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on Econometric Time Series |
| By: | Martin Bruns; Michele Piffer |
| Abstract: | Fairtrade certification aims at transferring wealth from the consumer to the farmer; however, coffee passes through many hands before reaching final consumers. Bringing together retail, wholesale, and stock market data, this study estimates how much more consumers are paying for Structural VAR models are frequently identified using sign restrictions on contemporaneous impulse responses. We develop a methodology that can handle a set of prior distributions that is much larger than the one currently allowed for by traditional methods. We then develop an importance sampler that explores the posterior distribution just as conveniently as with traditional approaches. This makes the existing trade-off between careful prior selection and tractable posterior sampling disappear. We use this framework to combine sign restrictions with information on the volatility of the variables in the model, and show that this sharpens posterior inference. Applying the methodology to the oil market, we find that supply shocks have a strong role in driving the dynamics of the price of oil and in explaining the drop in oil production during the Gulf war. |
| Keywords: | Sign restrictions, Bayesian inference, oil market |
| JEL: | C32 C11 E50 H62 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1796&r=all |
| By: | Haroon Mumtaz (Queen Mary University of London); Katerina Petrova (University of St. Andrews) |
| Abstract: | In this paper we extend the Bayesian Proxy VAR to incorporate time variation in the parameters. A Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in the US and show that there is limited evidence for a structural change in the tax multiplier. |
| Keywords: | Time-Varying parameters, Stochastic volatility, Proxy VAR, tax shocks |
| JEL: | C2 C11 E3 |
| Date: | 2018–11–07 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:875&r=all |
| By: | Richard T. Baillie (Michigan State University, USA, Kings College, University of London, UK & Rimini Center for Economic Analysis, Italy); Fabio Calonaci (Queen Mary University of London); Dooyeon Cho (Sungkyunkwan University, Republic of Korea); Seunghwa Rho (Emory University, USA) |
| Abstract: | The presence of long memory in Realized Volatility (RV) is a widespread stylized fact. The origins of long memory in RV have been attributed to jumps, structural breaks, non-linearities, or pure long memory. An important development has been the Heterogeneous Autoregressive (HAR) model and its extensions. This paper assesses the separate roles of fractionally integrated long memory models, extended HAR models and time varying parameter HAR models. We find that the presence of the long memory parameter is often important in addition to the HAR models. |
| Keywords: | Long memory, Restricted ARFIMA, Realized volatility, HAR model, Time varying parameters |
| JEL: | C22 C31 |
| Date: | 2019–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:881&r=all |
| By: | Francis, Neville (University of North Carolina, Chapel Hill); Owyang, Michael T. (Federal Reserve Bank of St. Louis); Soques, Daniel (University of North Carolina, Chapel Hill) |
| Abstract: | We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find four groups, or “clusters”, of countries which experience idiosyncratic recessions relative to the global cycle. Additionally, we find the primary indicators of international recessions to be fluctuations in equity markets and geopolitical uncertainty. In out-of-sample forecasting exercises, we find that our model is an improvement over standard benchmark models for forecasting both aggregate output growth and country-level recessions. |
| Keywords: | Markov-switching; time-varying transition probabilities; cluster analysis |
| JEL: | C11 C32 E32 F44 |
| Date: | 2019–01–22 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2019-010&r=all |
| By: | Shayan Zakipour-Saber (Queen Mary University of London) |
| Abstract: | Are monetary policy regimes state-dependent? To answer the question this paper estimates New Keynesian general equilibrium models that allow the state of the economy to influence the monetary authority's stance on inflation. I take advantage of recent developments in solving rational expectations models with state-dependent parameter drift to estimate three models on U.S. data between 1965-2009. In these models, the probability of remaining in a monetary policy regime that is relatively accommodative towards inflation, varies over time and depends on endogenous model variables; in particular, either deviations of inflation or output from their respective targets or a monetary policy shock. The main contribution of this paper is that it finds evidence of state-dependent monetary policy regimes. The model that allows inflation to influence the monetary policy regime in place, fits the data better than an alternative model with regime changes that are not state-dependent. This finding points towards reconsidering how changes in monetary policy are modelled. |
| Keywords: | Markov-Switching DSGE, State-dependence, Bayesian Estimation |
| JEL: | C13 C32 E42 E43 |
| Date: | 2019–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:882&r=all |
