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on Econometrics |
| By: | Lewis, Daniel J. (Federal Reserve Bank of New York) |
| Abstract: | An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances is available only under specific parametric assumptions on the variance process. I propose a new identification argument that identifies the SVAR up to shock orderings using the autocovariance structure of second moments of the residuals implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions like those required by existing approaches. I offer intuitive criteria to select among shock orderings; this selection does not impact inference asymptotically. The identification scheme performs well in simulations. I apply it to the debate on fiscal multipliers. I obtain estimates that are lower than those of Blanchard and Perotti (2002) and Mertens and Ravn (2014), but in line with those of more recent studies. |
| Keywords: | identification; impulse response function; structural shocks; SVAR; fiscal multiplier; time-varying volatility; heteroskedasticity |
| JEL: | C32 C58 E20 E62 H30 |
| Date: | 2018–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:871&r=ecm |
| By: | Maurizio Daniele (University of Konstanz); Winfried Pohlmeier (University of Konstanz); Aygul Zagidullina (University of Konstanz) |
| Abstract: | We propose a novel estimation approach for the covariance matrix based on the l1-regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard approximate factor model. We prove consistency of the covariance matrix estimator under the Frobenius norm as well as the consistency of the factor loadings and the factors. Our Monte Carlo simulations reveal that the SAF covariance estimator has superior properties in finite samples for low and high dimensions and different designs of the covariance matrix. Moreover, in an out-of-sample portfolio forecasting application the estimator uniformly outperforms alternative portfolio strategies based on alternative covariance estimation approaches and modeling strategies including the 1/N-strategy. |
| Keywords: | Approximate Factor model, weak factors, l1-regularization, high dimensional covariance matrix, portfolio allocation |
| JEL: | C38 G11 G17 |
| Date: | 2018–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:knz:dpteco:1807&r=ecm |
| By: | Cuicui Lu; Weining Wang; Jeffrey M. Wooldridge |
| Abstract: | In this paper, we study estimation of nonlinear models with cross sectional data using two-step generalized estimating equations (GEE) in the quasi-maximum likelihood estimation (QMLE) framework. In the interest of improving efficiency, we propose a grouping estimator to account for the potential spatial correlation in the underlying innovations. We use a Poisson model and a Negative Binomial II model for count data and a Probit model for binary response data to demonstrate the GEE procedure. Under mild weak dependency assumptions, results on estimation consistency and asymptotic normality are provided. Monte Carlo simulations show efficiency gain of our approach in comparison of different estimation methods for count data and binary response data. Finally we apply the GEE approach to study the determinants of the inflow foreign direct investment (FDI) to China. |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1810.05855&r=ecm |
| By: | Marcello Pericoli (Bank of Italy); Marco Taboga (Bank of Italy) |
| Abstract: | We propose a general method for the Bayesian estimation of nonlinear no-arbitrage term structure models. The main innovations we introduce are: 1) a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy; and 2) computational graph optimizations for accelerating the MCMC sampling of the model parameters and of the unobservable state variables that drive the short-term interest rate. We apply the proposed techniques for estimating a shadow rate model with a time-varying lower bound, in which the shadow rate can be driven by both spanned unobservable factors and unspanned macroeconomic factors. |
| Keywords: | yield curve, shadow rate, deep learning, artificial intelligence |
| JEL: | C32 E43 G12 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1189_18&r=ecm |
| By: | Tian, Jing (Tasmanian School of Business & Economics, University of Tasmania); Goodwin, Thomas |
| Abstract: | We propose an unobserved modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework can be used to analyze the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decisions. |
| Keywords: | Decision making, decomposition, evaluating forecasts, state space models, weather forecasting |
| JEL: | C32 C53 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:28354&r=ecm |
| By: | Catalina Bolancé (RISKCENTER-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona); Montserrat Guillén (RISKCENTER-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona); Arelly Ornelas (RISKCENTER-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona) |
| Abstract: | The aim of this paper is to estimate the longevity risk and its trend according to the age of the individual. We focus on individuals over 65. We use the value-at-risk to measure the longevity risk. We have proposed the use of an alternative methodology based on the estimation of the truncated cumulative distribution function and the quantiles. We apply a robust estimation method for fitting parametric distributions. Finally, we compare parametric and nonparametric estimations of longevity risk. |
| Keywords: | Longevity, value-at-risk, nonparametric inference. |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:xrp:wpaper:xreap2018-5&r=ecm |
| By: | George Kapetanios; Stephen Millard; Katerina Petrova; Simon Price |
| Abstract: | We re-examine the great ratios associated with balanced growth models and ask whether they have remained constant over time. We first use a benchmark DSGE model to explore how plausible smooth variations in structural parameters lead to movements in great ratios that are comparable to those seen in the UK data. We then employ a nonparametric methodology that allows for slowly varying coefficients to estimate trends over time. To formally test for stable relationships in the great ratios, we propose a statistical test based on these nonparametric estimators devised to detect time varying cointegrating relationships. Small sample properties of the test are explored in a small Monte Carlo exercise. Generally, we find no evidence for cointegration when parameters are constant, but strong evidence when allowing for time variation. The implications are that in macroeconometric models allowance should be made for shifting long-run relationships, including DSGE models where smooth variation should be allowed in the deep structural relationships. |
| Keywords: | Time variation, great ratios, cointegration |
| JEL: | C14 C26 C51 O4 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2018-53&r=ecm |