JEL Classification C12, C22
Mestrado em Ciências Empresariais; O presente trabalho analisa o nível de integração entre os mercados moçambicano, sul-africano e internacional de açúcar. Utilizam-se os testes de raiz unitária Dickey-Fuller Aumentado (ADF), de cointegração de Johansen, Modelo Vectorial de Correcção de Erro (VEC) e teste de exogeneidade, para determinar a ordem de integração das variáveis, verificar a cointegração e determinar o número de vectores de cointegração. Finalmente, são impostas restrições ao modelo seleccionado para verificar a vigência da Lei do Preço Único e identificar as variáveis explicadas e explicativas.
O trabalho resulta da necessidade de uma melhor percepção do mecanismo de transmissão de preços e da cadeia de valor do mercado nacional de açúcar com o mercado sul-africano e internacional, e tem como objectivo proporcionar informação sobre as relações de médio e longo prazos, entre os mercados de açúcar moçambicano, sul-africano e internacional, vigência ou não da lei do preço único entre os mercados, e ainda a caracterização da cadeia de valor do açúcar produzido em Moçambique.
Os resultados mostram que no período em análise os mercados moçambicano e sul-africano estão integrados e que os preços de açúcar no mercado moçambicano são conduzidos pelos preços do açúcar do mercado sul-africano...
Mestrado em Marketing / JEL Classification System: M31 - Marketing; C12 - Hypothesis Testing: General; Ao longo dos últimos anos as preocupações com o ambiente têm sido cada vez maiores, devido à crescente degradação que este tem vindo a sofrer ao longo do tempo. E, por isso, tem sido cada vez mais relevante perceber o porquê dos consumidores comprarem ou não produtos amigos do ambiente, visto este ser um comportamento relevante de forma a ajudar a preservar o ambiente, e que pode e deve ser incentivado.
Assim, com base na revisão de literatura realizada, pode-se afirmar que a realização de um comportamento pro-ambiental sinaliza algumas qualidades acerca das pessoas que os colocam em prática (costly signaling perspective), qualidades estas que ainda não está claro quais são, e que isso pode influenciar o comportamento das pessoas relativamente aos produtos verdes. Deste modo, o estudo realizado observou a influência que duas qualidades, inteligência e estar na moda, têm sobre a preferência por produtos verdes. Foi possível observar que a existência de um motivo de inteligência leva a que as pessoas revelem maior preferência pelo produto verde quando comparado com o produto convencional, e que o mesmo não acontece com o motivo de estar na moda...
This paper presents a new approach to estimation and inference in panel data models with unobserved common factors possibly correlated with exogenously given individual-specific regressors and/or the observed common effects. The basic idea behind the proposed estimation procedure is to filter the individual-specific regressors by means of (weighted) cross-section aggregates such that, asymptotically as the cross-section dimension (N) tends to infinity, the differential of unobserved common factors are eliminated. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by cross sectional averages of the dependent variable and the individual specific regressors. It is shown that the proposed correlated common effects (CCE) estimators for the individual-specific regressors (and its pooled counterpart) are asymptotically unbiased as N ? 8, both when T (the time-series dimension) is fixed, and when N and T tend to infinity jointly. Further, the CCE estimators are asymptotically normal for T fixed as N ? 8, and when (N,T) ? 8, jointly provided vT/N ? 0 as (N,T) ? 8. A generalisation of these results to multi-factor structures is also provided.
