Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

The value of local partners in Brazil: the effects of ownership clusters and business group membership on firm value in emerging markets

Este estudo analisa como a classe de acionistas afeta o valor das empresas brasileiras listadas na bolsa de valores no ponto de vista da governança corporativa. O trabalho examina a interação entre o valor das empresas e cinco tipos de concentrações acionárias comumente presente em mercados emergentes: famílias, agentes públicos, investidores estrangeiros, executivos e investidores financeiros nacionais. A análise empírica demonstra que o mix e a concentração de participação acionária afeta significativamente o valor das empresas. Utilizando uma compilação única de dados em painel de 2004 a 2008, a presente pesquisa também desenvolve hipóteses sobre o efeito da participação em grupos econômicos para o valor das empresas. A investigação encontra evidências de que, apesar de sua importância para o desenvolvimento de empresas brasileiras, o capital familiar, instituições públicas, e investidores estrangeiros estão cedendo lugar a monitores mais especializados e menos concentrados, como executivos e instituições financeiras nacionais. Estes resultados indicam que a governança corporativa no Brasil pode estar alcançando níveis de maturidade mais elevados. Adicionalmente, apesar de não haver indicação da existência de correlação entre a participação em grupos econômicos e o valor das empresas, os resultados indicam que a presença de um tipo específico de acionista em uma empresa do grupo facilita investimentos futuros desta classe de acionista em outras empresas do mesmo grupo, sinalizando que os interesses acionários são provavelmente perpetuados dentro de uma mesma rede de empresas. Finalmente, a pesquisa demonstra que enquanto o capital familiar prefere investir em empresas com ativa mobilidade do capital, investidores internacionais e instituições públicas procuram investimentos em equity com menor mobilidade de capital, o que lhes garante mais transparência com relação ao uso dos recursos e fundos das empresas.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Private or public? a taxonomy of optimal ownership and management regimes

We develop a theory of public versus private ownership based on value diversion by managers. Government is assumed to face stronger institutional constraints than has been assumed in previous literature. The model which emerges from these assumptions is fexible and has wide application. We provide amapping between the qualitative characteristics of an asset, its main use - including public goods characteristics, and spillovers toother assets values - and the optimal ownership and management regime. The model is applied to single and multiple related assets. We address questions such as; when is it optimal to have one of a pair ofr elated assets public and the other private; when is joint management desirable; and when should a public asset be managed by the owner of a related private asset? We show that while private ownership can be judged optimal in some cases solely on the basis of qualitative information, the optimality of any other ownership and management regimes relies on quantitative analysis. Our results reveal the situations in which policy makers will have difficulty in determining the opimal regime.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

A Common-feature approach for testing present-value restrictions with financial data

It is well known that cointegration between the level of two variables (labeled Yt and yt in this paper) is a necessary condition to assess the empirical validity of a present-value model (PV and PVM, respectively, hereafter) linking them. The work on cointegration has been so prevalent that it is often overlooked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. The basis of this result is the use of rational expectations in forecasting future values of variables in the PVM. If this condition fails, the present-value equation will not be valid, since it will contain an additional term capturing the (non-zero) conditional expected value of future error terms. Our article has a few novel contributions, but two stand out. First, in testing for PVMs, we advise to split the restrictions implied by PV relationships into orthogonality conditions (or reduced rank restrictions) before additional tests on the value of parameters. We show that PV relationships entail a weak-form common feature relationship as in Hecq, Palm, and Urbain (2006) and in Athanasopoulos, Guillén, Issler and Vahid (2011) and also a polynomial serial-correlation common feature relationship as in Cubadda and Hecq (2001), which represent restrictions on dynamic models which allow several tests for the existence of PV relationships to be used. Because these relationships occur mostly with nancial data, we propose tests based on generalized method of moment (GMM) estimates, where it is straightforward to propose robust tests in the presence of heteroskedasticity. We also propose a robust Wald test developed to investigate the presence of reduced rank models. Their performance is evaluated in a Monte-Carlo exercise. Second, in the context of asset pricing, we propose applying a permanent-transitory (PT) decomposition based on Beveridge and Nelson (1981), which focus on extracting the long-run component of asset prices, a key concept in modern nancial theory as discussed in Alvarez and Jermann (2005), Hansen and Scheinkman (2009), and Nieuwerburgh, Lustig, Verdelhan (2010). Here again we can exploit the results developed in the common cycle literature to easily extract permament and transitory components under both long and also short-run restrictions. The techniques discussed herein are applied to long span annual data on long- and short-term interest rates and on price and dividend for the U.S. economy. In both applications we do not reject the existence of a common cyclical feature vector linking these two series. Extracting the long-run component shows the usefulness of our approach and highlights the presence of asset-pricing bubbles.

