The FMLS-based CUSUM statistic for testing the null of smooth time-varying cointegration in the presence of a structural break

In this paper we extend the FMLS-based CUSUM cointegration test (Xiao and Phillips, 2002) for testing the smooth time-varying cointegration null hypothesis. For this purpose we use Chebyshev time polynomials to specify time-varying coefficients under the null. We derive the limiting distribution of the statistic, which is pivotal with the order of the Chebyshev time polynomials, and we provide the critical values to conduct the proposed test.     

Testing for structural breaks with local smoothers: A simulation study

By means of an extensive Monte Carlo simulation study based on the design of Chen and Hong (2012) we compare the performance of the tests they proposed for parameter stability with the linearity test of Li et al. (2002) and the functional form test of Li and Wang (1998). We find that the test of Li et al. (2002) test adapted to testing for parameter stability performs favorably well in terms of size and equally well in terms of power compared with the others, whereas the test by Li and Wang has no power.     

Exploring the effects of aggregation error in the estimation of consumer demand elasticities

Errors introduced by using aggregate data in estimating a consumer demand model have long been a concern. We study the effects of such errors on elasticity estimates derived from AIDS and QUAIDS models. Based on a survey of published articles, a generic parameterization of the income distribution, and the range of Gini coefficients reported for 28 OECD countries, we generate and analyze a large number of “observations” on the differences between elasticities calculated at the aggregate level and those calculated at the micro level. We suggest a procedure for evaluating the likely range of aggregation error when a model is estimated with aggregate data.     

Inverse demand systems and choice of functional form

Barten (Empirical Economics 18 (1993) 129) recently advocated estimation of a synthetic demand system that mechanically nests four other popular differential demand models. This paper follows a similar strategy, but in the context of four inverse share-equation demand systems: The Inverse Translog Demand System (ITLDS); the Inverse Almost Ideal Demand System (IAIDS); the Inverse Lewbel Demand System (ILDS); and the Inverse Non-Separable Linear Expenditure System (INLES). Each of these specifications is artificially nested in a Hybrid Inverse Demand System (HIDS). An empirical application to three categories of quarterly U.S. meat demand data over the period 1961-1996 indicates the HIDS is a preferred specification.     

Testing for efficiency in selected developing foreign exchange markets: An equilibrium-based approach

This paper proposes an alternative way of testing FOREX efficiency for developing countries. The FOREX market will be efficient if fully reflects all available information. If this holds, the actual exchange rate will not deviate significantly from its equilibrium rate. Moreover, the spot rate should deviate from its equilibrium rate by only transitory components (i.e. it should follow a white noise process). This test is applied to three Central and Eastern European Countries — members of the EU. Considering an LSTAR model we find no evidence of nonlinear adjustment in the misalignment series. So, linear unit root tests imply that the Poland/Euro FOREX market is efficient, the Czech/Euro FOREX market is not, while the Slovak/Euro FOREX market is quasi-efficient.     

Alcohol content and demand for alcoholic beverages: A system approach

This paper examines how alcohol content affects the consumption of alcoholic beverages in Finland. Three different quality hypotheses are studied and compared: Fisher and Shell, Theil, and an additive one. The comparison of the hypotheses is based on quality elasticities implied by the hypotheses. The results show that, under all hypotheses, alcohol content positively affects the demand for alcoholic beverages, and this effect depends negatively on income. The results of the comparison of the hypotheses show that the additive fits the data best. However, the other hypotheses are almost as good: Fisher and Shell's hypothesis better than Theil's.I would like to thank K. Koskela, A. Nyberg, M. Salo, M. Stenius, and I. Suoniemi for their useful comments and suggestions. The author bears sole responsibility for any remaining errors.     

Carbon tax simulations using a household demand model

The main objective of this paper is to analyse consumer response and welfare effects due to changes in energy or environmental policy. To achieve this objective we formulate and estimate an econometric model for non-durable consumer demand in Sweden that utilises micro- and macro-data. In the simulations, we consider two revenue neutral scenarios that both imply a doubling of the CO₂ tax; one that returns the revenues in the form of a lower VAT and one that subsidise public transport. One conclusion from the simulations is that the CO₂ tax has regional distribution effects, in the sense that household living in sparsely populated areas carry a larger share of the tax burden.     

On testing for nonlinearity in multivariate time series

This paper considers a multivariate extension of the test for neglected nonlinearity proposed by Tsay (1986) that uses principal components to overcome the problem of dimensionality that is common with tests of this type. Monte Carlo experiments reveal that the modified multivariate test provides a significant dimensional reduction without suffering from any systematic level distortion or power loss, and is more powerful than univariate nonlinearity tests.     

On the Fisher information matrix of a vector ARMA process

We derive a neat and compact representation of the asymptotic Fisher information matrix of a vector ARMA process. Its inverse can be used immediately as the asymptotic covariance matrix of the Gaussian maximum likelihood estimator. We also provide the robust sandwich covariance estimator when the process is non-Gaussian.     

Testing for joint significance in nonstationary binary choice model

This paper investigates the test of joint significance for binary choice model with multiple integrated explanatory variables. It is found that for the widely used logit and probit models, even though the estimators have a different convergence rate under null hypothesis compared with the case under alternative, the commonly used Wald statistic is still useful, and asymptotically chi-squared.     

