data suggesting non-classical measurement error. I identify the impact of prior years' earnings variability on the current reporting of earnings using administrative data on earnings histories. Prior earnings volatility strongly affects measurement error in current period. However, the effect of prior shocks diminish significantly over time—suggesting that first-differencing and fixed-effects techniques will not improve accuracy.     

OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.Additionally, there are two more drawbacks to past time-series models: (1) some assumptions (Bollerslev, 1986; Engle, 1982) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices.To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets.In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.     

Errors in variables in a random-effects probit model for panel data

In this paper we consider a probit model estimated on panel data with random person-specific effects when an independent variable is measured with error. For normally distributed measurement error, a structural maximum-likelihood estimate from a likelihood that is conditional on the observed independent variable but marginal on the incidental parameter is studied. A minimum-distance estimator without the use of external instruments is proposed.     

The determinants of child mortality in the Philippines: estimation of a structural model

"This paper employs a longitudinal data set from the Philippines to estimate a structural model of the determinants of infant mortality. To control for the endogeneity of some explanatory variables, a set of reduced form equations for these outcomes is estimated simultaneously with the mortality hazard. A non-parametric discrete factor method is used to control for common unobservable factors in the error terms. Birth interval and parity lose explanatory power once biological measures are considered in the model. The results indicate significant correlations across the equations' error terms. The significant effects of breastfeeding and growth on mortality are quantified in simulation experiments." The data are from a longitudinal survey conducted in metropolitan Cebu between 1983 and 1986.     

A generalized additive Tobit model

This paper proposes a semi-parametric approach to estimation in Tobit models. A generalized additive Tobit model of residential local long distance (intra-LATA) telephone demand is estimated on a cross-section of residential telephone consumers across twenty-eight states. While past studies of telecommunications demand have used fully parametric models, the model presented here is non-parametric in two dimensions: first no distributional assumption is made for the error distribution, and second, the demand equation is non-parametric with respect to price. We find that the elasticity of demand is substantially lower (in absolute value) that found in previous studies for a 40% cut in tariffs. First version received: July 2000/Final version received: March 2001 RID="*" ID="*"  I thank the referee and Associate Editor for suggestions which improved the paper. The views expressed here are of the author and not Analysis Group | Economics.