Heteroscedastic Disturbances Research Articles

Sustainability of marine fishing: a case study of West Bengal

The marine fishery sector in India is important not only for domestic demand for protein food requirements but also from the imperatives of exports. An increased investment in this sector has taken place during the last few years and the fishing fleet has expanded at a very rapid rate. Presently, however, marine fishers are facing difficulties in fish capture in spite of exerting the same and sometimes more effort. Thus the question of the sustainability of marine fishing arises. The sustainability of marine fishing in the Digha-Shankarpur coastal region has been studied through estimation of the maximum sustainable yield based on a simple bioeconomic model. WLS estimation has been done to remove heteroscedastic disturbances. Estimated results show that actual yields of all varieties are still below maximum sustainable yield (MSY) except prawn, which has reached near optimal levels.

A Heteroscedastic Generalized Extreme Value Discrete Choice Model

Of the commonly used discrete choice models, the probit class allows flexible covariance structures for disturbances but is computationally burdensome for problems with more than a few alternatives. The generalized extreme value (GEV) class, including the widely used logit and nested logit models, has the advantage of computational ease but suffers in general from the restriction of homoscedastic disturbances. This article generalizes the GEV class to allow heteroscedastic disturbances across decision makers as well as across choice alternatives. The resulting models include the heteroscedastic extreme value model as a special case, which is a generalized logit model with heteroscedasticity across choice alternatives. Particular attention is paid to the heteroscedastic logit and nested logit models because of their widespread use in practice. An empirical application reanalyzing data from the 1980 presidential election tests the hypothesis of information-induced heteroscedasticity across voters and finds support for a heteroscedastic logit model that reveals stronger effects of voter information on the turnout decision than suggested by the original standard logit model in Ordeshook and Zeng.

Domestic Political Institutions and Exchange Rate Commitments in the Developing World

Policymakers use a fixed exchange rate regime to signal their commitment to low inflation and to exchange rate stability. Increasing economic integration and the rise of democratic institutions make it more difficult for policymakers to maintain the credibility of this commitment. We use binary probit (with a variety of corrections for autocorrelated and heteroscedastic disturbances) to test hypotheses relating democratic institutions to exchange rate regime choice on a sample of 76 developing countries over the period 1973‐1994. The empirical analysis indicates that domestic political preferences—as measured by the structure of domestic political institutions and the fractionalization of the party system—influence exchange rate regime choice. We find that floating exchange rate regimes are more likely in democratic than in nondemocratic polities and that democratic polities with majoritarian electoral systems are more likely to fix their exchange rates than those with systems of proportional representation. The growth of international capital markets is truly extraordinary. Cross-border capital flows dwarf those of international trade: recent estimates suggest that foreign exchange trading alone now exceeds one trillion dollars a day. The magnitude and volatile nature of international capital flows has led some political economists to suggest that increased economic integration and capital mobility has become so pervasive that it now acts as a “structural characteristic of the international system, similar to anarchy” (Keohane and Milner, 1996:257). These scholars point to globalization as a crucial factor leading to a convergence of economic policy in the industrialized world. While a wave of economic liberalization has swept OECD economies, governments in developing countries still use a variety of traditional economic tools to protect the relative autonomy of their domestic policies. Vital in this process is exchange rate policy for it is the exchange rate that serves as a buffer between international and domestic markets. Even after the collapse of the Bretton Woods system of pegged exchange rates, most developing countries continue to fix the value of their currency to that of their major trading partner. The logic is clear: by fixing the domestic currency’s value to that of a trading partner, exchange rate volatility is minimized. As a result, bilateral flows of capital and goods are not disrupted by exchange rate uncertainty and

Stochastic regression model with heteroscedastic disturbance

This paper discusses some properties of stochastic regression model with continuous form of heteroscedastic disturbance. The strong consistency and asymptotic normality of a generalized weighted least squares estimate will be investigated under certain conditions on the stochastic regressors and errors. More, the linear hypothesis testing problem also be discussed and an example to be demonstrated to reestablish the results of Cheng and Chang (1990, Tech. Report, National Tsing Hua University).

"Truth" Is Stranger than Prediction, More Questionable than Causal Inference

Robert Luskin's article in this issue provides a useful service by appropriately qualifying several points I made in my 1986 American Journal of Political Science article. Whereas I focused on how to avoid common mistakes in quantitative political science, Luskin clarifies ways to extract some useful information from usually problematic statistics: correlation coefficients, standardized coefficients, and especially RZ. Since these three statistics are very closely related (and indeed deterministic functions of one another in some cases), I focus in this discussion primarily on R2, the most widely used and abused. Luskin also widens the discussion to various kinds of specification tests, a general issue I also address. In fact, as Beck (1991) reports, a large number of formal specification tests are just functions of R2, with differences among them primarily due to how much each statistic penalizes one for including extra parameters and fewer observations. Reasons for Concern about Model Specification Quantitative political scientists often worry about model selection and specification, asking questions about parameter identification, autocorrelated or heteroscedastic disturbances, parameter constancy, variable choice, measurement error, endogeneity, functional forms, stochastic assumptions, and selection bias, among numerous others. These model specification questions are all important, but we may have forgotten why we pose them. Political scientists commonly give three reasons: (I) finding the true model, or the full explanation; (2) prediction; and (3) estimating specific causal effects. I argue here that (1) is used the most but useful the least; (2) is very useful but not usually in political science where forecasting is not often a central concern; and (3) correctly represents the goals of political scientists and should form the basis of most of our quantitative empirical work.

