Search

I suggest a model to forecast life expectancy based on the electrocardiogram signal. I show how the heart electrical activity can be decomposed in the state- space and that the suggested mode! has superior out-of-sample properties compared to a set of alternatives. When the model is used to forecast my own electrocardiogram, a striking result arises: the n-step ahead forecast remains bounded and positive even after one googol period. Therefore, my life expectancy tends to infinity implying that I am immortal. (JEL110,119), I. INTRODUCTION Cardiology is a branch of medicine that studies the functioning of the heart. Since the invention of the electrocardiogram (ECG) by Willem Einthoven in 1903, medical science has [...]

Ending violent international conflicts requires understanding the causal factors that perpetuate them. In the Israeli--Palestinian conflict, Israelis and Palestinians each tend to see themselves as victims, engaging in violence only in response to attacks initiated by a fundamentally and implacably violent foe bent on their destruction. Econometric techniques allow us to empirically test the degree to which violence on each side occurs in response to aggression by the other side. Prior studies using these methods have argued that Israel reacts strongly to attacks by Palestinians, whereas Palestinian violence is random (i.e., not predicted by prior Israeli attacks). Here we replicate prior findings that Israeli killings of Palestinians increase after Palestinian killings of Israelis, but crucially show further that when nonlethal forms of violence are considered, and when a larger dataset is used, Palestinian violence also reveals a pattern of retaliation: (i) the firing of Palestinian rockets increases sharply after Israelis kill Palestinians, and (ii) the probability (although not the number) of killings of Israelis by Palestinians increases after killings of Palestinians by Israel. These findings suggest that Israeli military actions against Palestinians lead to escalation rather than incapacitation. Further, they refute the view that Palestinians are uncontingently violent, showing instead that a significant proportion of Palestinian violence occurs in response to Israeli behavior. Well-established cognitive biases may lead participants on each side of the conflict to underappreciate the degree to which the other side's violence is retaliatory, and hence to systematically underestimate their own role in perpetuating the conflict. doi/ 10.1073/pnas.1012115107

The impact of Nicaraguan cash transfer program, that pays households based on school attendance and family visits to health clinics and seminars, is examined. A difference-in-difference estimation for distinct wealth groups shows that the program improved school enrollment among poor households in coffee-cultivating communities. Insights on the results are also given.

The works of Cliff and Ord have had a major impact on empirical practices in real estate. Cliff and Ord proposed both techniques for detecting as well as modeling spatial [...]

The unit root test is not designed to perform tasks associated with the random walk hypothesis. The test is designed to investigate characteristics of time series and is not a predictability test. The test also imposes a one time structural break in time series, which may bias the results.

Using an econometric case study approach, the various methods employed by economists in evaluating the role played by human resource management practices in improving productivity performance of a retail trade are analyzed.

Econometrics methods, which examine the impact of apprenticeship guidance for skilled workers on gender wage gap and career choice, are presented.

An important feature of panel data is that it allows the estimation of parameters characterizing dynamics from individual level data. Several authors argue that such parameters can also be identified from repeated cross-section data and present estimators to do so. This paper reviews the identification conditions underlying these estimators. As grouping data to obtain a pseudo-panel is an application of instrumental variables (IV), identification requires that standard IV conditions are met. This paper explicitly discusses the implications of these conditions for empirical analyses. We also propose a computationally attractive IV estimator that is consistent under essentially the same conditions as existing estimators. While a Monte Carlo study indicates that this estimator may work well under relatively weak conditions, these conditions are not trivially satisfied in applied work. Accordingly, a key conclusion of the paper is that these estimators cannot be implemented under general conditions. JEL classification: C21; C23; C81 Keywords: Repeated cross-sections; Pseudo-panel data; Cohorts; Instrumental variables; Individual dynamics

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finite-sample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns. JEL classification: C14; C41; C52 Keywords: Duration models; Gamma kernel; Hazard rate; Specification testing

