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on Econometric Time Series |
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Issue of 2020‒02‒24
eleven papers chosen by Jaqueson K. Galimberti Auckland University of Technology |
| By: | Benjamin Poignard (Graduate School of Economics, Osaka University); Manabu Asaiz (FacultyofEconomics,SokaUniversity) |
| Abstract: | Although multivariate stochastic volatility (MSV) models usually produce more accurate forecasts compared to multivariate GARCH models, their estimation techniques such as Monte Carlo likelihood or Bayesian Markov Chain Monte Carlo are computationally demanding and thus suffer from the so-called gcurse of dimensionality": using such methods, the applications are typically restricted to low-dimensional vectors. In this paper, we propose a fast estimation approach for MSV models based on a penalised ordinary least squares framework. Specifying the MSV model as a multivariate state-space model, we propose a two-step penalised procedure for estimating the latter using a broad range of potentially non-convex penalty functions. In the first step, we approximate an EGARCH type dynamic using a penalised AR process with a sufficiently large number of lags, providing a sparse estimator. Conditionally on this first step estimator, we estimate the state vector based on a AR type dynamic. This two-step procedure relies on OLS based loss functions and thus easily accommodates high-dimensional vectors. We provide the large sample properties of the two-step estimator together with the so- called support recovery of the first step estimator. The empirical performances of our method are illustrated through in-sample simulations and out-of-sample variance-covariance matrix forecasts, where we consider as competitors commonly used MGARCH models. |
| Keywords: | Forecasting;MultivariateStochasticVolatility;OracleProperty;PenalisedM-estimation |
| JEL: | C13 C32 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:2002&r=all |
| By: | Zongwu Cai (Department of Economics, The University of Kansas); Seong Yeon Chang (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China) |
| Abstract: | This paper considers predictive regressions where a structural break is allowed at some unknown date. We establish novel testing procedures for testing predictability via empirical likelihood methods based on some weighted score equations. Theoretical results are useful in practice because we adopt a unified framework under which it is unnecessary to distinguish whether the predictor variables are stationary or nonstationary. In particular, nonstationary predictor variables are allowed to be (nearly) integrated or exhibit a structural change at some unknown date. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables. |
| Keywords: | Autoregressive process; Empirical likelihood; Structural change; Unit root; Weighted estimation |
| JEL: | C12 C14 C32 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:201811&r=all |
| By: | Morten Ørregaard Nielsen (Queen's University and CREATES); Antoine Noël |
| Abstract: | This paper provides an exact algorithm for efficient computation of the time series of conditional variances, and hence the likelihood function, of models that have an ARCH($\infty$) representation. This class of models includes, e.g., the fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model. Our algorithm is a variation of the fast fractional difference algorithm of \cite{JensenNielsen2014}. It takes advantage of the fast Fourier transform (FFT) to achieve an order of magnitude improvement in computational speed. The efficiency of the algorithm allows estimation (and simulation/bootstrapping) of ARCH($\infty$) models, even with very large data sets and without the truncation of the filter commonly applied in the literature. We also show that the elimination of the truncation of the filter substantially reduces the bias of the quasi-maximum-likelihood estimators. Our results are illustrated in two empirical examples. |
| Keywords: | Circular convolution theorem, Conditional heteroskedasticity, Fast Fourier transform, FIGARCH, Truncation |
| JEL: | C22 C58 C63 C87 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1425&r=all |
| By: | Delle Monache, Davide; Venditti, Fabrizio; Petrella, Ivan |
| Abstract: | In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying parameters. We use this method to study the time-varying relationship between the price dividend ratio, expected stock returns and expected dividend growth in the US since 1880. We find a significant increase in the long-run equilibrium value of the price dividend ratio over time, associated with a fall in the long-run expected rate of return on stocks. The latter can be attributed mainly to a decrease in the natural rate of interest, as the long-run risk premium has only slightly fallen. JEL Classification: C22, C32, C51, C53, E31 |
| Keywords: | equity premium, present-value models., score-driven models, state space models, time-varying parameters |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20202369&r=all |
| By: | Krüger, Jens; Ruths Sion, Sebastian |
| Abstract: | In this paper we document the results of a forecast evaluation exercise for the real world price of crude oil using VAR models estimated by sparse (regularization) estimators. These methods have the property to constrain single parameters to zero. We find that estimating VARs with three core variables (real price of oil, index of global real economic activity, change in global crude oil production) by the sparse methods is associated with substantial reductions of forecast errors. The transformation of the variables (taking logs or differences) is also crucial. Extending the VARs by further variables is not associated with additonal gains in forecast performance as is the application of impulse indicator saturation before the estimation. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:118208&r=all |
| By: | Matteo, Pelagatti; Giacomo, Sbrana |
