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.

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 Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

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.

Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

Leader-organization fit: comparing the effectiveness of paternalistic and transformational leadership in different organizational cultures

Research on paternalistic leadership (PL) has been based exclusively on national cultures´ differences. However there are cues that other contextual variables can add to the explanation of this construct. Due to its capacity to influence expectations of individuals in organizations, organizational culture can contribute to fill this gap. To test if organizational culture influences the effectiveness of leadership style, we conducted two experimental studies using Amazon’s Mechanical Turk, comparing effects of paternalistic and transformational leadership on followers’ outcomes. Using video clips and vignettes, we found that PL is better related to followers´ outcomes in cultures oriented to people than outcome, and that TL has a better relationship in cultures oriented to innovation than stability. The results suggest that organizational culture helps in explaining PL endorsement, and that further analysis of the influence of this variable to PL can provide a better understanding of the expression of this leadership style in organizations.

Improvement on the sales forecast accuracy for a fast growing company by the best combination of historical data usage and clients segmentation

Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.

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.

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.

Automatic model selection for forecasting Brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

On paternalistic leadership fit: exploring cross-cultural endorsement, leader-follower fit, and the boundary role of organizational culture

Although cross-cultural leadership research has thrived in international business literature, little attention has been devoted to understanding the effectiveness of non-western theories beyond their original contexts. The purpose of this study is to examine the cross-cultural endorsement of paternalistic leadership, an emerging non-western leadership theory, using data from GLOBE project. Using multigroup confirmatory factor analyses we found measurement equivalence of a scale derived from GLOBE’s data, which enabled us to compare the endorsement of paternalistic leadership dimensions across 10 cultural clusters and 55 societies. Our study revealed that there are significant differences in the importance societies give to each dimension, suggesting that paternalism as leadership style is not universally nor homogeneously endorsed. Furthermore, results suggest that different patterns of endorsement of each of these dimensions give rise to idiosyncratic shades of paternalistic leadership across societies. Implications for theory and future research on international business are discussed.

The art of being together: a social constructionist perspective on dialogic methods in the organizational context

The purpose of this project is to understand, under a social constructionist approach, what are the meanings that external facilitators and organizational members (sponsors) working with dialogic methods place on themselves and their work. Dialogic methods, with the objective of engaging groups in flows of conversations to envisage and co-create their own future, are growing fast within organizations as a means to achieve collective change. Sharing constructionist ideas about the possibility of multiple realities and language as constitutive of such realities, dialogue has turned into a promising way for transformation, especially in a macro context of constant change and increasing complexity, where traditional structures, relationships and forms of work are questioned. Research on the topic has mostly focused on specific methods or applications, with few attempts to study it in a broader sense. Also, despite the fact that dialogic methods work on the assumption that realities are socially constructed, few studies approach the topic from a social constructionist perspective, as a research methodology per se. Thus, while most existing research aims at explaining whether or how particular methods meet particular results, my intention is to explore the meanings sustaining these new forms of organizational practice. Data was collected through semi-structured interviews with 25 people working with dialogic methods: 11 facilitators and 14 sponsors, from 8 different organizations in Brazil. Firstly, the research findings indicate several contextual elements that seem to sustain the choices for dialogic methods. Within this context, there does not seem to be a clear or specific demand for dialogic methods, but a set of different motivations, objectives and focuses, bringing about several contrasts in the way participants name, describe and explain their experiences with such methods, including tensions on power relations, knowledge creation, identity and communication. Secondly, some central ideas or images were identified within such contrasts, pointing at both directions: dialogic methods as opportunities for the creation of new organizational realities (with images of a ‘door’ or a ‘flow’, for instance, which suggest that dialogic methods may open up the access to other perspectives and the creation of new realities); and dialogic methods as new instrumental mechanisms that seem to reproduce the traditional and non-dialogical forms of work and relationship. The individualistic tradition and its tendency for rational schematism - pointed out by social constructionist scholars as strong traditions in our Western Culture - could be observed in some participants’ accounts with the image of dialogic methods as a ‘gym’, for instance, in which dialogical – and idealized –‘abilities’ could be taught and trained, turning dialogue into a tool, rather than a means for transformation. As a conclusion, I discuss what the implications of such taken-for-granted assumptions may be, and offer some insights into dialogue (and dialogic methods) as ‘the art of being together’.