A number of panel unit root tests that allow for cross section dependence have been proposed in the literature, notably by Bai and Ng (2002), Moon and Perron (2003) and Phillips and Sul (2002) who use orthogonalization type procedures to asymptotically eliminate the cross dependence of the series. In this paper we propose a simple alternative test where the standard DF (or ADF) regressions are augmented with the cross section averages of lagged levels and first-differences of the individual series. A truncated version of the CADF statistics is also considered. New asymptotic results are obtained both for the individual CADF statistics and their simple averages. It is shown that the CADFi statistics are asymptotically similar and do not depend on the factor loadings under joint asymptotics where N (cross section dimension) and T (time series dimension) ? 8, such that N/T? k, where k is a fixed finite non-zero constant. But they are asymptotically correlated due to their dependence on the common factor. Despite this
This paper re-examines the panel unit root tests proposed by Chang (2002). She establishes asymptotic independence of the t-statistics when integrable functions of lagged dependent variable are used as instruments even if the original series are cross sectionally dependent. She claims that her non-linear instrumental variable (NIV) panel unit root test is valid under general error cross correlations for any N (the cross section dimension) as T (the time dimension of the panel) tends to infinity. These results are largely due to her particular choice of the error correlation matrix which results in weak cross section dependence. Also, the asymptotic independence property of the t- statistics disappears when Chang's modified instruments are used. Using a common factor model with a sizeable degree of cross section correlations, we show that Chang's NIV panel unit root test suffers from gross size distortions, even when N is small relative to T.
This paper examines and compares the finite sample performance of the existing tests for sample selection bias, especially under the multi-collinearity problem pointed out by Nawata (1993). The results show that under such multicollinearity problem, (i) the t-test for sample selection bias based on the Heckman and Greene variance estimator can be unreliable; (ii) the standard t-test (Heckman 1979) and the asymptotically efficient Lagrange multiplier test (Melino 1982) have correct size but very little power; (iii) however, the likelihood ratio test following the maximum likelihood estimation remains powerful.
This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004) , and the other uses principal components following the work of Stock and Watson (2002) . The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.
This paper proposes a modified version of Swamy?s test of slope homogeneity for panel data models where the cross section dimension (N) could be large relative to the time series dimension (T). We exploit the cross section dispersion of individual slopes weighted by their relative precision. Using Monte Carlo experiments, we show that the test has the correct size and satisfactory power in panels with strictly exogenous regressors for various combinations of N and T. For autoregressive (AR) models the test performs well for moderate values of the root of the autoregressive process, but with roots near unity a bias-corrected bootstrapped version performs well even if N is large relative to T. The cross section dispersion tests are used to test the homogeneity of slopes in autoregressive models of individual earnings using the PSID data and show statistically significant evidence of slope heterogeneity in the earnings dynamics.
This paper This paper develops a new approach to the problem of testing the existence of a long-run level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary. The proposed tests are based on standard F- and t-statistics used to test the significance of the lagged levels of the variables in a first-difference regression. Two sets of asymptotic critical values are provided: one set assuming that all the regressors are I(1) and another set assuming they are all I(0). These two sets of critical values provide a band covering all possible classifications of the regressors into I(0), I(1) or mutually cointegrated. Accordingly, various bounds testing procedures are proposed. The empirical relevance of the bounds procedures is demonstrated by a re-examination of the earnings equation included in the UK Treasury macro-econometric model. This is a particularly relevant application as there is considerable doubt concerning the order of integration of variables such as the unemployment rate, union strength and the wedge between the real product wage' and the real consumption wage' that enter the earnings equation
This paper argues in favour of a closer link between decision and forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used in meteorological forecasts, it is hardly mentioned in standard academic textbooks on economic forecasting. Some of the main issues involved are illustrated in the context of a two-state, two-action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and useful links between Kuipers score, used as a measure of forecast accuracy in the meteorology literature, and the market timing tests used in finance, are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be exploited in the presence of transaction costs are discussed.
The authors develop a test of infinite degree stochastic dominance based on the use of the empirical moment generating function. Two applications are considered. One uses the income data of Anderson (Econometrica, 1996) and derives results consistent with his. In the other application, the dominance between the US and UK stockmarkets is examined. Using data on the SP 500 and the FTALL-Share, it is shown that the US displays infinite degree stochastic dominance over the UK.
The authors demonstrate the conditions under which the bivariate probit model can be considered a special case of the more general multinomial probit model. Since the attendant parameter restrictions produce a singular covariance matrix, the subsequent problems of testing on the boundary of the parameter space are circumvented by the construction of a score test.
This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.