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on Econometric Time Series |
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Issue of 2021‒02‒22
fourteen papers chosen by Jaqueson K. Galimberti Auckland University of Technology |
| By: | Georgiev, Iliyan; Demetrescu, Matei; Rodrigues, Paulo MM; Taylor, AM Robert |
| Abstract: | Predictive regression methods are widely used to examine the predictability of (excess) returns on stocks and other equities by lagged macroeconomic and financial variables. Extended IV [IVX] estimation and inference has proved a particularly valuable tool in this endeavour as it allows for possibly strongly persistent and endogenous regressors. This paper makes three distinct contributions to the literature. First we demonstrate that, provided either a suitable bootstrap implementation is employed or heteroskedasticity-consistent standard errors are used, the IVX-based predictability tests of Kostakis et al. (2015) retain asymptotically pivotal inference, regardless of the degree of persistence or endogeneity of the (putative) predictor, under considerably weaker assumptions on the innovations than are required by Kostakis et al. (2015) in their analysis. In particular, we allow for quite general forms of conditional and unconditional heteroskedasticity in the innovations, neither of which are tied to a parametric model. Second, and associatedly, we develop asymptotically valid bootstrap implementations of the IVX tests under these conditions. Monte Carlo simulations show that the bootstrap methods we propose can deliver considerably more accurate finite sample inference than the asymptotic implementation of these tests used in Kostakis et al. (2015) under certain problematic parameter constellations, most notably for their implementation against one-sided alternatives, and where multiple predictors are included. Third, under the same conditions as we consider for the fullsample tests, we show how sub-sample implementations of the IVX approach, coupled with a suitable bootstrap, can be used to develop asymptotically valid one-sided and two-sided tests for the presence of temporary windows of predictability. |
| Date: | 2021–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:29779&r=all |
| By: | Mario Forni; Luca Gambetti; marco Lippi; Luca Sala |
| Abstract: | Small scale VAR models are subject to two major issues: first, the information set might be too narrow; second, many macroeconomic variables are measured with error. The two features produce distorted estimates of the impulse response functions. We propose a new procedure, called Common Components Structural VARs (CC-SVAR), which solves both problems. It consists in (a) treating the variables, prior to estimation, in order to extract their common components; this eliminates measurement errors; (b) estimating a VAR with m > q common components, that is a singular VAR, where q is the number of shocks driving the economy; this solves the fundamentalness problem. SVARs and CC-SVARs are compared in the empirical analysis of monetary policy and technology shocks. The results obtained by SVARs are not robust, in that they strongly depend on the choice and the treatment of the variables considered. On the contrary, using CCSVARs (i) contractionary monetary shocks produce a decrease of prices independently of the variables included in the model, (ii) irrespective of whether hours worked enter the model in log-levels or growth rates, technology improvements produce an increase in hours worked. |
| Keywords: | Structural VAR models, structural factor models, nonfundamentalness, measurement errors |
| JEL: | C32 E32 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:mod:recent:147&r=all |
| By: | Martín Almuzara (Federal Reserve Bank of New York); Gabriele Fiorentini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Enrique Sentana (CEMFI and CEPR) |
| Abstract: | We analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measure-ments. We study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators of the dynamics parameters and reductions in the preci-sion of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. We also develop an R 2 measure of common trend observability that determines the severity of misspecification. Finally, we apply our framework to US quarterly data on GDP and GDI, obtaining an improved aggregate output measure. |
| Keywords: | Cointegration, GDP, GDI, Overdifferencing, Signal Extraction |
| JEL: | C32 E01 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2021_03&r=all |
| By: | Hidalgo, Javier; Lee, Jungyoon; Seo, Myung Hwan |
| Abstract: | This paper considers robust inference in threshold regression models when the practitioners do not know whether at the threshold point the true specification has a kink or a jump, nesting previous works that assume either continuity or discontinuity at the threshold. We find that the parameter values under the kink restriction are irregular points of the Hessian matrix, destroying the asymptotic normality and inducing the cube-root convergence rate for the threshold estimate. However, we are able to obtain the same asymptotic distribution as Hansen (2000) for the quasi-likelihood ratio statistic for the unknown threshold. We propose to construct confidence intervals for the threshold by bootstrap test inversion. Finite sample performances of the proposed procedures are examined through Monte Carlo simulations and an economic empirical application is given. |
| Keywords: | Change point; Cube root; Grid bootstrap; Kink |
| JEL: | C12 C13 C24 |
| Date: | 2019–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:100333&r=all |
| By: | Davide Debortoli; Mario Forni; Luca Gambetti; Luca Sala |
| Abstract: | Monetary policy easing and tightening have asymmetric effects: a policy easing has large effects on prices but small effects on real activity variables. The opposite is found for a policy tightening: large real effects but small effects on prices. Non-linearities are estimated using a new and simple procedure based on linear Structural Vector Autoregressions with exogenous variables (SVARX). We rationalize the results through the lenses of a simple model with downward nominal wage rigidities. |
| Keywords: | monetary policy shocks, non-linear effects, structural VAR models |
| JEL: | C32 E32 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:mod:recent:146&r=all |
| By: | Rui Fan; Ji Hyung Lee; Youngki Shin |
| Abstract: | In this paper we study the benefit of using the adaptive LASSO for predictive quantile regression. It is common that predictors in predictive quantile regression have various degrees of persistence and exhibit different signal strengths in explaining the dependent variable. We show that the adaptive LASSO has the consistent variable selection and the oracle properties under the simultaneous presence of stationary, unit root and cointegrated predictors. Some encouraging simulation and out-of-sample prediction results are reported. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.11568&r=all |