| By: | Stéphane Lhuissier; Fabien Tripier |
| Abstract: | Using a Markov-switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model, in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural parameters, the distance between the regime-switching VAR-based impulse response functions and those implied by the model. Our results point to changes in the degree of financial frictions. We discuss the implications of this structural interpretation and show that the expectation effect of regime switching in financial conditions is an important component of the financial accelerator mechanism. If agents hold pessimistic expectations about future financial conditions, then shocks are amplified and transmitted more rapidly to the economy. |
| Keywords: | Uncertainty shocks, Regime switching, Financial frictions, Expectation effects. |
| JEL: | C32 E32 E44 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:714&r=all |
| By: | Valentin Jouvanceau (Univ Lyon, Université Lyon 2, GATE UMR 5824, F-69130 Ecully, France) |
| Abstract: | Have the macroeconomic effects of QE programs been overestimated empirically? Using a large set of model specifications that differ in the degree of time-variation in parameters, the answer is yes. Our forecasting exercise suggests that it is crucial to allow for time-variation in parameters, but not for stochastic volatility to improve the fit with data. Having a more reliable specification, we find that the portfolio balance and signaling channels had sizable contributions to the transmission of QE programs. Finally, our identified structural shocks show that QE1 had larger macroeconomic effects than QE2 and QE3, but much smaller than usually found in the literature. |
| Keywords: | Quantitative Easing, Model specification, TVP-FAVAR, Transmission channels |
| JEL: | C11 C32 C52 E52 E58 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:1912&r=all |
| By: | Jiayuan Zhou; Feiyu Jiang; Ke Zhu; Wai Keung Li |
| Abstract: | We propose a new Conditional BEKK matrix-F (CBF) model for the time-varying realized covariance (RCOV) matrices. This CBF model is capable of capturing heavy-tailed RCOV, which is an important stylized fact but could not be handled adequately by the Wishart-based models. To further mimic the long memory feature of the RCOV, a special CBF model with the conditional heterogeneous autoregressive (HAR) structure is introduced. Moreover, we give a systematical study on the probabilistic properties and statistical inferences of the CBF model, including exploring its stationarity, establishing the asymptotics of its maximum likelihood estimator, and giving some new inner-product-based tests for its model checking. In order to handle a large dimensional RCOV matrix, we construct two reduced CBF models --- the variance-target CBF model (for moderate but fixed dimensional RCOV matrix) and the factor CBF model (for high dimensional RCOV matrix). For both reduced models, the asymptotic theory of the estimated parameters is derived. The importance of our entire methodology is illustrated by simulation results and two real examples. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.12077&r=all |
| By: | McAleer, M.J. |
| Abstract: | In order to hedge efficiently, persistently high negative covariances or, equivalently, correlations, between risky assets and the hedging instruments are intended to mitigate against financial risk and subsequent losses. If there is more than one hedging instrument, multivariate covariances and correlations will have to be calculated. As optimal hedge ratios are unlikely to remain constant using high frequency data, it is essential to specify dynamic time-varying models of covariances and correlations. These values can either be determined analytically or numerically on the basis of highly advanced computer simulations. Analytical developments are occasionally promulgated for multivariate conditional volatility models. The primary purpose of the paper is to analyse purported analytical developments for the only multivariate dynamic conditional correlation model to have been developed to date, namely Engle’s (2002) widely-used Dynamic Conditional Correlation (DCC) model. Dynamic models are not straightforward (or even possible) to translate in terms of the algebraic existence, underlying stochastic processes, specification, mathematical regularity conditions, and asymptotic properties of consistency and asymptotic normality, or the lack thereof. The paper presents a critical analysis, discussion, evaluation and presentation of caveats relating to the DCC model, and an emphasis on the numerous dos and don’ts in implementing the DCC and related model in practice |
| Keywords: | Hedging, covariances, correlations, existence, mathematical regularity, invertibility, likelihood function, statistical asymptotic properties, caveats, practical implementation |
| JEL: | C22 C32 C51 C52 C58 C62 G32 |
| Date: | 2019–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:115611&r=all |
| By: | McAleer, M.J. |
