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on Econometric Time Series |
| 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=ets |
| 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=ets |
| By: | Xolani Sibande (Department of Economics, University of Pretoria, Pretoria, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska, USA and School of Business and Economics, Loughborough University, Leicestershire, UK) |
| Abstract: | The influence of financial markets on the real economy, including that of stock market returns on unemployment, is a key focus in the literature. Using DCC-MGARCH tests, we analyse time-varying causality between stock market returns and unemployment in the UK using monthly data from 1855 to 2017. The tests reveal that there is significant evidence of information spillover between the stock market and the labour market. This information spillover was found to be significant in the direction of stock market returns to unemployment, insignificant in the opposite direction, and significant bi-directionally. The results were also found to be congruent to the macroeconomic history of the UK. |
| Keywords: | Time-varying Granger causality, stock market returns, unemployment |
| JEL: | C12 C32 J01 G14 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201863&r=ets |
| By: | Kapetanios, George; Millard, Stephen; Price, Simon; Petrova, Katerina |
| 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. |
| Date: | 2018–10–18 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:23320&r=ets |
| 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=ets |
| By: | Belke, Ansgar; Beckmann, Joscha; Dubova, Irina |
| Abstract: | Inflation expectations play a crucial role for monetary policy transmission, having become even more important since the emergence of unconventional monetary policy. Based on survey data provided by Consensus Economics, we assess determinants of professional inflation expectations for the G7 economies. We emphasize the role of international spillovers in inflation expectations stemming from monetary policy decisions in the US. We also consider several possible determinants, such as changes in the path of monetary policy, oil price shocks and uncertainty measures. Based on a Bayesian VAR, we find significant evidence for international spillovers stemming from expectations about US monetary policy based on impulse-response functions and forecast error decompositions. We also provide similar evidence on spillovers from the dispersion across inflation forecasts. |
| Keywords: | Bayesian VAR,expectations,inflation,survey data,updating |
| JEL: | C22 E31 E52 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc18:181518&r=ets |