STAR model

About: STAR model is a research topic. Over the lifetime, 2661 publications have been published within this topic receiving 101945 citations.

Improved Variational Inference with Inverse Autoregressive Flow

Abstract: The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network. In experiments, we show that IAF significantly improves upon diagonal Gaussian approximate posteriors. In addition, we demonstrate that a novel type of variational autoencoder, coupled with IAF, is competitive with neural autoregressive models in terms of attained log-likelihood on natural images, while allowing significantly faster synthesis.

Characterizing nonlinearities in business cycles using smooth transition autoregressive models

Abstract: During the past few years investigators have found evidence indicating that various time-series representing business cycles, such as production and unemployment, may be nonlinear. In this paper it is assumed that if the time-series is nonlinear, then it can be adequately described by a smooth transition autoregressive (STAR) model. The paper describes the application of these models to quarterly logarithmic production indices for 13 countries and ‘Europe’. Tests reject linearity for most of these series, and estimated STAR models indicate that the nonlinearity is needed mainly to describe the responses of production to large negative shocks such as oil price shocks.

Evidence on Structural Instability in Macroeconomic Time Series Relations

Abstract: An experiment is performed to assess the prevalence of instability in univariate and bivariate macroeconomic time series relations and to ascertain whether various adaptive forecasting techniques successfully handle any such instability. Formal tests for instability and out-of-sample forecasts from sixteen different models are computed using a sample of 76 representative U.S. monthly postwar macroeconomic time series, constituting 5700 bivariate forecasting relations. The tests indicate widespread instability in univariate and bivariate autoregressive models. However, adaptive forecasting models, in particular time varying parameter models, have limited success in exploiting this instability to improve upon fixed-parameter or recursive autoregressive forecasts.

Selection of the order of an autoregressive model by Akaike's information criterion

Abstract: SUMMARY The asymptotic distribution is obtained of the order of regression selected by Akaike's information criterion in autoregressive models. The asymptotic quadratic risks of estimates of regression parameters are evaluated when the order is selected by this method. Some results of computational experiments are given.

Characterizing nonlinearities in business cycles using smooth transition

Abstract: SUMMARY During the past few years investigators have found evidence indicating that various time-series representing business cycles, such as production and unemployment, may be nonlinear. In this paper it is assumed that if the time-series is nonlinear, then it can be adequately described b a smooth transition autoregressive (STAR) model. The paper describes the application of these models to quarterly logarithmic production indices for 13 countries and 'Europe'. Tests reject linearity for most of these series, and estimated STAR models indicate that the nonlinearity is needed mainly to describe the responses of production to large negative shocks such as oil price shocks.