State ownership and corporate governance

State ownership of publicly-traded corporations remains pervasive around the world, and has been increasing in recent years. Existing literature focuses on the implications of government ownership for corporate governance and performance at the firm level. This Article, by contrast, explores the different but equally important question of whether the presence of the state as a shareholder can impose negative externalities on the corporate law regime available to the private sector. Drawing from historical experiments with government ownership in the United States, Brazil, China, and Europe, this study shows that the conflict of interest stemming from the state’s dual role as a shareholder and regulator can influence the content of corporate laws to the detriment of outside investor protection and efficiency. It thus addresses a gap in the literature on the political economy of corporate governance by incorporating the political role of the state as shareholder as another mechanism to explain the relationship between corporate ownership structures and legal investor protection. Finally, this Article explores the promise of different institutional arrangements to constrain the impact of the state’s interests as a shareholder on the corporate governance environment, and concludes by offering several policy recommendations.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

The effects of state ownership in the internationalization of emerging multinationals

Esta tese é um compendio de três trabalhos de pesquisa que visam analisar o efeito da participação do estado na estrutura de propriedade das Multinacionais de Países Emergentes (EMNEs). A participação do estado como acionista é um fenómeno que pode trazer novas contribuições no âmbito da governança corporativa, administração da empresa e a tomada de decisões estratégicas. Os estudos aqui inclusos permitem identificar, a partir de momentos, diferentes, até que ponto o estado, na posição de proprietário da EMNE, pode impactar a mesma. Os trabalhos vão desde os mecanismos de escolha de firmas nas quais investir ate o impacto no ritmo de internacionalização das empresas, explicando também os mecanismos que o estado usa para ganhar aceso à tomada de decisões por meio de mudanças na governança corporativa

Ownership, control and corporate valuation of Brazilian companies

We study the direct and indirect ownership structure of Brazilian corporations and their market value and risk by the end of 1996 and 1998. Ownership is quite concentrated with most companies being controlled by a single direct shareholder. We find evidence that indirect control structures may be used to concentrate control even more rather than to keep control of the company with a smaller share of total capital. The greater the concentration of voting rights then less the value of the fmn should be due to potential expropriation ofrninority shareholders. We fmd evidence that when there is a majority shareholder and when indirect ownership structures are used without the loss of control, corporate valuations are greater when control is dilluted through the indirect ownership structure. This evidence is consistent with the existence of private benefits of control that can be translated as potential minority shareholder expropriation.

Valuation and performance of firms in complex ownership structures : an application to Korean chaebols

We propose several new metrics to describe the complex ownership structure of business groups, and provide simple formulas and algorithms to compute these metrics. We use these measures to describe in detail the ownership structure of Korean chaebols in the period of 2003 to 2004. In addition, we validate the usefulness of our new metrics by showing empirically that they are important for understanding the valuation and performance of group firms. In particular, we show evidence that firms that are central to the control structure of the chaebol (central firms), firms in cross-shareholdings, and firms that are placed at the bottom of the group (i.e., with lower ultimate ownership) have lower profitability than other group firms. The valuation results suggest that central firms and firms in cross-shareholding loops have lower valuations than other public Chaebol firms. The lower valuation of these firms is not explained by variation in measures of ownership concentration and separation between ownership and control.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.