| By: | Peter Z. Schochet |
| Abstract: | This article develops new closed-form variance expressions for power analyses for commonly used panel model estimators. The main contribution is to incorporate variation in treatment timing into the analysis, but the variance formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates to improve precision. An illustrative power analysis provides guidance on appropriate sample sizes for various model specifications. An available Shiny R dashboard performs the sample size calculations. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.06770&r=all |
| By: | Caggiano, Giovanni; Castelnuovo, Efrem |
| Abstract: | We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves self-identifcation via a combination of narrative, sign, ratio, and correlation restrictions. We find that the world output loss that materialized during the great recession would have been 13% lower in absence of GFU shocks. We also unveil the existence of a global finance uncertainty multiplier: the more global financial conditions deteriorate after GFU shocks, the larger the world output contraction is. |
| JEL: | C32 E32 |
| Date: | 2021–02–11 |
| URL: | http://d.repec.org/n?u=RePEc:bof:bofrdp:2021_001&r=all |
| By: | Francis X. Diebold; Maximilian Gobel |
| Abstract: | We propose a simple statistical approach for fixed-target forecasting of Arctic sea ice extent, and we provide a case study of its real-time performance for target date September 2020. The real-time forecasting begins in early June and proceeds through late September. We visually detail the evolution of the statistically-optimal point, interval, and density forecasts as time passes, new information arrives, and the end of September approaches. Among other things, our visualizations may provide useful windows for assessing the agreement between dynamical climate models and observational data. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.10359&r=all |
| By: | Mario Forni; Luca Gambetti; Luca Sala |
| Abstract: | We estimate macroeconomic uncertainty and the effects of uncertainty shocks by means of a new procedure based on standard VARs. Under suitable assumptions, our procedure is equivalent to using the square of the VAR forecast error as an external instrument in a proxy SVAR. We add orthogonality constraints to the standard proxy SVAR identification scheme. We also derive a VAR-based measure of uncertainty. We apply our method to a US data set; we find that uncertainty is mainly exogenous and is responsible of a large fraction of business-cycle fluctuations. |
| Keywords: | Uncertainty shocks, OLS estimation, Stochastic volatility |
| JEL: | C32 E32 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:mod:recent:148&r=all |
| By: | Christoph Hanck; Till Massing |
| Abstract: | This article discusses cointegration tests for nonlinear cointegration in the presence of variance breaks in the errors. We build on approaches of Cavaliere and Taylor (2006, Journal of Time Series Analysis) for heteroskedastic cointegration tests and of Choi and Saikkonen (2010, Econometric Theory) for nonlinear cointegration tests. We propose a bootstrap test and prove its consistency. A Monte Carlo study shows the approach to have appealing finite sample properties and to work better than an approach using subresiduals. We provide an empirical application to the environmental Kuznets curves (EKC), finding that the cointegration tests do not reject the EKC hypothesis in most cases. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.08809&r=all |
| By: | Harvey, David I; Leybourne, Stephen J; Taylor, AM Robert |
| Abstract: | We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression t-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these statistics depend on the endogeneity and local-to-unity parameters characterising the predictor. Analysis of the asymptotic local power functions of feasible implementations of these two tests, based on asymptotically conservative critical values, motivates a switching procedure between the two, employing the quasi-GLS demeaned variant unless the magnitude of the estimated endogeneity correlation parameter is small. Additionally, if the data suggests the predictor is weakly persistent, our approach switches into the standard t-ratio test with reference to standard normal critical values. |
| Keywords: | predictive regression; persistence; endogeneity; quasi-GLS demeaning; unit root test; hybrid statistic |
| Date: | 2021–02–15 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:29814&r=all |
| By: | Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Alumni Hall 3145, Edwardsville IL, 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); He Li (School of International Economics and Politics, Liaoning University, Shenyang, Liaoning, China); Yu You (Li Anmin Advanced Institute of Finance and Economics, Liaoning University, Shenyang, Liaoning, China) |
| Abstract: | This paper establishes a predictive relationship between financial vulnerability and volatility in emerging stock markets. Focusing on China and India and utilizing GARCH-MIDAS models, we show that incorporating financial vulnerability can substantially improve the forecasting power of standard macroeconomic fundamentals (output growth, inflation and monetary policy interest rate) for stock market volatility. The findings have significant implications for investors to improve the accuracy of volatility forecasts. |
| Keywords: | Stock Market Volatility, Financial Vulnerability, GARCH-MIDAS, Emerging Markets |
| JEL: | C32 C53 G15 G17 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202112&r=all |
| By: | Raffaella Giacomini; Toru Kitagawa; Matthew Read |
| Abstract: | We consider structural vector autoregressions subject to 'narrative restrictions', which are inequality restrictions on functions of the structural shocks in specific periods. These restrictions raise novel problems related to identification and inference, and there is currently no frequentist procedure for conducting inference in these models. We propose a solution that is valid from both Bayesian and frequentist perspectives by: 1) formalizing the identification problem under narrative restrictions; 2) correcting a feature of the existing (single-prior) Bayesian approach that can distort inference; 3) proposing a robust (multiple-prior) Bayesian approach that is useful for assessing and eliminating the posterior sensitivity that arises in these models due to the likelihood having flat regions; and 4) showing that the robust Bayesian approach has asymptotic frequentist validity. We illustrate our methods by estimating the effects of US monetary policy under a variety of narrative restrictions. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.06456&r=all |