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on Econometrics |
| By: | Patrick Gagliardini (University of Lugano and Swiss Finance Institute); Eric Ghysels (University of North Carolina Kenan-Flagler Business School, University of North Carolina (UNC)); Mirco Rubin (University of Bristol) |
| Abstract: | We examine the relationship between MIDAS regressions and the estimation of state space models applied to mixed frequency data. While in some cases the binding function is known, in general it is not, and therefore indirect inference is called for. The approach is appealing when we consider state space models which feature stochastic volatility, or other non-Gaussian and nonlinear settings where maximum likelihood methods require computationally demanding approximate filters. The stochastic volatility feature is particularly relevant when considering high frequency financial series. In addition, we propose a filtering scheme which relies on a combination of re-projection methods and now-casting MIDAS regressions with ARCH models. We assess the efficiency of our indirect inference estimator for the stochastic volatility model by comparing it with the Maximum Likelihood (ML) estimator in Monte Carlo simulation experiments. The ML estimate is computed with a simulation-based Expectation-Maximization (EM) algorithm, in which the smoothing distribution required in the E step is obtained via a particle forward-filtering/backward-smoothing algorithm. Our Monte Carlo simulations show that the Indirect Inference procedure is very appealing, as its statistical accuracy is close to that of MLE but the former procedure has clear advantages in terms of computational efficiency. An application to forecasting quarterly GDP growth in the Euro area with monthly macroeconomic indicators illustrates the usefulness of our procedure in empirical analysis. |
| Keywords: | Indirect inference, MIDAS regressions, State space model, Stochastic volatility, GDP forecasting. |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1646&r=ecm |
| By: | Manuel Arellano (CEMFI, Centro de Estudios Monetarios y Financieros); Stéphane Bonhomme (University of Chicago) |
| Abstract: | Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens the possibility to estimate nonlinear reduced forms in a large class of structural dynamic models with heterogeneous agents. We show how such reduced forms may be used to document policy-relevant derivative effects, and to improve the understanding and facilitate the implementation of structural models. |
| Keywords: | Dynamic models, structural economic models, panel data, unobserved heterogeneity. |
| JEL: | C23 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2017_1703&r=ecm |
| By: | Matthieu Garcin (Natixis Asset Management et LabEX ReFi); Clément Goulet (Centre d'Economie de la Sorbonne et LabEX ReFi) |
| Abstract: | In this paper, we propose an innovative methodology for modelling the news impact curve. The news impact curve provides a non-linear relation between past returns and current volatility and thus enables to forecast volatility. Our news impact curve is the solution of a dynamic optimization problem based on variational calculus. Consequently, it is a non-parametric and smooth curve. To our knowledge, this is the first time that such a method is used for volatility modelling. Applications on simulated heteroskedastic processes as well as on financial data show a better accuracy in estimation and forecast for this approach than for standard parametric (symmetric or asymmetric ARCH) or non-parametric (Kernel-ARCH) econometric techniques |
| Keywords: | Volatility modeling; news impact curve; calculus of variations; wavelet theory; ARCH |
| JEL: | C02 C14 C22 C51 C53 C58 C61 |
| Date: | 2015–09 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:15086r&r=ecm |
| By: | Chudik, Alexander (Federal Reserve Bank of Dallas); Pesaran, M. Hashem (USC Dornsife INET, University of Southern California and University of Cambridge); Yang, Jui-Chung (USC Dornsife INET, University of Southern California) |
| Abstract: | This paper considers estimation and inference in fixed effects (FE) panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when NN (the cross section dimension) is large relative to TT (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless NN/TT is sufficiently small. To deal with the bias and size distortion of FE estimator when NN is large relative to TT, the use of half-panel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order TT –2, and for valid inference it is only required that NN/TT3 --> 0, as NN, TT --> 00 jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when NN > TT, with the proposed estimator showing little size distortions. The use of half-panel jackknife FE-TE estimator is illustrated with two empirical applications from the literature. |
| JEL: | C12 C13 C23 |
| Date: | 2016–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:281&r=ecm |
| By: | Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | I adapt the Generalised Method of Moments to deal with nonlinear models in which a finite number of isolated parameter values satisfy the moment conditions. To do so, I initially study the closely related limiting class of first-order underidentified models, whose expected Jacobian is rank deficient but not necessarily 0. In both cases, the proposed procedures yield efficiency gains and underidentification tests within a standard asymptotic framework. I study models with and without separation of data and parameters. Finally, I illustrate the proposed inference procedures with a dynamic panel data model and a non-linear regression model for discrete data. |