The power of the Durbin-Watson test when the errors are heteroscedastic

We consider the robustness of the Durbin-Watson test to mis-specification via heteroscedastic disturbances. Exact powers are calculated using real and artificial regressors. We find that heteroscedasticity may dramatically alter the power of the test.

ESTIMATING RISK EFFECTS IN CHINESE FOODGRAIN PRODUCTION

Given the importance of Chinese foodgrain production both in China and in the world food market, it is useful to explore the sources of the increased variabilities of China's foodgrain production (Stone and Zhong, 1989). A production function with composite error structure and a heteroscedastic disturbance is applied to cross‐section and time‐series data from China. The function provides information on the contributions of inputs to production variance and therefore on risk. It is found that production variance is positively related to sown area, chemical fertiliser and irrigation, and is negatively related to electricity use. However, most of the estimates determining the marginal risk effects lack statistical significance. This suggests that the measured controllable factors do not contribute very significantly to Chinese foodgrain production variability.

On the Optimality of Some Tests of the Error Covariance Matrix in the Linear Regression Model

SUMMARY This paper adopts the approach of conditional distributions to investigate the optimality of some tests of the error covariance matrix in the linear regression model. Specifically, we show that point optimal tests, advocated by King and Evans and King, are the most powerful similar test. We also derive the locally best similar and the locally best unbiased similar tests. Finding the latter cumbersome to apply, we then propose the asymptotically best similar and the asymptotically best unbiased similar (ABUS) tests as alternative criteria. We show that the two-sided Durbin–Watson test is ABUS against serial correlation and that the two-sided Lagrange multiplier test is ABUS against heteroscedastic disturbances.

A Generalized Error Component Model with Heteroscedastic Disturbances

This paper generalizes the one-way error component model from the homoskedastic to the heteroskedastic case. Unlike P. Mazodier and A. Trognon's (1978) heteroskedastic two-way error component model, an explicit form for V11/2 is obtained that allows the computation of generalized least squares as a simple modification of J. A. Hausman's (1978) procedure for the homoskedastic case. Two methods for estimating the variance components are proposed. The first estimates the variance components based on ordinary least squares and within residuals. The second uses the minimum norm quadratic unbiased estimation procedure suggested by C. R. Rao (1970). These methods are then applied to the estimation of gasoline demand for eighteen OECD countries over the period 1964-78. Copyright 1988 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Least absolute deviations estimation for the censored regression model

This paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored ‘Tobit’) model. The proposed estimator is a generalization of least absolute deviations estimation for the standard linear model, and, unlike estimation methods based on the assumption of normally distributed error terms, the estimator is consistent and asymptotically normal for a wide class of error distributions, and is also robust to heteroscedasticity. The paper gives the regularity conditions and proofs of these large-sample results, and proposes classes of consistent estimators of the asymptotic covariance matrix for both homoscedastic and heteroscedastic disturbances.

A joint test for serial correlation and heteroscedasticity

The problem of testing jointly for first-order autoregressive and heteroscedastic disturbances in the linear regression model is considered. A test which is most powerful invariant in a predetermined neighbourhood of the alternative hypothesis parameter space is proposed.

Estimation and testing in time-series regression models with heteroscedastic disturbances

We consider ARMAX models with heteroscedastic residuals. Consistent estimation of the regression coefficient allows the Bicker-White approach to heteroscedasticity to be extended to moving averages of heteroscedastic disturbances. Tests for the presence of a moving-average or of heteroscedasticity are developed and estimation of the moving-average parameters considered.

Using Time-Series and Cross-Section Data to Estimate a Production Function with Positive and Negative Marginal Risks

Abstract Two production-function models with error components for time and firms and a heteroscedastic disturbance are proposed. Unlike previous models, these two permit the variance of output to increase or decrease as one of the inputs is increased. When the models and some suggested estimators are applied to the pastoral zone of eastern Australia, the results indicate that labor, water, and possibly fencing, are likely to reduce the variance of wool production; and that sheep, and buildings and land, are likely to increase the variance.

Using Time-Series and Cross-Section Data to Estimate a Production Function with Positive and Negative Marginal Risks

Abstract Two production-function models with error components for time and firms and a heteroscedastic disturbance are proposed. Unlike previous models, these two permit the variance of output to increase or decrease as one of the inputs is increased. When the models and some suggested estimators are applied to the pastoral zone of eastern Australia, the results indicate that labor, water, and possibly fencing, are likely to reduce the variance of wool production; and that sheep, and buildings and land, are likely to increase the variance.

A Simple Test for Heteroscedasticity and Random Coefficient Variation

A simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test. For a wide range of heteroscedastic and random coefficient specifications, the criterion is given as a readily computed function of the OLS residuals. Some finite sample evidence is presented to supplement the general asymptotic properties of Lagrangian multiplier tests.