|
on Econometric Time Series |
| By: | Tomás Caravello; Zacharias Psaradakis; Martín Sola |
| Abstract: | Issues that arise in the practical implementation of the Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015a) recursive procedures for identifying and dating explosive bubbles in time-series data are considered. It is argued that the standard practice of using conventional levels of significance for critical values involved in the algorithms that locate the origination and termination dates of explosive episodes lead to false discoveries of explosiveness with large probability. In addition, the use of critical values for right-tailed unit-root tests obtained under the assumption of a drift whose magnitude depends on the sample size and becomes negligible in large samples result in over-rejection of the unit-root hypothesis when, as in many financial time series, the drift effect is non-negligible relatively to the stochastic trend. The magnitude of these difficulties is quantified via simulations, using artificial data whose stochastic properties reflect closely those of real-world time series such as asset prices and dividends. The findings offer a potential explanation for the relatively large number of apparent explosive episodes that are often reported in applied work. An empirical illustration involving monthly data on U.S. real stock prices and real dividends is also discussed. |
| Keywords: | Bubbles, Date-stamping, Explosive behaviour, Recursive, Unit-root test. |
| JEL: | C12 C15 C22 |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:udt:wpecon:2021_06&r= |
| By: | Jérôme Trinh (Université de Cergy-Pontoise, THEMA) |
| Abstract: | In this paper, we propose a method to disaggregate very small time series by fitting them with higher frequency related series using a cointegration regression with multiple partial endogenous structural breaks. We allow any coecient to change at up to two dates of structural break and three related series and provide critical values for the test of cointegration corrected for the very small sample size. We find that increasing the num- ber of related series drastically improves the power of the test by allowing for increased flexibility in the cointegration model. The simulated power of the test is shown to be very high even in very small sample sizes such as fifteen observations. This flexibility also mildly improves the accuracy of the disaggregation method when the sample size is as small as thirty-five observations. An application to the Chinese national accounts data is provided and allows the study of the Chinese business cycles stylized facts. We find that household consumption, public spending, and trade surpluses are the main driver of the business cycle. |
| Keywords: | Time series, macroeconomic forecasting, disaggregation, structural change, business cycles, emerging economies |
| JEL: | C32 E17 E37 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ema:worpap:2022-10&r= |
| By: | Marko Mlikota; Frank Schorfheide |
| Abstract: | Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and a nonlinear dynamic stochastic general equilibrium model. The runtime reductions we obtain range from 27% to 88%. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.07070&r= |
| By: | Archil Gulisashvili |
| Abstract: | We establish a comprehensive sample path large deviation principle (LDP) for log-processes associated with multivariate time-inhomogeneous stochastic volatility models. Examples of models for which the new LDP holds include Gaussian models, non-Gaussian fractional models, mixed models, models with reflection, and models in which the volatility process is a solution to a Volterra type stochastic integral equation. The LDP for log-processes is used to obtain large deviation style asymptotic formulas for the distribution function of the first exit time of a log-process from an open set and for the price of a multidimensional binary barrier option. We also prove a sample path LDP for solutions to Volterra type stochastic integral equations with predictable coefficients depending on auxiliary stochastic processes. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.09015&r= |
| By: | Pesaran, M. H.; Pick, A.; Timmermann, A. |
| Abstract: | We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of parameter heterogeneity. We investigate conditions under which panel forecasting methods can perform better than forecasts based on individual estimates and demonstrate how gains in predictive accuracy depend on the degree of parameter heterogeneity, whether heterogeneity is correlated with the regressors, the goodness of fit of the model, and, particularly, the time dimension of the data set. We propose optimal combination weights for forecasts based on pooled and individual estimates and develop a novel forecast poolability test that can be used as a pretesting tool. Through a set of Monte Carlo simulations and three empirical applications to house prices, CPI inflation, and stock returns, we show that no single forecasting approach dominates uniformly. However, forecast combination and shrinkage methods provide better overall forecasting performance and offer more attractive risk profiles compared to individual, pooled, and random effects methods. |
| Keywords: | Forecasting, Panel data, Heterogeneity, Forecast evaluation, Forecast combination, Shrinkage, Pooling |
| JEL: | C33 C53 |
| Date: | 2022–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2219&r= |