The linear and nonlinear seemingly unrelated regression problem with general error distribution is analyzed using recent likelihood theory that arguably provides the definitive distribution for assessing a scalar parameter; this involves implicit but well defined conditioning and marginalization for determining intrinsic measures of departure. Highly accurate p-values are obtained for the key difference between two regression coefficients of central interest. The p-value gives the statistical position of the data with respect to the key parameter. The theory and the results indicate that this methodology provides substantial improvement on first-order likelihood procedures, both in distributional accuracy, and in precise measurement of the key parameter. JEL classification: C1; C3 Keywords: Asymptotics; Conditioning; Likelihood; p-value; SUR

This paper studies analogs of Granger's representation theorem in the context of a general nonlinear vector autoregressive error correction model. The model allows for nonlinear autoregressive conditional heteroskedasticity and the conditional distribution involved can be a mixture distribution of a rather general type. Mixture models of this kind can be thought of as generalizations of threshold models and they have attracted attention in the recent time series and econometrics literature. The paper develops a useful transformation which shows how the nonlinear error correction model can be transformed to a nonlinear vector autoregressive model so that available results on the stationarity or nonstationarity of the latter can be used for the former. The most satisfactory results are obtained in a model in which a specific structural relation between the nonlinearity and equilibrium correction prevails. Without this structural relation only a lower bound for the number of long-run equilibrium relations can explicitly be determined because the exact number depends on properties of the first and second moments of a nonlinear stationary component of the process. JEL classification: C32 Keywords: Cointegration; Mixing; Nonlinear error correction; Nonstationarity; Stationarity

The article explains why conventional, capital-oriented econometrics are not effective for people businesses, or businesses that have people as their primary resource, as conventional methods can misidentify volatility and/or weak performance. New methods for calculating performance and profit are given.

The defense-growth nexus is investigated empirically using longitudinal data for Guatemala and allowing the effect of defense spending on growth to be nonlinear. Using recently developed econometric methods involving threshold regressions, evidence of a level-dependent effect of military expenditure on GDP growth is found: A positive and significant externality effect of defense spending prevails for relatively low levels of defense spending and becomes negative, albeit insignificant, for higher levels. Key words--defense expenditures, nonlinearity, economic growth, externality effect, Latin America, Guatemala

Theory often specifies a particular cointegrating vector among integrated variables, and testing for a unit root in the known cointegrating vector is often required. Although it is common to simply [...]

In econometrics the same data is usually selected for both model selection and forecasting. The flaws in the methodology are explained and a better alternative, the 'neutral Laplace estimator' is presented for stock market forecasting.

Within a flexible regression model (J.D. Hamilton, Econometrica 69 (3) (2001) 537) we offer a battery of new Lagrange multiplier statistics that circumvent the problem of unidentified nuisance parameters under the null hypothesis of linearity and that are robust to the specification of the covariance function that defines the random field. These advantages are the result of (i) switching from the [L.sub.2] to the [L.sub.1] norm; and (ii) assuming that the random field is sufficiently smooth for its covariance function to be locally approximated by a high order Taylor expansion. A Monte Carlo simulation suggests that our statistics have superior power performance on detecting bilinear, neural network, and smooth transition autoregressive specifications. We also provide an application to the Industrial Production Index of sixteen OECD countries. JEL classification: C12; C15; C22 Keywords: Flexible regression; Random field; Neglected nonlinearity

This paper re-visits the problem of estimating the regression error variance in a linear multiple regression model after preliminary hypothesis tests for either linear restrictions on the coefficients or homogeneity of variances. There is an extensive literature that discusses these problems, particularly in terms of the sampling properties of the pre-test estimators using various loss functions as the basis for risk analysis. In this paper, a unified framework for analysing the risk properties of these estimators is developed under a general class of loss structures that incorporates virtually all first-order differentiable losses. Particular consideration is given to the choice of critical values for the pre-tests. Analytical results indicate that an [alpha]-level substantially higher than those normally used may be appropriate for optimal risk properties under a wide range of loss functions. The paper also generalizes some known analytical results in the pre-test literature and proves other results only previously shown numerically. JEL classification: C12; C13 Keywords: Entropy loss; First-order differentiable; Lebesgue integrable; Linear restrictions; LINEX loss; Quadratic loss; Risk; Variance homogeneity