| Abstract: | Persistently high negative covariances between risky assets and hedging instruments are intended to mitigate against risk and subsequent financial losses. In the event of having more than one hedging instrument, multivariate covariances need to be calculated. Optimal hedge ratios are unlikely to remain constant using high frequency data, so it is essential to specify dynamic covariance models. These values can either be determined analytically or numerically on the basis of highly advanced computer simulations. Analytical developments are occasionally promulgated for multivariate conditional volatility models. The primary purpose of the paper is to analyse purported analytical developments for the most widely-used multivariate dynamic conditional covariance model to have been developed to date, namely the Full BEKK model of Baba et al. (1985), which was published as Engle and Kroner (1995). Dynamic models are not straightforward (or even possible) to translate in terms of the algebraic existence, underlying stochastic processes, specification, mathematical regularity conditions, and asymptotic properties of consistency and asymptotic normality, or the lack thereof. The paper presents a critical analysis, discussion, evaluation and presentation of caveats relating to the Full BEKK model, and an emphasis on the numerous dos and don’ts in implementing Full BEKK in practice |
| Keywords: | Hedging, covariances, existence, mathematical regularity, inevitability, likelihood, function, statistical asymptotic properties, caveats, practical implementation |
| JEL: | C22 C32 C51 C58 C62 G32 |
| Date: | 2019–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:115612&r=all |
| By: | Cheng, T.; Gao, J.; Linton, O. |
| Abstract: | We propose two new nonparametric predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series. We define estimation methods and establish the large sample properties of these methods in the short horizon and the long horizon case. We apply our methods to stock return prediction using a number of standard predictors such as dividend yield. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we _nd that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting. We also compare our methods with the linear regression and historical mean methods according to an economic metric. In particular, we show how our methods can be used to deliver a trading strategy that beats the buy and hold strategy (and linear regression based alternatives) over our sample period. |
| Keywords: | Kernel estimator, locally stationary process, series estimator, stock return prediction |
| JEL: | C14 C22 G17 |
| Date: | 2019–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1932&r=all |
| By: | Richard T. Baillie (Michigan State University, USA, King’s College London & Rimini Center for Economic Analysis, Italy); Fabio Calonaci (Queen Mary University of London); George Kapetanios (King’s College London) |
| Abstract: | This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential approach of Fama and MacBeth (1973). However, the hierarchical method uses very flexible bandwidth selection methods in kernel weighted regressions which can emphasize local, or recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point of time. The choice of bandwidths and weighting schemes, are achieved by cross validation. This leads to consistent estimators of the risk premia and factor loadings. Also, out of sample forecasting for stocks and two large portfolios indicates that the hierarchical method leads to statistically significant improvement in forecast RMSE. |
| Keywords: | Asset pricing model, FamaMacBeth model, estimation of beta, kernel weighted regressions, cross validation, time-varying parameter regressions |
| JEL: | C22 F31 G01 G15 |
| Date: | 2019–01–07 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:879&r=all |
| By: | Lopes Moreira Da Veiga, María Helena; Marín Díazaraque, Juan Miguel; Zea Bermudez, Patrícia de |
| Abstract: | The paper proposes the use of data cloning (DC) to the estimation of general asymmetric stochastic volatility (ASV) models with flexible distributions for the standardized returns. These models are able to capture the asymmetric volatility, the leptokurtosis and the skewness of the distribution of returns. Data cloning is a general technique to compute maximum likelihood estimators, along with their asymptotic variances, by means of a Markov chain Monte Carlo (MCMC) methodology. The main aim of this paper is to illustrate how easily general ASV models can be estimated and consequently studied via data cloning. Changes of specifications, priors and sampling error distributions are done with minor modifications of the code. Using an intensive simulation study, the finite sample properties of the estimators of the parameters are evaluated and compared to those of a benchmark estimator that is also user-friendly.The results show that the proposed estimator is computationally efficient and robust, and can be an effective alternative to the exiting estimation methods applied to ASV models. Finally, we use data cloning to estimate the parameters of general ASV models and forecast the one-step-ahead volatility of S&P 500 and FTSE-100 daily returns. |
| Keywords: | Skewed and Heavy-Tailed distributions; Non-Gaussian Nonlinear Time Series Models; Data Cloning; Asymmetric Volatility |
| Date: | 2019–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:28214&r=all |