Combining historical and preliminary information to obtain timely time series data

A method is presented to improve the precision of timely data, which are published when final data are not yet available. Explicit statistical formulae, equivalent to Kalman filtering, are derived to combine historical with preliminary information. The application of these formulae is validated by the data, through a statistical test of compatibility between sources of information. A measure of the share of precision of each source of information is also derived. An empirical example with Mexican economic data serves to illustrate the procedure.     

Rationality,specification tests,and macroeconomic models

Rational expectation models embody cross-equation restrictions that are implied by the theory of rational expectations. In this paper we illustrate how tests of these restrictions may be implemented in terms of general macroeconomic models by employing the models of Taylor and Sargent as examples. In addition, the more important issue of the proper interpretation of the results of these tests is addressed. We contend that tests for rationality should become part of the model-building process as they are akin to specification tests for models in which rational expectations is treated as the maintained hypothesis. A procedure is suggested when the restriction are inconsistent with data. Special emphasis is placed upon examining how changes in specifications of the model's exogenous variables can influence test results.     

Testing and imposing Slutsky symmetry in nonparametric demand systems

Maximization of utility implies that consumer demand systems have a Slutsky matrix which is everywhere symmetric. However, previous non- and semi-parametric approaches to the estimation of consumer demand systems do not give estimators that are restricted to satisfy this condition, nor do they offer powerful tests of this restriction. We use nonparametric modeling to test and impose Slutsky symmetry in a system of expenditure share equations over prices and expenditure. In this context, Slutsky symmetry is a set of nonlinear cross-equation restrictions on levels and derivatives of consumer demand equations. The key insight is that due to the differing convergence rates of levels and derivatives and due to the fact that the symmetry restrictions are linear in derivatives, both the test and the symmetry restricted estimator behave asymptotically as if these restrictions were (locally) linear. We establish large and finite sample properties of our methods, and show that our test has advantages over the only other comparable test. All methods we propose are implemented with Canadian micro-data. We find that our nonparametric analysis yields statistically significantly and qualitatively different results from traditional parametric estimators and tests.     

Testing overidentifying restrictions with many instruments and heteroskedasticity

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the overidentifying test statistic. Correct asymptotic critical values are derived for this statistic when the number of instruments grows large, at a rate up to the sample size. It is also shown that the test is valid when the number of instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. This paper finds in Monte Carlo studies that the test is more accurate and less sensitive to the number of instruments than the Hausman–Sargan or GMM tests of overidentifying restrictions.     

Specification tests based on the heterogeneous generalized gamma model of duration: With an application to Kennan's strike data

In this paper, tests for neglected heterogeneity and functional form misspecification of some commonly used parametric distributions are derived within a heterogeneous generalized gamma model. It is argued that the conventional test of heterogeneity may not be valid when the underlying hazard function is misspecified. Hence, if the estimated hazard function is deemed restrictive, tests for functional form misspecification should accompany any test of heterogeneity. An empirical illustration based on Kennan's (1985) model of strikes is used to show that incorrect inferences may be drawn, as in a number of previous analyses, if the relevant restrictions are not tested jointly.     

Robust Non‐nested Testing for Ordinary Least Squares Regression when Some of the Regressors are Lagged Dependent Variables*

The problem of testing non‐nested regression models that include lagged values of the dependent variable as regressors is discussed. It is argued that it is essential to test for error autocorrelation if ordinary least squares and the associated J and F tests are to be used. A heteroskedasticity–robust joint test against a combination of the artificial alternatives used for autocorrelation and non‐nested hypothesis tests is proposed. Monte Carlo results indicate that implementing this joint test using a wild bootstrap method leads to a well‐behaved procedure and gives better control of finite sample significance levels than asymptotic critical values.     

Testing for non-nested conditional moment restrictions using unconditional empirical likelihood

We propose non-nested hypothesis tests for conditional moment restriction models based on the method of generalized empirical likelihood (GEL). By utilizing the implied GEL probabilities from a sequence of unconditional moment restrictions that contains equivalent information of the conditional moment restrictions, we construct Kolmogorov–Smirnov and Cramér–von Mises type moment encompassing tests. Advantages of our tests over Otsu and Whang’s (2011) tests are: (i) they are free from smoothing parameters, (ii) they can be applied to weakly dependent data, and (iii) they allow non-smooth moment functions. We derive the null distributions, validity of a bootstrap procedure, and local and global power properties of our tests. The simulation results show that our tests have reasonable size and power performance in finite samples.     