We extend the maximum likelihood estimation method of Ait-Sahalia (Econometrica 70 (2002) 223) for time-homogeneous diffusions to time-inhomogeneous ones. We derive a closed-form approximation of the likelihood function for discretely sampled time-inhomogeneous diffusions, and prove that this approximation converges to the true likelihood function and yields consistent parameter estimates. Monte Carlo simulations for several financial models reveal that our method largely outperforms other widely used numerical procedures in approximating the likelihood function. Furthermore, parameter estimates produced by our method are very close to the parameter estimates obtained by maximizing the true likelihood function, and superior to estimates obtained from the Euler approximation. JEL classification: C0; C1; C4; G0 Keywords: Maximum likelihood estimation; Time-inhomogeneous diffusion; Transition density; Hermite expansion

Econometric interest in the possibility of long memory has developed, as a flexible alternative to or compromise between the usual short memory or unit root prescriptions, for example in the context of modelling cointegrating or other relationships and in describing the dependence structure of nonlinear functions of financial returns. Semiparametric methods of estimating the memory parameter can avoid bias incurred by misspecification of the short memory component. We introduce a broad class of such semiparametric estimates that also covers pooling across frequencies. A leading 'Box-Cox' sub-class, indexed by a single tuning parameter, interpolates between the popular local log-periodogram and local Whittle estimates, leading to a smooth interpolation of asymptotic variances. The bias of these two estimates also differs to higher order, and we also show how bias, and asymptotic mean-squared error, can be reduced, across the class of estimates studied, by means of a suitable version of higher-order kernels. We thence calculate an optimal bandwidth (the number of low-frequency periodogram ordinates employed) which minimizes this mean-squared error. Finite sample performance is studied in a small Monte Carlo experiment, and an empirical application to intra-day foreign exchange returns is included. JEL classification: C22 Keywords: Long memory; Semiparametric methods; Higher-order kernel; M-estimation; Bias; Mean-squared error

This paper considers index models, such as simple neural network models and smooth transition regressions, with integrated regressors. The models can be used to analyze various nonlinear relationships among nonstationary economic time series. Asymptotics for the nonlinear least squares (NLS) estimator in such models are fully developed. The estimator is shown to be consistent with a convergence rate that is a mixture of [n.sup.3/4], [n.sup.1/2] and [n.sup.1/4] for simple neural network models, and of [n.sup.5/4], n, [n.sup.3/4] and [n.sup.1/2] for smooth transition regressions. Its limiting distribution is also obtained. Some of its components are mixed normal, with mixing variates depending upon Brownian local time as well as Brownian motion. However, it also has nonGaussian components. It is in particular shown that applications of usual statistical methods in such models generally yield inefficient estimates and/or invalid tests. We develop a new methodology to efficiently estimate and to correctly test in those models. A simple simulation is conducted to investigate the finite sample properties of the (NLS) estimators and the newly proposed efficient estimators. JEL classification: C22; C45 Keywords: Index model; Integrated time series; Neural network model; Smooth transition regression; Brownian motion and Brownian local time

We establish relationships between certain Bayesian and classical approaches to instrumental variable regression. We determine the form of priors that lead to posteriors for structural parameters that have similar properties as classical 2SLS and LIML and in doing so provide some new insight, especially in the context of weak instruments, to the small sample behavior of Bayesian and classical procedures. Using a reduced rank restriction on a multivariate linear model, we determine the priors that give rise to posteriors that are identical in functional form to the sampling densities of the classical 2SLS and LIML estimators. JEL classification: C11; C20 Keywords: Diffuse prior; Instrumental variables; Posterior distributions; Reduced rank; Weak instruments

This paper presents a simple algorithm for identifying sub-centres using standard properties of contiguity matrices to simplify the identification procedure. Sub-centres are identified for Atlanta, Baltimore--Washington, Boston, New York and Philadelphia. A sub-centre is defined to be a set of contiguous tracts that each have significantly positive residuals in a first-stage non-parametric regression and that together have at least 10 000 employees. The algorithm is also applicable when potential sub-centre sites are identified by other methods, such as minimum employment density cut-offs.

