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on Econometric Time Series |
| By: | Lutz Kilian |
| Abstract: | This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the dominant driver of the real price of oil. The focus of this paper is not only on correcting some misunderstandings in the recent literature, but on the substantive and methodological insights generated by this exchange, which are of broader interest to applied researchers. |
| Keywords: | oil supply elasticity, oil demand elasticity, IV estimation, structural VAR, Bayesian inference, oil price, gasoline price |
| JEL: | Q43 Q41 C36 C52 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8567&r=all |
| By: | Bruno Bosco |
| Abstract: | The purpose of this paper is to offer an analysis of the price behavior of Phase III (2013–2020) EU- ETS emission allowances of CO2, by focusing on the dynamics of daily auction equilibrium prices and on the changes of the volatility of the underlying stochastic process. The paper initially investigates the characteristics of equilibrium prices as they result from auction rules and bidders' behavior and uses them as a theoretical basis of the statistical hypothesis–common to the empirical literature active in this field– of a changing conditional variance of prices. Then, different versions of a GARCH model are employed to estimate both mean and variance equations of price dynamics and to evaluate what factors affect price volatility, recorded excess supply, and bidders’ surplus. Brief policy considerations are also offered. |
| Keywords: | EU-ETS emission auctions; Equilibrium prices volatility; GARCH |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:mib:wpaper:448&r=all |
| By: | Amor Aniss Benmoussa; Reinhard Ellwanger; Stephen Snudden |
| Abstract: | We propose a new no-change benchmark to evaluate forecasts of series that are temporally aggregated. The new benchmark is the last high-frequency observation and reflects the null hypothesis that the underlying series, rather than the aggregated series, is unpredictable. Under the random walk null hypothesis, using the last high-frequency observation improves the mean squared prediction errors of the no-change forecast constructed from average monthly or quarterly data by up to 45 percent. We apply this insight to forecasts of the real price of crude oil and show that a new benchmark that relies on monthly closing prices dominates the conventional no-change forecast in terms of forecast accuracy. Although model-based forecasts also improve when models are estimated using closing prices, only the futures-based forecast significantly outperforms the new benchmark. Introducing a more suitable benchmark changes the assessments of different forecasting approaches and of the general predictability of real oil prices. |
| Keywords: | Econometric and statistical methods, International topics |
| JEL: | C53 Q47 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:20-39&r=all |
| By: | Shi Chen; Cathy Yi-Hsuan Chen; Wolfgang Karl H\"ardle |
| Abstract: | In order to price contingent claims one needs to first understand the dynamics of these indices. Here we provide a first econometric analysis of the CRIX family within a time-series framework. The key steps of our analysis include model selection, estimation and testing. Linear dependence is removed by an ARIMA model, the diagnostic checking resulted in an ARIMA(2,0,2) model for the available sample period from Aug 1st, 2014 to April 6th, 2016. The model residuals showed the well known phenomenon of volatility clustering. Therefore a further refinement lead us to an ARIMA(2,0,2)-t-GARCH(1,1) process. This specification conveniently takes care of fat-tail properties that are typical for financial markets. The multivariate GARCH models are implemented on the CRIX index family to explore the interaction. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.12129&r=all |
| By: | Barbara Sadaba; Sunčica Vujič; Sofia Maier |
| Abstract: | In this paper, we present new evidence from unobserved components time-series models on the cyclical behavior of the demand for education relative to economic cycles. We investigate the cyclical properties of schooling decisions, the time-varying exposure of these decisions to changes in the state of the macro economy, and the relative importance of shocks that drive economic fluctuations on the demand for schooling. To this end, we perform a trend-cycle decomposition of enrollment ratios for the United Kingdom over the period 1995Q1 to 2019Q4. We first establish the presence of a persistent cyclical process in the demand for schooling independent of a slow-moving trend. We then show that the direction of the effect of the economic cycle on schooling decisions (i.e., pro-cyclical, counter-cyclical, a-cyclical) is largely time-dependent, together with the degree of synchronization. Importantly, we find that changes in the demand for schooling are largely explained by economic cycles. We note, however, that the effects are different for different subsamples based on demographic characteristics. |
| Keywords: | Business fluctuations and cycles; Econometric and statistical methods |
| JEL: | C3 C32 E3 I2 J2 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:20-38&r=all |
| By: | Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa) |
| Abstract: | This paper examines if incorporating investors' uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors' uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. |
| Keywords: | Investors' uncertainty, Stock market risk, MSM, Volatility forecasting |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202090&r=all |
| By: | Berta, Paolo (University of Milan-Bicocca); Lovaglio, Pietro Giorgio (University of Milan-Bicocca); Paruolo, Paolo (European Commission); Verzillo, Stefano (European Commission) |
| Abstract: | Response management to the SARS-CoV-2 outbreak requires to answer several forecasting tasks. For hospital managers, a major one is to anticipate the likely needs of beds in intensive care in a given catchment area one or two weeks ahead, starting as early as possible in the evolution of the epidemic. This paper proposes to use a bivariate Error Correction model to forecast the needs of beds in intensive care, jointly with the number of patients hospitalised with Covid-19 symptoms. Error Correction models are found to provide reliable forecasts that are tailored to the local characteristics both of epidemic dynamics and of hospital practice for various regions in Europe in Italy, France and Scotland, both at the onset and at later stages of the spread of the disease. This reasonable forecast performance suggests that the present approach may be useful also beyond the set of analysed regions. |
| Keywords: | SARS-CoV-2, Covid-19, Intensive Care Units, Cointegration, Error correction models, Health forecasting, Multivariate time series, Vector Autoregression Models |
| JEL: | C53 C32 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:jrs:wpaper:202008&r=all |
| By: | Arturas Juodis; Yiannis Karavias; Vasilis Sarafidis |
| Abstract: | This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies on the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross-sections guarantees that the estimator has a root NT convergence rate. In order to account for the well-known "Nickell bias", the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks' profitability and cost efficiency. |
| Keywords: | panel data, Granger causality, VAR, "Nickell bias", bias correction, fixed effects |
| JEL: | C12 C13 C23 C33 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-32&r=all |
| By: | Heather D. Gibson (Bank of Greece); Stephen G. Hall (University of Leicester, Bank of Greece and University of Pretoria); George S. Tavlas (Bank of Greece) |
| Abstract: | We provide a new way of deriving a number of dynamic unobserved factors from a set of variables. We show how standard principal components may be expressed in state space form and estimated using the Kalman filter. To illustrate our procedure we perform two exercises. First, we use it to estimate a measure of the current-account imbalances among northern and southern euro-area countries that developed during the period leading up to the outbreak of the euro-area crisis, before looking at adjustment in the post-crisis period. Second, we show how these dynamic factors can improve forecasting of the euro-dollar exchange rate. |
| Keywords: | Principal Components;Factor Models; Underlying activity; Forecasts |
| JEL: | E3 G01 G14 G21 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:282&r=all |