Alicia N. Rambaldi

Monte Carlo Evidence on Cointegration and Causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.

A Stochastic Frontier Production Function with Flexible Risk Properties

This paper considers a stochastic frontier production function which has additive, heteroscedastic error structure. The model allows for negative or positive marginal production risks of inputs, as originally proposed by Just and Pope (1978). The technical efficiencies of individual firms in the sample are a function of the levels of the input variables in the stochastic frontier, in addition to the technical inefficiency effects. These are two features of the model which are not exhibited by the commonly used stochastic frontiers with multiplicative error structures.An empirical application is presented using cross-sectional data on Ethiopian peasant farmers. The null hypothesis of no technical inefficiencies of production among these farmers is accepted. Further, the flexible risk models do not fit the data on peasant farmers as well as the traditional stochastic frontier model with multiplicative error structure.

Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.

Extrapolation of Purchasing Power Parities Using Multiple Benchmarks and Auxiliary Information: A New Approach

The paper presents an econometric framework for the construction of a consistent panel of purchasing power parities (PPPs) which makes it possible to combine all the PPP benchmark data from various phases of the International Comparison Program with the data on national price movements in the form of implicit deflators from national accounts. The method improves upon the current practice used in the construction of the Penn World Tables (PWT), and similar tables produced by the World Bank which tend to be anchored on a selected benchmark. The econometric formulation is based on a regression model for the national price levels where the disturbances are assumed to be heteroskedastic and spatially correlated across countries. The regression model along with data on country specific price movements are combined using a state–space formulation and optimum predictions of PPPs are obtained. As a property of the method presented in the paper, we show that the resulting PPP predictions are weighted averages of extrapolations of PPPs from different benchmarks—thus the method provides a formal approach which has a simple intuitive interpretation. The smoothed PPP predictions (and standard errors) obtained through the state–space are produced for both ICP-participating and non-participating countries and non-benchmark years. A complete tableau of PPPs for 141 countries spanning the period 1970 to 2005 is compiled using the method. Results for some selected countries are presented and the new series are compared and contrasted with the currently available PWT series. Extrapolated series for the remaining countries are available from the authors upon request.

Housing shadow prices in an inundation-prone suburb

For flood-prone urban areas, the prospect of increasing population densities and more frequent extreme weather associated with climate change is alarming. Proactive adaptation can reduce potential flood risks in theory. However, there is limited empirical economics exploring this issue, without which convincing residents within exposed areas to participate in adaptation is challenging. In this paper, a hedonic model is presented of property prices for a flood-prone inner-city suburb of Brisbane, Australia. The study defines a continuous flood-risk variable based on the vertical distances of properties relative to a flood level that occurs on average once every 100 years. The results show significant property-price discounting of 5.5 per cent per metre below the defined flood level. Detailed hedonic characteristics also provided shadow price estimates of housing characteristics and distances to amenities (such as bus-stops, train-stations, parks and bikeways) and these hedonics need to be considered when holistically assessing the dynamics of suburbs for adaptation planning.

Is the Australian wool futures market efficient as a predictor of spot prices

Giles and Goss (1980) have suggested that, if a futures market provides a forward pricing function, then it is an efficient market. In this article a simple test for whether the Australian Wool Futures market is efficient is proposed. The test is based on applying cointegration techniques to test the Law of One Price over a three, six, nine, and twelve month spread of futures prices. We found that the futures market is efficient for up to a six‐month spread, but no further into the future. Because futures market prices can be used to predict spot prices up to six months in advance, woolgrowers can use the futures price to assess when they market their clip, but not for longer‐term production planning decisions.

How trade and foreign investment affect the growth of a small but not so open economy: Australia?

This article analyses the impact of trade and foreign investment on a small but not so open economy, Australia, whose growth rate outpaced the majority of the OECD countries in the last decade. We model five channels of outward orientation: exports, imports, foreign direct investment, foreign portfolio investment and other foreign investment. A cointegrated vector autoregressive model, complemented by a robust Granger noncausality test, is specified to identify permanent channels of outward orientation. Imports and direct investment are found to have a growth effect in the long run. The effect of imports is almost three times that of direct investment.

Hedonic Imputed Property Price Indexes: The Effects of Econometric Modeling Choices

In this paper we consider how choices in the econometric approach to impute prices affect the Tornqvist and Jevons hedonic imputed indexes. We compare the rolling window approach to estimation by smoothing methods. The main difference between the rolling window and the smoothing methods is in the way information is weighted. We propose that the Kalman filter smoother is the most appropriate estimator for the task as it optimally weights current and past information. We show the rolling window approach does not produce estimates that are attenuated over time leading to chain drift in the index. We also compare two alternative specifications to model property location. The empirical section uses data from a small and homogeneous market in the state of Queensland, Australia. The Tornqvist and Jevons indexes differ in value during periods of market volatility. This seems expected given their different weighting of transactions and the likelihood that price movements of properties at the upper and lower end of the price distribution might differ during periods of market volatility.

Modelling Spatially Correlated Error Structures in the Time-Space Extrapolation of Purchasing Power Parities

The paper examines the role and significance of modeling spatially correlated disturbances in the extrapolation of purchasing power parities and real incomes. Alternative specifications for the spatial weighting matrix are experimented within the general econometric framework developed by Rao et al for the purpose of constructing a consistent time-space tableau of PPPs based on the PPPs for benchmarks constructed as a part of the International Comparison Program (ICP) and temporal movements in national price levels. The paper presents a comparative analysis of the effect of alternative specifications of the spatial weights matrix on the PPP extrapolations. A method based on the principal components approach is suggested when several spatial weights matrices are available. The paper presents empirical findings from the application of the methodology to ICP data available including the recently completed 2005 ICP Benchmark comparisons available from the World Bank. The results clearly indicate the need to model and use a spatially correlated error structure especially when the benchmark data are incomplete. The results are very similar when the spatial weights are based on trade data or on principal component weights.

Selecting The "Best" Prediction Model: An Application To Agricultural Cooperatives

A credit scoring function incorporating statistical selection criteria was proposed to evaluate the credit worthiness of agricultural cooperative loans in the Fifth Farm Credit District. In-sample (1981-1986) and out-of-sample (1988) prediction performance of the selected models were evaluated using rank transformation discriminant analysis, logit, and probit. Results indicate superior out-of-sample performance for the management oriented approach relative to classification of unacceptable loans, and poor performance of the rank transformation in out-of-sample prediction.