Self-selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self-selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology that combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell (1993)). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self-selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state-specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state-to-state migration flows of college- versus high school-educated individuals respond strongly to differences in the return to education and amenities across states. KEYWORDS: Selection bias, polychotomous choice, Roy model, return to education, migration.

This article describes a new Monte Carlo algorithm, dynamically weighted importance sampling (DWIS), for simulation and optimization. In DWIS, the state of the Markov chain is augmented to a population. At each iteration, the population is subject to two move steps, dynamic weighting and population control. These steps ensure that DWIS can move across energy barriers like dynamic weighting, but with the weights well controlled and with a finite expectation. The estimates can converge much faster than they can with dynamic weighting. A generalized theory for importance sampling is introduced to justify the new algorithm. Numerical examples are given to show that dynamically weighted importance sampling can perform significantly better than the Metropolis--Hastings algorithm and dynamic weighting in some situations. KEY WORDS: Dynamic weighting; Markov chain Monte Carlo; Metropolis--Hastings algorithm; Sequential importance sampling.

Economics is commonly associated with the use of quantitative methods, and because of this, supposedly, the discipline is more 'rigorous' than other social sciences, which are in turn associated with the application of qualitative methods, presumed to be less rigorous. This paper challenges these assumptions and argues that the more serious distinction, which applies both to quantitative and to qualitative research, is between data analysis and data mining. Opposing the opposition of quantitative and qualitative analyses, the paper shows how a productive synergy may be established both between methods, and between disciplines, using examples from studies of labor in rural Africa, of the relationships of household size and poverty, and of child survival. Key words -- methods, poverty, statistics, econometrics

This paper develops an empirical model of the relationship between road traffic accidents and traffic flows. The analysis focuses on the accident externality, which is determined mainly by the difference between the marginal and average risks. The model is estimated using a new data-set which combines hourly London traffic count data from automated vehicle recorders together with police records of road accidents. The accident-flow relationship is seen to vary considerably between different road classes and geographical areas. More importantly, even having controlled for these and other differences, the accident externality is shown to vary significantly with traffic flows. In particular, while the accident externality is typically close to zero for low to moderate traffic flows, it increases substantially at high traffic flows.

Previous papers estimate base-independent equivalence scales and test base-independence using strict parametric assumptions on Engel curves and equivalence scale functions. These parametric tests reject the hypothesis of base independence. I construct a semiparametric estimator of a household equivalence scale under the assumption of base independence without putting any further restrictions on the shape of household Engel curves. This estimator uses cross-equation restrictions on a system of estimated nonparametric engel curves to identify equivalence scale parameters. I test the hypothesis of base independence against a fully nonparametric alternative and find that preferences are consistent with the existence of a base-independent equivalence scale for some interhousehold comparisons. Keywords: Equivalence scales; Semiparametric estimation; Household demand

Steady states and transient states of the inventory problem with built-in forecast updates are characterized and results are evaluated using dynamic programming. Steady states are shown to posses convexity, limits and boundedness with the myopic policy proven to be near-optimal. Dynamic programming solutions on two accuracy levels show that optimal solutions turn out to be one of the two integers closest to the myopic.

A product representation technique featuring products of binomial trees for use in simultaneous management of several risky assets is presented and evaluated. The technique goes around the disproportional representation of products by presenting a condition of the marginal utility being optimally independent thus resulting in the proper defining of risk neutral probabilities in the product tree.

