Testing for structural change in the dynamic adjustment model with autoregressive errors

The dynamic CUSUM test for structural change proposed by Kr?mer, Ploberger and Alt (1988) is investigated when the errors are serially correlated in a linear dynamic model. We show that the dynamic CUSUM test can be modified to allow for serial correlation in the disturbance using the same procedure as in Kao and Ross (1995), and that the modified dynamic CUSUM test retains its asymptotic significance levels. Monte Carlo results suggest that the empirical size of the dynamic CUSUM test is highly distorted while the empirical size of the modified dynamic CUSUM test is fairly robust to the change on the degree of autocorrelation. We also find that the power of the modified test essentially depends on the angle between the mean regressors and the structural shift. First version received: April 1997/Final version received: January 1998     

The sensitivity of the RESET tests to disturbance autocorrelation in regression analysis

This paper investigates the sensitivity of the RESET tests, proposed by Ramsey (1969) and modified by Thursby and Schmidt (1977), to disturbance autocorrelation in regression analysis. Porter and Kashyap (1984) show that RESET is not robust to autocorrelated disturbance when there is a highly autocorrelated regressor in the model. We show that RESET is sensitive to disturbance autocorrelation even when the regressors are not autocorrelated. We explain the findings of Thursby (1979) and Porter and Kashyap (1984) as well as our result by showing that a spurious correlation between the regressor and the disturbance is responsible for the serious size distortion of the RESET tests. First version received: June 1999/Final version received: November 2000     

Asymptotic Variance–Covariance Matrices of Two-Stage Estimators in the Presence of Continuous and Binary Dependent Variables with an Empirical Application

Journal of Quantitative Economics - We develop a general procedure to derive the asymptotic variance–covariance matrices of several two-stage estimators that can be used to estimate...     

Spurious deterministic seasonality and autocorrelation corrections with quarterly data: Further Monte Carlo results

Following recent work of Franses, Hylleberg and Lee (FHL), this paper analyses the consequences of fitting a deterministic seasonal model to a quarterly time series which can be (at least approximately) described by a seasonal unit root(s) model. Besides the distribution of the coefficient of determination, the empirical distributions of two commonly used statistics are also investigated through Monte Carlo experiments for small, moderately large and large samples. FHL's work is also extended allowing the possibility of residual autocorrelation corrections. The main conclusion that emerges from the results is that one should not try to measure the importance of deterministic seasonality nor test for its presence in the context of such (static) regression models, even when using some form of residual autocorrelation correction. A simple empirical application is provided to illustrate our results. First version received: July 1997/final version received: July 1998     

Asymptotic standard errors of point elasticities calculated from simultaneous equation systems

An expression for the asymptotic covariance matrix of point elasticities, computed from a simultaneous linear equation system with estimated coefficients, is derived in this paper. Numerical examples are provided using the Klein-I model estimated by 2SLS.     

Economies of scale in public education: an econometric analysis

This article investigates the sources of scale economies in the production of public education. The relationship between the average cost of producing educational output and school characteristics including school and district size is estimated using a neoclassical cost function. The empirical analysis used panel data from Utah school districts and estimates the function using the covariance and error component models after making necessary corrections for heteroskedasticity and autocorrelation. The uncorrected fixed effects model generates a significant negative coefficient on district size in both the cost and expenditure functions; the coefficient on number of students has the hypothesized sign but is not significant in either equation. After making various corrections for autocorrelation and heteroskedasticity, the coefficients have the correct signs and are significant in all equations. Thus, it is concluded that scale economies arise from both sources but that the evidence is stronger for district size.     

Is it efficient to analyse efficiency rankings?

When production functions are estimated as frontier functions, the deviations from the frontier can be interpreted as individual inefficiency estimates. Unfortunately, it has recently been shown that efficiency differences across individuals are very often statistically insignificant. In this paper, we will analyse the consequences of the consideration of confidence statements for the reliability of efficiency rankings. The stochastic frontier and confidence intervals derived by Horrace and Schmidt are compared to the COLS approach and bootstrap confidence intervals. The membership function is proposed as a simple Monte-Carlo approximation for the probability for an individual to be the most efficient in the sample. First version received: May 1998/final version accepted: July 1999     

Heteroskedasticity testing through a comparison of Wald statistics

This paper shows that a test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The test is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom. The power of the test is sensitive to the choice of parametric restriction used by the Wald statistics, so the supremum of a range of individual test statistics is proposed. Two versions of a supremum-based test are considered: the first version does not have a known asymptotic null distribution, so the bootstrap is employed to approximate its empirical distribution. The second version has a known asymptotic distribution and, in some cases, is asymptotically pivotal under the null. A simulation study illustrates the use and finite-sample performance of both versions of the test. In this study, the bootstrap is found to provide better size control than asymptotic critical values, namely with heavy-tailed, asymmetric distributions of the covariates. In addition, the use of well-known modifications of the heteroskedasticity consistent covariance matrix estimator of OLS coefficients is also found to benefit the tests’ overall behaviour.     

