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on Econometric Time Series |
| By: | Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies, Japan; Rimini Centre for Economic Analysis); Blessings Majoni (National Graduate Institute for Policy Studies, Japan) |
| Abstract: | We obtain a novel analytic expression of the likelihood for a stationary inverse gamma Stochastic Volatility (SV) model. This allows us to obtain the Maximum Likelihood Estimator for this non linear non Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixture of gammas and therefore we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for 7 currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for 4 countries currency data and for 2 countries inflation data. |
| Keywords: | Hypergeometric Function, Particle Filter, Parallel Computing, Euler Acceleration |
| JEL: | C32 C58 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:23-11&r=ets |
| By: | Andrea Carriero; Massimiliano Marcellino; Tommaso Tornese |
| Abstract: | We propose a blended approach which combines identification via heteroskedasticity with the widely used methods of sign restrictions, narrative restrictions, and external instruments. Since heteroskedasticity in the reduced form can be exploited to point identify a set of orthogonal shocks, its use results in a sharp reduction of the potentially large identified sets stemming from the typical approaches. Conversely, the identifying information in the form of sign and narrative restrictions or external instruments can prove necessary when the conditions for point identification through heteroskedasticity are not met and offers a natural solution to the labeling problem inherent in purely statistical identification strategies. As a result, we argue that blending these methods together resolves their respective key issues and leverages their advantages, which allows to sharpen identification. We illustrate the blending approach in an artificial data experiment first, and then apply it to several examples taken from recent and influential literature. Specifically, we consider labour market shocks, oil market shocks, monetary and fiscal policy shocks, and find that their effects can be rather different from what previously obtained with simpler identification strategies. |
| Keywords: | SVAR, Identification, Heteroskedasticity, Sign restrictions, Proxy variables |
| JEL: | C11 C32 D81 E32 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp23200&r=ets |
| By: | Marian Vavra (National Bank of Slovakia) |
| Abstract: | This paper examines the small sample properties of a linear programming estimator in time series quantile regression models. Under certain regularity conditions, the estimator produces consistent and asymptotically normally distributed estimates of model parameters. However, despite these desirable asymptotic properties, we find that the estimator performs rather poorly in small samples. We suggest the use of a subsampling method to correct for a bias and discuss a simple rule of thumb for setting a block size. Our simulation results show that the subsampling method can effectively reduce the bias at very low computational costs and without significantly increasing the root mean squared error of the estimated parameters. The importance of bias correction for economic policy is highlighted in a growth-at-risk application. |
| JEL: | C15 C22 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:svk:wpaper:1094&r=ets |
| By: | Kohns, David (Aalto University); Potjagailo, Galina (Bank of England) |
| Abstract: | We propose a mixed‑frequency regression prediction approach that models a time‑varying trend, stochastic volatility and fat tails in the variable of interest. The coefficients of high‑frequency indicators are regularised via a shrinkage prior that accounts for the grouping structure and within‑group correlation among lags. A new sparsification algorithm on the posterior motivated by Bayesian decision theory derives inclusion probabilities over lag groups, thus making the results easy to communicate without imposing sparsity a priori. An empirical application on nowcasting UK GDP growth suggests that group‑shrinkage in combination with the time‑varying components substantially increases nowcasting performance by reading signals from an economically meaningful subset of indicators, whereas the time‑varying components help by allowing the model to switch between indicators. Over the data release cycle, signals initially stem from survey data and then shift towards few ‘hard’ real activity indicators. During the Covid pandemic, the model performs relatively well since it shifts towards indicators for the service and housing sectors that capture the disruptions from economic lockdowns. |
| Keywords: | Bayesian MIDAS regressions; forecasting; time‑variation and fat tails; grouped horseshoe prior; decision analysis |
| JEL: | C11 C32 C44 C53 E37 |
| Date: | 2023–06–02 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:1025&r=ets |
| By: | Dong, Hao; Otsu, Taisuke; Taylor, Luke |
| Abstract: | We propose a semiparametric estimator for varying coefficient models when the regressors in the nonparametric components are measured with error. Varying coefficient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori. Instead, we suppose we have access to a repeated measurement of the noisy regressor and present results using the approach of Delaigle, Hall and Meister (2008) and, for cases when the measurement error may be asymmetric, the approach of Li and Vuong (1998) based on Kotlarski's (1967) identity. We show that the convergence rate of the estimator is significantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. We study the small sample behaviour of our estimator in a simulation study and apply it to a real dataset. In particular, we consider the role of cognitive ability in augmenting the effect of risk preferences on earnings. |
| Keywords: | Consolidator Grant (SNP 615882); Research Fund (AUFF-26852) |
| JEL: | J1 |
| Date: | 2022–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:108147&r=ets |
| By: | Tommaso Tornese |
| Abstract: | Using monthly data for Belgium, France, Germany, Italy and Spain for the period 2002-2019, we build a Hierarchical Euro Area Dynamic Nelson-Siegel model that allows for time varying exposures of national factors on the common components, and for stochastic volatility both at the regional and country specific level. Despite the share of national variance explained by the Euro Area factors is generally dominant, our results point out a dramatic decrease of the relative importance of common forces during the 2008 and 2012 crises, which created a neat separation between “core” and “peripheral” countries. This gap is particularly visible in the term premia demanded by investors on long term sovereign bonds. Furthermore, in line with Byrne et al. (2019), we find that both the level of interest rates and the associated term premia are closely related to confidence and uncertainty measures. In the aftermath of the crises these relationships appear weakened, presumably due to unconventional interventions of the ECB. |
| Keywords: | Term structure, Factor Model, Euro Area, Time-varying loadings, Stochastic volatility. |
| JEL: | C11 C32 E43 F36 G15 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp23199&r=ets |