A comparison of Newton-based techniques and first order iterations used in solving nonlinear forward-looking models reveals that Newton-based techniques are considerably faster and less prone to simulation failure. The study compares the two techniques by solving them on MULTIMOD and finds that the Newton-Raphson iterative method is better than the Fair-Taylor algorithm.

An analysis of Krylov method use in simulations of large macroeconomic models with forward-looking variables reveals that the methods feature low computational complexity and low storage requirements. The study determines an alternative to the exact Newton method with sparse Gaussian elimination and finds that inexact Newton algorithms using Krylov methods viable alternatives.

An analysis of monotonicity in comparative statics reveals that there are sufficient conditions where strict monotone comparative statics conclusions in optimization problems may be drawn with and without binding constraints. Imposing a stronger differential version of the single crossing property and arguing first-order conditions would enable the results to be extended to the Lattice-theoretic results of Milgrom and Shannon. These results are of particular importance in holdup problems, output decisions, optimal tax problems and modern manufacturing.

An analysis of the resulting equilibria in a simple one-commodity model of the overlapping generations version of the Shapley-Shubik market game reveals that complex and chaotic equilibrium dynamics are possible. There is also an alternative to the technology-based explanation for the historical fact that the onset of industrialization brings about business cycles.

An analysis of the mechanisms for solving cost-sharing problems with homogenous cost functions reveals that using an Ordinality Axiom, the mechanisms may be extended to arbitrary problems. The axiom requires all cost shares to invariant under all increasing transformations of the measuring scales and that the Aumann-Shapley extension of the proportional mechanisms is not ordinal.

An analysis of strategic information gathering before a principal offers a contract reveals that the agent's equilibrium strategy will be mixed with the probability of the agent acquiring information strictly between 0 and 1. The optimal contract the principal will offer an agent may be in the form of a contract where an uninformed agent obtains rent and may be asked to overproduce. It can also be a contract wherein the agent's production schedule is severely distorted, or a contract wherein it may be too costly to separate an informed from an uninformed agent.

An analysis of the role of consumer durables in the generation and propagation of business cycles reveals that consumer durables can be modelled in a dynamic general equilibrium model with indeterminacy. This model is capable of generating the degree of persistence without the introduction of serial correlations. It is also shown that returns to scale can be of very low size in multisector models and yet able to generate fully specified general models with indeterminacy.

An analysis of multisectoral models with constant variable returns-to-scale and very small market imperfections reveals that indeterminacy may easily arise. Standard parametrizations may be used to calibrate the constant returns model with sunspots to produce moments of aggregate consumption, investment output and employment that are closely matched to US data.

Protection against asymmetric shocks in global economies can be provided through a complex insurance scheme. This scheme, derived from a complex econometric formula based on historical data, is needed now that the exchange rate has been lost as a policy instrument due to the European monetary union and the euro currency. Simplifying the econometric formula makes the scheme less viable as a protective measure.

An application of the multinomial logit model on coffee purchase behavior based on size and brand given that the customer as made a category selection reveals that the model tracks sales well in a hold out sample. Explanatory variables in the model include store actions such as price and promotion and customer characteristics such as brand size and loyalty and results also show that sales tracking in the hold-out sample is superior for both aggregate and and individual levels.

A model of purchase quantity effects on consumer choice of product assortment is presented and evaluated against a null model with constrained purchase quantity and no effects of flavour choice. Results of the evaluation reveals that the alternate model is better than the null model in terms of data fit with the estimated parameter strongly significant and correctly signed.

An analysis of the problem of forecasting ongoing factory orders reveals that a good data marketing warehouse and the use of some rigorous statistical models can help reduce the 'bullwhip effect' that limits predictability. The study reveals that marketing and forecasting functions should be integrated in order for market-response models to be used in a proactive manner.

An affordability model for explaining the growth of new consumer durables is presented and compared against the more popular Bass diffusion model. The alternate model is based on affordability and evaluation results show that the alternate model is slightly inferior in terms of fit but is superior to the Bass diffussion model in terms of parameter stability, parameter validity and forecasting ability.