Measuring the effectiveness of non-price export promotion using a supply-side approach

In this paper we assess the effectiveness of the Market Promotion Program (MPP) in increasing U.S. exports and benefitting U.S. agricultural producers and food processors. Export shipments are linked to producer welfare using Kohli's (1978) profit maximization (GNP function) approach to modeling international trade. Using estimated profit functions in conjunction with a synthetic export demand function for processed agricultural products, we compute changes in farm and food processing sector profits that result from alternative own-price and advertising elasticities of export demand with and without the MPP subsidy. This approach allows us to investigate aggregate welfare effects of nonprice promotion without requiring the difficult task of estimating the export demand effects of market promotion activities for numerous commodities and importing countries. First version received: April 1999/Final version received: June 2000     

The treatment of seasonality in error correction models as unobserved component: a case study for an Austrian consumption function

This paper deals with an alternative approach to treating seasonality in error correction models for consumption with a parsimonious parameterization as proposed by Harvey and Scott. We introduce an unobserved seasonal component into an error correction model for Austrian consumer expenditures on nondurables and services and compare the results with different approaches. The use of stochastic seasonal results in a definite improvement of the estimated model. First version received: October 1997/Final version received: May 2000     

A latent dynamic factor approach to forecasting multivariate stock market volatility

This paper proposes a latent dynamic factor model for high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and combines common latent factors driven by HAR processes and idiosyncratic autoregressive dynamics. The model accounts for positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates are obtained using basic Markov chain Monte Carlo methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices shows remarkably good forecasting results, in-sample and out-of-sample.     

Principal Component Models for Generating Large GARCH Covariance Matrices

The implementation of multivariate GARCH models in more than a few dimensions is extremely difficult: because the model has many parameters, the likelihood function becomes very flat, and consequently the optimization of the likelihood becomes practicably impossible. There is simply no way that full multivariate GARCH models can be used to estimate directly the very large covariance matrices that are required to net all the risks in a large trading book. This paper begins by describing the principal component GARCH or 'orthogonal GARCH' (O-GARCH) model for generating large GARCH covariance matrices that was first introduced in Alexander and Chibumba (1996) and subsequently developed in Alexander (2000, 2001b). The O-GARCH model is an accurate and efficient method for generating large covariance matrices that only requires the estimation of univariate GARCH models. Hence, it has many practical advantages, for example in value–at–risk models. It works best in highly correlated systems, such as term structures. The purpose of this paper is to show that, if sufficient care is taken with the initial calibration of the model, equities and foreign exchange rates can also be included in one large covariance matrix. Simple conditions for the final covariance matrix to be positive semi-definite are derived.
(J.E.L.: C32, C53, G19, G21, G28).     

Data-dependent selection of the lag truncation parameter in unit root tests of the Phillips-Perron type

The Andrews (Econometrica, 1991, 59, 817–858) plug-in method of heteroscedastic and autocovariance consistent covariance matrix estimation is used to construct estimators of the long-run variance parameter for use in Phillips-Perron unit root tests. This allows the lag truncation parameter to be data dependent. Monte Carlo size and power estimates are obtained suggesting that this apparently natural approach does not provide significant improvements in test performance.     

SEMINONPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATION OF CONDITIONAL MOMENT RESTRICTION MODELS*

This article studies estimation of a conditional moment restriction model with the seminonparametric maximum likelihood approach proposed by Gallant and Nychka (Econometrica 55 (March 1987), 363–90). Under some sufficient conditions, we show that the estimator of the finite dimensional parameter θ is asymptotically normally distributed and attains the semiparametric efficiency bound and that the estimator of the density function is consistent under L₂ norm. Some results on the convergence rate of the estimated density function are derived. An easy to compute covariance matrix for the asymptotic covariance of the θ estimator is presented.     