A simple unit root test against asymmetric STAR nonlinearity with an application to real exchange rates in Nordic countries

Existing tests of the unit root hypothesis against the alternative hypothesis of exponential smooth transition autoregressive (ESTAR) nonlinearity implicitly assume symmetry under the alternative. This paper proposes a simple unit root test against the alternative of symmetric or asymmetric ESTAR nonlinearity. In the event that the unit root hypothesis is rejected, a simple test of symmetric versus asymmetric ESTAR nonlinearity is also proposed. The asymptotic distributions of the test statistics are straightforward to establish and finite-sample performance is studied with Monte Carlo simulations. An empirical application involving the real exchange rates of four Nordic countries against the U.S. dollar illustrates the usefulness of the new tests.     

Testing slope homogeneity in large panels with serial correlation

Pesaran and Yamagata (Pesaran, M.H., Yamagata, T., Testing slope homogeneity in large panels, Journal of Econometrics 142, 50–93, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features.     

A method for finding the maximal set in excess demand

We present a polynomial time method for identifying the maximal set in excess demand at a given payoff vector. This set can be used in “large” partnership formation problems to identify the minimum element in the set of individually rational payoff vectors at which there is no overdemanded set of agents. This minimum element corresponds to the minimum Walrasian equilibrium price vector in a special case of the partnership formation problem.     

Testing of the mean reversion parameter in continuous time models

In this paper we use the approximate bias expressions developed in Yu (2012) and Bao et al. (2013) to improve the testing of the ordinary least squares or quasi-maximum likelihood estimator of the mean reversion parameter in continuous time models. We follow the approach given in Iglesias and Phillips (2005) and Chambers (2013), where if we bias correct the estimated mean reversion parameter, we can improve on the small sample properties of the testing procedure. Simulation results confirm the usefulness of this approach using a t$t$-statistic in this setting in the near unit root situation when the mean reversion parameter is approaching its lower bound. Therefore we always recommend bias correcting when applying a t$t$-statistic in practice in this context.     

Testing for normality in linear regression models using regression and scale equivariant estimators

In this paper we provide a general solution to the problem of controlling the probability of a type I error in normality tests for the disturbances in linear regressions when using robust-regression residuals. We show that many classes of well-known robust regression estimators belong to the class of regression and scale equivariant estimators. It is these equivariance properties that are used to reduce the nuisance parameter space under the null, from which we develop Monte Carlo and Maximized Monte Carlo tests for the null of disturbance normality. Finally, we illustrate in a simulation experiment the potential power gains from using robust-regression residuals in testing this null hypothesis.     

Nowcasting causality in mixed frequency vector autoregressive models

This paper introduces nowcasting causality as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in a mixed-frequency VAR and illustrate its impact on the significance of high-frequency variables in mixed-frequency conditional models.     

Improving consumption measurement and other survey data through CAPI: Evidence from a randomized experiment

This paper reports on a randomized survey experiment among 1840 households, designed to compare pen-and-paper interviewing (PAPI) to computer-assisted personal interviewing (CAPI). We find that PAPI data contain a large number of errors, which can be avoided in CAPI. Error counts are not randomly distributed across the sample, but are correlated with household characteristics, potentially introducing sample bias if dubious observations need to be dropped. We demonstrate a tendency for the spread of total measured consumption to be higher on paper compared to CAPI, translating into significantly higher measured inequality. Investigating further the nature of PAPI's measurement error for consumption, we fail to reject the hypothesis that it is classical: it attenuates the coefficient on consumption when used as explanatory variable and we find no evidence of bias when consumption is used as dependent variable. Finally, CAPI and PAPI are compared in terms of interview length, costs and respondents' perceptions.     

Innis Lecture: Inference on income distributions

Abstract This paper attempts to provide a synthetic view of varied techniques available for performing inference on income distributions. Two main approaches can be distinguished: one in which the object of interest is some index of income inequality or poverty, the other based on notions of stochastic dominance. From the statistical point of view, many techniques are common to both approaches, although of course some are specific to one of them. I assume throughout that inference about population quantities is to be based on a sample or samples, and, formally, all randomness is due to that of the sampling process. Inference can be either asymptotic or bootstrap based. In principle, the bootstrap is an ideal tool, since in this paper I ignore issues of complex sampling schemes and suppose that observations are IID. However, both bootstrap inference and, to a considerably greater extent, asymptotic inference can fall foul of difficulties associated with the heavy right‐hand tails observed with many income distributions. I mention some recent attempts to circumvent these difficulties.     

On the permanent effect of an aggregate demand shock: Evidence from the G-7 countries

This paper extends the work of Cover, Enders and Hueng (2006) to examine the idea that an aggregate demand shock may have permanent effect on the output level by indirectly shifting the aggregate supply curve. We utilize the bivariate SVAR modeling and adopt an identification scheme, which allows for the possibility that a shift in the aggregate demand curve may induce the long-run aggregate supply curve to shift. We have shown that aggregate supply shocks are positively affected by the demand shocks in each of the G-7 countries. It is found that a one-time positive aggregate demand shock increases the output level permanently in these industrialized economies. We have also shown that our decomposition strategy can help resolve anomalies in the responses of inflation to a positive aggregate supply shock observed in a simple Blanchard-Quah decomposition.     

Ticket pricing and the impression of excess demand

If willingness to pay depends on characteristics of other attendees, a monopolist will use a lineup as a screening mechanism only if a consumer's characteristic is inversely related to her cost of lining up. No capacity constraint is necessary.