Multivariate Hyper-Rotated GARCH-BEKK

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

Estimating Impulse-Response Functions for Macroeconomic Models using Directional Quantiles

A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate quantile processes. The distribution of the responses can then be obtained by using uniformly distributed random vectors. An empirical example of exchange rate pass-through in Argentina is presented.

A Robust Test for Monotonicity in Asset Returns

In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics of an asset. Existing tests in literature fail to control the probability of a type 1 error or have low power under heavy-tailed distributions of return differentials. Monte Carlo simulations illustrate that our test statistic has a correct empirical size under all data-generating processes together with a similar power to other tests. Conversely, alternative tests are nonconservative under conditional heteroskedasticity or heavy-tailed distributions of return differentials. We also present an empirical application on the monotonicity of returns on various portfolios sorts that highlights the usefulness of our approach.

On a Different way of Understanding the Edge-Effect for the Inference of ARMA-type Processes (in Zd${\mathbb{Z}}^{d}$)

The edge-effect concerning the standard estimators’ bias for the parameters of multi-indexed ARMA-type series is a common hurdle; it is investigated whether an alternative ARMA parameterization might release any unwelcome complication. The theoretical blocks, of when the factorized model is free of the edge-effect, are provided and simulation results are used to reinforce the same views. Estimation or other perspectives are discussed in a conclusion.

Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors

Johansen’s (2000. “A Bartlett Correction Factor for Tests of on the Cointegrating Relations.” Econometric Theory 16: 740–78) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to financial variables is fat-tailed and often skewed; there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using the non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require specification of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than it’s analytical counterpart. The procedure involves calculating a number of bootstrap values of the LR test statistic and estimating the expected value of the test statistic by the average value of the bootstrapped LR statistic. Simulation results suggest that the inference procedure has good finite sample property and is less dependent on the parameter space of the data generating process.

The Export of Commodities and the Validity of the Export-Led Growth (ELG) Hypothesis for the Brazilian Economy: An Analysis of the Commodity Boom Period

The present study examines the Brazilian economy in the light of the export-led growth (ELG) hypothesis, in order to examine if this hypothesis is valid for periods in which commodities occupy a significant part of exports, for this reason, for the period known as the “commodity boom”. In order to address the proposed objective, the estimation method used was the autoregression with vector error correction (VEC) in its structural version. The results suggest that the economic growth that occurred in Brazil during the analysed period does not corroborate the ELG hypothesis, which is endorsed by results obtained in similar studies.

Seasonal Adjustment of Daily Time Series

Currently, the methods used by producers of official statistics do not facilitate the seasonal and calendar adjustment of daily time series, even though an increasing number of series with daily observations are available. The aim of this paper is the development of a procedure to estimate and adjust for periodically recurring systematic effects and the influence of moving holidays in time series with daily observations. To STL based seasonal adjustment routine is combined with a RegARIMA model for the estimation of calendar and outlier effects. The procedure is illustrated and validated using a set of daily time series with different seasonal characteristics as well as simulated data. The developed procedure closes a gap by facilitating the seasonal and calendar adjustment of daily time series.

Variable Selection in Regression Models Using Global Sensitivity Analysis

Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ‘Pantula-principle’ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection.

Estimation of Continuous and Discrete Time Co-integrated Systems with Stock and Flow Variables

This paper proposes an exact discrete time error correction model for co-integrated systems in continuous time and outlines a computationally efficient algorithm that leads to the Gaussian estimates of the model’s parameters. Its performance in estimation is assessed by contrasting our estimates with those obtained after applying Johansen’s discrete time approach to cointegrated systems. The data, for analysis, consist of two simulated systems; one comprised entirely of stock variables and another one formed by flow variables. In the results, we show that for the system with stock variables Johansen’s approach and ours perform similarly. For the system with flow variables, however, Johansen’s estimates show a persistent estimation bias with negligible improvements in larger samples, while ours yields a smaller bias that lowers as the sample size increases. As our model incorporates a moving average component in the error term that permits full dynamics, we argue that Johansen’s bias reflects the cost of ignoring aggregation in the specification.