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on Econometric Time Series |
| By: | Ines Wilms; Luca Barbaglia; Christophe Croux |
| Abstract: | Retailers use the Vector AutoRegressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available for not just one store, but a whole chain of stores. We propose to study cross-category effects using a multi-class VAR model: we jointly estimate cross-category effects for several distinct but related VAR models, one for each store. Our methodology encourages effects to be similar across stores, while still allowing for small differences between stores to account for store heterogeneity. Moreover, our estimator is sparse: unimportant effects are estimated as exactly zero, which facilitates the interpretation of the results. A simulation study shows that the proposed multi-class estimator improves estimation accuracy by borrowing strength across classes. Finally, we provide three visual tools showing (i) the clustering of stores on identical cross-category effects, (ii) the networks of product categories and (iii) the similarity matrices of shared cross-category effects across stores. |
| Keywords: | Fused Lasso, Multi-class estimation, Multi-store sales application, Sparse estimation, Vector AutoRegressive model |
| Date: | 2016–05 |
| URL: | http://d.repec.org/n?u=RePEc:ete:kbiper:540947&r=ets |
| By: | GUERRON-QUINTANA, Pablo; INOUE, Atsushi; KILIAN, Lutz |
| Abstract: | One of the leading methods of estimating the structural parameters of DSGE models is the VAR-based impulse response matching estimator. The existing asymptotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model parameters. Situations in which this order condition is violated arise routinely in applied work. We establish the consistency of the impulse response matching estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. Our analysis sheds new light on the choice of the weighting matrix and covers both weakly and strongly identified DSGE model parameters. We also show that under our assumptions special care is needed to ensure the asymptotic validity of Bayesian methods of inference. A simulation study suggests that the interval estimators we propose are reasonably accurate in practice. We also show that using these methods may affect the substantive conclusions in empirical work. |
| Keywords: | Structural estimation, DSGE, VAR, impulse response, nonstandard asymptotics, bootstrap, weak identification, robust inference. |
| JEL: | C32 C52 E30 E50 |
| Date: | 2016–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-27&r=ets |
| By: | Badi Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Long Liu (College of Business, University of Texas at San Antonio, UTSA Circle, Texa 78249) |
| Abstract: | This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as special cases. |
| Keywords: | Prediction; Panel Data; Fixed Effects; Random Effects; Serial Correlation; Spatial Error Correlation |
| JEL: | C33 |
| Date: | 2016–02 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:188&r=ets |
| By: | Heni Boubaker (IPAG LAB, IPAG Business School, France); Giorgio Canarella (University of Nevada, Las Vegas, USA); Rangan Gupta (Department of Economics, University of Pretoria); Stephen M. Miller (University of Nevada, Las Vegas, USA) |
| Abstract: | We propose a new long-memory model with a time-varying fractional integration parameter, evolving non-linearly according to a Logistic Smooth Transition Autoregressive (LSTAR) specification. To estimate the time-varying fractional integration parameter, we implement a method based on the wavelet approach, using the instantaneous least squares estimator (ILSE). The empirical results show the relevance of the modeling approach and provide evidence of regime change in inflation persistence that contributes to a better understanding of the inflationary process in the US. Most importantly, these empirical findings remind us that a "one-size-fits-all" monetary policy is unlikely to work in all circumstances. |
| Keywords: | Time-varying long-memory, LSTAR model, MODWT algorithm, ILSE estimator |
| JEL: | C13 C22 C32 C54 E31 |
| Date: | 2016–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201647&r=ets |
| By: | Luke Hartigan (School of Economics, UNSW Business School, UNSW) |
| Abstract: | HAC estimators are known to produce test statistics that reject too frequently in finite samples. One neglected reason comes from using the OLS residuals when constructing the HAC estimator. If the regression matrix contains high leverage points, such as from outliers, then the OLS residuals will be negatively biased. This reduces the variance of the OLS residuals and the HAC estimator takes this to signal a more accurate coefficient estimate. Transformations to reflate the OLS residuals and offset the bias have been used in the related HC literature for many years, but these have been overlooked in the HAC literature. Using a suite of simulations I provide strong evidence in favour of replacing the OLS residual-based HAC estimator with estimators related to extensions of either of the two main HC alternatives. In an empirical application I show how different inference from using the alternative HAC estimators can be important, not only from a statistical perspective, but also from an economic one as well. |
| Keywords: | Covariance matrix estimation, Finite sample analysis, Leverage points, Autocorrelation, Hypothesis testing, Monte Carlo simulation, Inference |
| JEL: | C12 C13 C15 C22 |
| Date: | 2016–05 |
| URL: | http://d.repec.org/n?u=RePEc:swe:wpaper:2016-06&r=ets |
| By: | Shelton Peiris (School of Mathematics and Statistics University of Sydney, Australia.); Manabu Asai (Faculty of Economics Soka University, Japan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain.) |
| Abstract: | In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model. |
| Keywords: | Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting. |
| JEL: | C18 C21 C58 |
| Date: | 2016–06 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1608&r=ets |