Testing equality restrictions in generalized linear models for multinomial data

Based on f{\phi } -divergences an estimator of the generalized linear models for multinomial data under linear restrictions on the parameters is considered. New test statistics, also based on f{\phi } -divergences are considered as alternatives to the classical ones for testing a hypothesis about linear restrictions on the parameters. The asymptotic distribution of them is obtained under the null hypothesis as well as under contiguous local hypotheses. An application of the estimators and the tests is illustrated in a numerical example and in simulation studies.     

Multi-scale tests for serial correlation

This paper introduces a new family of portmanteau tests for serial correlation. Using the wavelet transform, we decompose the variance of the underlying process into the variance of its low frequency and of its high frequency components and we design a variance ratio test of no serial correlation in the presence of dependence. Such decomposition can be carried out iteratively, each wavelet filter leading to a rich family of tests whose joint limiting null distribution is a multivariate normal. We illustrate the size and power properties of the proposed tests through Monte Carlo simulations.     

On Returns to Scale under Weight Restrictions in Data Envelopment Analysis

Weextend the concept of returns to scale in Data Envelopment Analysis(DEA) to the weight restriction environments. By the additionof weight restrictions, the status of returns to scale, i.e., increasing, constant and decreasing may suffer a change. Wefirst define ``returns to scale' under weight restrictions andpropose a method for identifying the status of returns to scale.Then, it is demonstrated that the region of the most productivescale size (MPSS) will usually be narrowed by this addition.For an inefficient decision making unit (DMU), a simple rulefor determining the status of returns to scale of its projectedDMU will be presented. An empirical study compares the resultsof the proposed method with those of the BCC model and demonstratesthe change in the MPSS for both models.     

Rational and polynomial lags: The finite connection

This article demonstrates that, for a finite distributed lag, the polynomial distributed lag (PDL) approximation suggested by Almon is a special case of the rational lag method formalized by Jorgenson. The proof relies upon the fact that the PDL estimator imposes differencing restrictions upon the parameters while rational lag methods impose quasi-differncing restrictions. Because of this relationship, the PDL restrictions are nested inside the rational lag ones, and this provides for a sequence of tests to discriminate between the two. An example is performed and an appendix describes an asymptotically efficient two-step estimator.     

Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris

This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrate these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period 1990-2003. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics.     

Joint and marginal specification tests for conditional mean and variance models

This article proposes a class of joint and marginal spectral diagnostic tests for parametric conditional means and variances of linear and nonlinear time series models. The use of joint and marginal tests is motivated from the fact that marginal tests for the conditional variance may lead to misleading conclusions when the conditional mean is misspecified. The new tests are based on a generalized spectral approach and do not need to choose a lag order depending on the sample size or to smooth the data. Moreover, the proposed tests are robust to higher order dependence of unknown form, in particular to conditional skewness and kurtosis. It turns out that the asymptotic null distributions of the new tests depend on the data generating process. Hence, we implement the tests with the assistance of a wild bootstrap procedure. A simulation study compares the finite sample performance of the proposed and competing tests, and shows that our tests can play a valuable role in time series modeling. Finally, an application to the S&P 500 highlights the merits of our approach.     

Asymptotically distribution-free tests for the volatility function of a diffusion

This paper develops two tests for parametric volatility function of a diffusion model based on Khmaladze (1981)’s martingale transformation. The tests impose no restrictions on the functional form of the drift function and are shown to be asymptotically distribution-free. The tests are consistent against a large class of fixed alternatives and have nontrivial power against a class of root-n$n$ local alternatives. The paper also extends the tests of volatility to testing for joint specifications of drift and volatility. Monte Carlo simulations show that the tests perform well in finite samples. The proposed tests are then applied to testing models of short-term interest, using data of Treasury bill rate and Eurodollar deposit rate. The empirical results show that the commonly used CKLS volatility function of Chan et al. (1992) fits volatility function poorly and none of the parametric interest rate models considered in the paper fit data well.     

