Generalized autoregressive conditional heteroscedasticity (GARCH)-type models have been successively used to capture the conditional volatility of macroeconomic and financial time series in the past two decades. However, few diagnostic tests are specifically devised to check the adequacy of...

The low-frequency movements of many economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes, which is guided by a judicious choice of priors and is characterized by sparsity. We present some...

The study uses the quarterly data covering the period 1980-2012 to empirically examine the impact of budget deficit on inflation in South Africa with the view to find the direction of causation and establish any cointegration existence. The study uses the Vector Autoregression (VAR) analysis...

The article is devoted to estimating the uncertainty parameters of Russian GDP on history, that arises as a result of revisions and refinements of data over time. A brief review of the reasons for the revisions allows us to form an understanding of their necessity and importance. For analysis,...

We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense,...

In this note, we build upon the asymptotic theory for GARCH processes, considering the general class of augmented GARCH(p, q) processes. Our contribution is to complement the well-known univariate asymptotics by providing a bivariate functional central limit theorem between the sample quantile...

We seek to determine whether a United States President's job approval rating is influenced by the Misery Index. This hypothesis is examined in two ways. First, we employ a nonlinear model that includes several macroeconomic variables: the current account deficit, exchange rate, unemployment,...

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional factor model implemented by the principal component analysis. Using the extracted factors, we develop a link-free...

Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both...

The distribution of lifetime is one of the most important factors for the determination of the population volume. Knowing the population volume is of great importance for the future planning. For the future planning depending on the population volume, it is necessary to know the distribution of...