| Abstract: | This paper proposes tree main results that enable the estimation of high dimensional multivariate stochastic volatility models. The first result is the closed-form steady-state Kalman filter for the multivariate AR(1) plus noise model. The second result is an accelerated EM algorithm for parameters estimation. The third result is an estimator of the correlation of two elliptical random variables with time-varying variances that is consistent and asymptotically normal regardless of the variances evolution. Speed and precision of our methodology are evaluated in a simulation experiment. Finally, we implement our method and compare its performance with other approaches in a minimum variance portfolio composed by the constituents of the CAC40 and S&P100 indexes. |
| Keywords: | Riccati equation, EM algorithm, Kalman filter, Correlation estimation, Large covariance matrix, Multivariate stochastic volatility |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:mib:wpaper:428&r=all |
| By: | Lindeløv, Jonas Kristoffer |
| Abstract: | The R package mcp does flexible and informed Bayesian regression with change points. mcp can infer changes in means, variances, autocorrelation structure, and any combination of these. Regression models can be specified on a segment-by-segment basis, including regression on variance and autoregressive parameters. Prior and posterior samples and summaries are returned for all parameters as well as a rich set of plotting options. Bayes Factors can be computed via Savage-Dickey density ratio and posterior contrasts. Cross-validation can be used for a more general model comparison. mcp ships with sensible defaults, including priors, but many options for the user to take control of these settings. The strengths and limitations of mcp are discussed in relation to existing change point packages in R. |
| Date: | 2020–01–05 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:fzqxv&r=all |
| By: | André Lucas (Vrije Universiteit Amsterdam); Julia Schaumburg (Vrije Universiteit Amsterdam); Bernd Schwaab (European Central Bank, Financial Research) |
| Abstract: | We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of interest can transition between clusters over time based on a Hidden Markov model (HMM). Finally, the HMM’s transition matrix can depend on lagged cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of settings. An empirical study of 299 European banks between 2008Q1 and 2018Q2 suggests that banks have become less diverse over time in key characteristics. On average, approximately 3% of banks transition each quarter. Transitions across clusters are related to cluster dissimilarity and differences in bank profitability. |
| Keywords: | dynamic clustering, panel data, Hidden Markov Model, score-driven dynamics, bank business models |
| JEL: | G21 C33 |
| Date: | 2020–02–04 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20200009&r=all |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Haiqiang Chen (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China); Xiaosai Liao (Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China) |
| Abstract: | This paper studies asset return predictability via quantile regression for all types of persistent regressors. We propose to estimating a quantile regression with an auxiliary regressor and constructing a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor, together with a novel test procedure. We show that it can reach the local power under the different optimal rates for nonstationary and stationary predictors, respectively. Our approach can be easily implemented to test the joint predictive ability of financial variables in multiple regression. The heterogenous predictability of US stock returns at different quantile levels is reexamined. |
| Keywords: | Auxiliary regressor; Highly persistent predictor; Multiple regression; Predictive quantile regression; Robust inference; Weighted estimator |
| JEL: | C22 C32 C58 G12 G14 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202002&r=all |
| By: | Chikako Baba; Salvatore Dell'Erba; Enrica Detragiache; Olamide Harrison; Aiko Mineshima; Anvar Musayev; Asghar Shahmoradi |
| Abstract: | Assessing when credit is excessive is important to understand macro-financial vulnerabilities and guide macroprudential policy. The Basel Credit Gap (BCG) – the deviation of the credit-to-GDP ratio from its long-term trend estimated with a one-sided Hodrick-Prescott (HP) filter—is the indicator preferred by the Basel Committee because of its good performance as an early warning of banking crises. However, for a number of European countries this indicator implausibly suggests that credit should go back to its level at the peak of the boom after the credit cycle turns, resulting in large negative gaps that might delay the activation of macroprudential policies. We explore two different approaches—a multivariate filter based on economic theory and a fundamentals-based panel regression. Each approach has pros and cons, but they both provide a useful complement to the BCG in assessing macro-financial vulnerabilities in Europe. |
| Keywords: | Real interest rates;Interest rate policy;Credit booms;Credit expansion;Credit aggregates;Credit Cycle,Credit Gap,Countercyclical Capital Buffer,Macroprudential Policies,WP,BCG,real interest rate,output gap,fundamental variable |
| Date: | 2020–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:20/6&r=all |
| By: | Dieppe,Alistair Matthew; Neville,Francis; Kindberg Hanlon,Gene Joseph |
| Abstract: | This paper addresses the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restricted VARs are well documented, the recent attempt to overcome said issues using the Max-Share approach of Francis et al. (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks. A modification to the Max-Share approach and two further spectral methods are proposed to improve empirical identification. Performance directly hinges on whether these confounding shocks are of high or low frequency. Applied to US and emerging market data, spectral identifications are most robust across specifications, and non-technology shocks appear to be biasing traditional methods of identifying technology shocks. These findings also extend to the SVAR identification of dominant business-cycle shocks, which are shown will be a variance-weighted combination of shocks rather than a single structural driver. |
| Keywords: | Labor Markets,Macroeconomic Management,National Governance,Social Analysis,Quality of Life&Leisure,Youth and Governance,Government Policies,Public Finance Decentralization and Poverty Reduction,Macro-Fiscal Policy,Taxation&Subsidies,Public Sector Economics,Economic Adjustment and Lending |
| Date: | 2019–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:9047&r=all |