Dynamic and stochastic structures in tourism demand modeling

In this paper we consider a model for international tourism demand. The point of departure of the analysis is a utility function that is both dynamic and stochastic. In the model the stochastic component is interpreted as random changes in preferences for goods and services, while the dynamic component can be seen as either habit formation or as interdependent preferences. The resulting demand functions are estimated as a multivariate state space model, where the stochastic components enter the model as stochastic seasonal and trend components. An application is constructed for different segments of the Swedish tourism market. The results indicate the importance of including both dynamic and stochastic components in the utility function, and the importance of using disaggregate data to enable investigation of each market segment.I am grateful to two anonymous referees for useful comments. Much of the research was done while I was a visiting scholar at University of California Berkeley. The hospitality of the RIPM division is gratefully acknowledged. The research was supported by grants from the Wallander Foundation.First version received: January 2003/Final version received: February 2004     

Retesting symmetry and homogeneity in a cointegrated demand system with bootstrapping: The case of meat demand in Greece

A small Almost Ideal Demand System is estimated for Greek meat consumption using the Johansen procedure in conjunction with parametric bootstrapping and Bartlett corrections. Asymptotic Wald and likelihood ratio tests broadly support the predicted number of cointegrating relationships but reject symmetry and homogeneity. Bootstrapping and Bartlett corrections give support to symmetry and homogeneity but give less support for the predicted number of cointegrating relationships.Jel classification: C32 D12First version received: September 2001/Final version received: March 2003     

(When) Should cointegrating regressions be detrended? The case of a German money demand function

If an economic relationship is superimposed by a linear time trend, the regression without detrending is misspecified. The estimators of such a regression do not converge to the true parameter values. First, the asymptotic limit arising from such misspecified regressions is characterized. Second, we observe with data before 1975 a significant time trend but no cointegrating relation between real money (M1), income and a long-term interest rate. The price level as a significant omitted variable is considered as an economic explanation for this feature. We find a price elasiticity larger than one. Third, with data after the breakdown of Bretton Woods (and the beginning of monetary targeting by the Bundesbank), real money, income and the interest rate alone are cointegrated. The long-run estimates seem to be fairly stable with data after the German union provided a step dummy accounts for a break in the mean. First version received: November 1996/final version received: June 1998     

Money demand, the Cagan model, testing rational expectations vs adaptive expectations: The case of Turkey

This paper estimates the Cagan type demand for money function for Turkish economy during the period 1986:1–1995:3 and tests whether Cagan's specification fits the Turkish data using an econometric technique assuming that forecasting errors are stationary. This paper also tests the hypothesis that monetary policy was implemented in aiming to maximize the inflation tax revenue. Finally, the Cagan model is estimated with the additional assumption of rational expectations for Turkey for the considered period. First version received: March 1998/final version received: October 1998     

Retail Demand for Meat in Australia: A Utility Theory Approach*

This paper examines Australian retail demand for meat for the post-war period 1949-50 to 1978-79. Several systems of demand equations are estimated, each system (comprising equations for beef, mutton, lamb, pork and chicken) being derived from an underlying static utility function. Model selection procedures are used to determine the preferred specifications). The validity of the utility theory approach is investigated by: testing for the presence of autocorrelation; examining the monotonicity and convexity properties of the utility functions; and comparing the estimated price and expenditure elasticities with those of other Australian studies. Serial correlation does arise as a problem, but other results provide support for the theory.     

Ein empirischer Test der Annahme der permanenten Einkommenshypothese von Milton Friedman

Summary We apply the Variate-Difference-Method to yearly Austrian data from 1956 to 1975 to test the permanent-income-hypothesis byM. Fiedman.First we determe the degree of the polynomial, which is sufficient to eliminate the trend (i.e. the permanent part) from time series of disposable income. Then the permanent income variable is constructed in two alternative ways: first, as a polynomial in time and second bySheppard's smoothing formulae. Transitory consumption is identified with the residuals of the consumption function. A correlation analysis shows that, transitory income and consumption are correlated, if permanent income is determined by a trend polynomial, while this is not the case ifSheppard's smoothing formulae is used to estimate permanent income.Consumption functions are specified for total consumption, durables and non-durables and eleven sub-groups of total private consumption. The results indicate that autocorrelation can considerably be reduced by usingSheppard's smoothing formulae.