Unit root quantile autoregression testing using covariates

This paper extends unit root tests based on quantile regression proposed by Koenker and Xiao [Koenker, R., Xiao, Z., 2004. Unit root quantile autoregression inference, Journal of the American Statistical Association 99, 775–787] to allow stationary covariates and a linear time trend. The limiting distribution of the test is a convex combination of Dickey–Fuller and standard normal distributions, with weight determined by the correlation between the equation error and the regression covariates. A simulation experiment is described, illustrating the finite sample performance of the unit root test for several types of distributions. The test based on quantile autoregression turns out to be especially advantageous when innovations are heavy-tailed. An application to the CPI-based real exchange rates using four different countries suggests that real exchange rates are not constant unit root processes.     

Inference in regression models with many regressors

We investigate the behavior of various standard and modified F$F$, likelihood ratio (LR$LR$), and Lagrange multiplier (LM$LM$) tests in linear homoskedastic regressions, adapting an alternative asymptotic framework in which the number of regressors and possibly restrictions grows proportionately to the sample size. When the restrictions are not numerous, the rescaled classical test statistics are asymptotically chi-squared, irrespective of whether there are many or few regressors. However, when the restrictions are numerous, standard asymptotic versions of classical tests are invalid. We propose and analyze asymptotically valid versions of the classical tests, including those that are robust to the numerosity of regressors and restrictions. The local power of all asymptotically valid tests under consideration turns out to be equal. The “exact” F$F$ test that appeals to critical values of the F$F$ distribution is also asymptotically valid and robust to the numerosity of regressors and restrictions.     

Incentive compatibility constraints and dynamic programming in continuous time

This paper is devoted to the study of infinite horizon continuous time optimal control problems with incentive compatibility constraints that arise in many economic problems, for instance in defining the second best Pareto optimum for the joint exploitation of a common resource, as in Benhabib and Radner [Benhabib, J., Radner, R., 1992. The joint exploitation of a productive asset: a game theoretic approach. Economic Theory, 2: 155–190]. An incentive compatibility constraint is a constraint on the continuation of the payoff function at every time. We prove that the dynamic programming principle holds, the value function is a viscosity solution of the associated Hamilton–Jacobi–Bellman (HJB) equation, and that it is the minimal supersolution satisfying certain boundary conditions. When the incentive compatibility constraint only depends on the present value of the state variable, we prove existence of optimal strategies, and we show that the problem is equivalent to a state constraints problem in an endogenous state region which depends on the data of the problem. Some economic examples are analyzed.     

Testing for a unit root in a random coefficient panel data model

This paper proposes new unit root tests in the context of a random autoregressive coefficient panel data model, in which the null of a unit root corresponds to the joint restriction that the autoregressive coefficient has unit mean and zero variance. The asymptotic distributions of the test statistics are derived and simulation results are provided to suggest that they perform very well in small samples.     

ASYMPTOTIC ABSOLUTE EFFICIENCY OF PROJECTION TESTS WITH AN APPLICATION TO N-RANKING TESTS

A class of χ²-type goodness-of-fit tests for the hypotheses of equal cell probabilities is introduced and the quality of the tests under arbitrary model restrictions is compared, where a suitable efficiency concept is used. As a special case, several nonparametric tests on treatment effects in the randomized block design can be considered. Some well known cases, such as the Anderson-, Friedman- and the Page-test, turn out to possess a certain minimax efficiency property under reasonable model assumptions. Finally a direct comparison between the Anderson- and Friedman-tests is given.     

Deregulating Overtime Hours Restrictions on Women and its Effects on Female Employment: Evidence from a Natural Experiment in Japan

This paper provides novel evidence on the effect of deregulating overtime hours restrictions on women by using the 1985 Amendments to the Labour Standards Act (LSA) in Japan as a natural experiment. The original LSA of 1947 prohibited women from working overtime exceeding two hours a day; six hours a week; and 150 hours a year. The 1985 Amendments exempted a variety of occupations and industries from such an overtime restriction on women. Applying a difference‐in‐difference model to census data, we find causal evidence pointing to the positive effect of this particular piece of labour market deregulation on the proportion of female employment. We then carry out a series of sensitivity analyses to ensure the robustness of our finding. Especially, we conduct a falsification test and an event study to show that our causal inference is not threatened by the differential pretreatment trends. Finally, we use quantile regressions and find that for jobs with more rapidly growing proportion of female employment, the effect of the exemption from the overtime work restriction on women is larger.