| Keywords: | Finite set, Generalised Method of Moments, Identification test. |
| JEL: | C10 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2015_1508&r=ecm |
| By: | Kiranmoy Das (Indian Statistical Institute); Bhuvanesh Pareek (Indian Institute of Management); Sarah Brown (Department of Economics, University of Sheffield); Pulak Ghosh (Department of Decision Sciences and Information Systems, Indian Institute of Management) |
| Abstract: | We develop a dynamic zero-inflated model to analyse the number of hospital admissions within an aging population, which allows for the considerable number of zero hospital admissions at the individual level and occurrence dependence. In addition, certain health conditions may lead to groups of individuals having similar hospital admission rates. We analyse the US Health and Retirement Survey, which includes selfassessed health (SAH), which can be predictive of hospital admissions. Our modelling framework embeds a dynamic hierarchical matrix stick-breaking process to flexibly characterize this dynamic group structure allowing individuals to belong to different SAH groups at different points in time. |
| Keywords: | Bayesian models, Dirichlet process, Dynamic hurdle, Lasso, Matrix stickbreaking process, Zero-inflated data. |
| JEL: | C11 C14 I12 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:shf:wpaper:2017001&r=ecm |
| By: | Manuel Arellano (CEMFI, Centro de Estudios Monetarios y Financieros); Stéphane Bonhomme (University of Chicago) |
| Abstract: | Nonrandom sample selection is a pervasive issue in applied work. In additive models, a number of techniques are available for consistent selection correction. However, progress in the development of non-additive selection corrections has been slower. In this survey we review recent proposals dealing with sample selection in quantile models. |
| Keywords: | Quantile regression, sample selection, copula, wage regressions. |
| JEL: | C13 J31 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2017_1702&r=ecm |
| By: | Claude Godreche; Satya N. Majumdar; Gregory Schehr |
| Abstract: | We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a L\'evy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few years, it was indeed realized that random walks are a very useful "laboratory" to test the effects of correlations on the record statistics. We start with the simple one-dimensional random walk with symmetric jumps (both continuous and discrete) and discuss in detail the statistics of the number of records, as well as of the ages of the records, i.e., the lapses of time between two successive record breaking events. Then we review the results that were obtained for a wide variety of random walk models, including random walks with a linear drift, continuous time random walks, constrained random walks (like the random walk bridge) and the case of multiple independent random walkers. Finally, we discuss further observables related to records, like the record increments, as well as some questions raised by physical applications of record statistics, like the effects of measurement error and noise. |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1702.00586&r=ecm |
| By: | Cong Li; Qi Li; Jeffrey Racine; DAIQIANG ZHANG |
| Abstract: | We consider the problem of model averaging over a set of semiparametric varying coefficient models where the varying coefficients can be functions of continuous and categorical variables. We propose a Mallows model averaging procedure that is capable of delivering model averaging estimators with solid finite-sample performance. Theoretical underpinnings are provided, finite-sample performance is assessed via Monte Carlo simulation, and an illustrative application is presented. The approach is very simple to implement in practice and R code is provided in an appendix. |
| JEL: | C14 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:mcm:deptwp:2017-01&r=ecm |
| By: | Stephen J. Redding; Esteban Rossi-Hansberg |
| Abstract: | The observed uneven distribution of economic activity across space is influenced by variation in exogenous geographical characteristics and endogenous interactions between agents in goods and factor markets. Until recently, the theoretical literature on economic geography had focused on stylized settings that could not easily be taken to the data. This paper reviews more recent research that has developed quantitative models of economic geography. These models are rich enough to speak to first-order features of the data, such as many heterogenous locations and gravity equation relationships for trade and commuting. Yet at the same time these models are sufficiently tractable to undertake realistic counterfactuals exercises to study the effect of changes in amenities, productivity, and public policy interventions such as transport infrastructure investments. We provide an extensive taxonomy of the different building blocks of these quantitative spatial models and discuss their main properties and quantification. |
| Keywords: | agglomeration; cities; economic geography; quantitative models; spatial economics |
| JEL: | F10 F14 R12 R23 R41 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:69020&r=ecm |