|
on Econometrics |
| By: | Jiti Gao; Bin Peng; Yayi Yan |
| Abstract: | Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse response analyses subject to structural VAR identification conditions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application on US government spending multipliers. |
| Keywords: | time-varying impulse response, parameter stability, instrumental variable approach |
| JEL: | C14 C32 E52 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2022-4&r= |
| By: | Tassos Magdalinos; Katerina Petrova |
| Keywords: | uniform inference, central limit theory, autoregression, predictive regression, instrumentation, mixed-Gaussianity, t-statistic, confidence intervals |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1837&r= |
| By: | Feng, Oliver Y.; Chen, Yining; Han, Qiyang; Carroll, Raymond J; Samworth, Richard J. |
| Abstract: | We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex optimisation problem, since the inflection point is unknown. We show that the estimator may nevertheless be regarded as a projection onto a finite union of convex cones, which allows us to propose a mixed primal-dual bases algorithm for its efficient, sequential computation. After developing a projection framework that demonstrates the consistency and robustness to misspecification of the estimator, our main theoretical results provide sharp oracle inequalities that yield worst-case and adaptive risk bounds for the estimation of the regression function, as well as a rate of convergence for the estimation of the inflection point. These results reveal not only that the estimator achieves the minimax optimal rate of convergence for both the estimation of the regression function and its inflection point (up to a logarithmic factor in the latter case), but also that it is able to achieve an almost-parametric rate when the true regression function is piecewise affine with not too many affine pieces. Simulations and a real data application to air pollution modelling also confirm the desirable finite-sample properties of the estimator, and our algorithm is implemented in the R package Sshaped. |
| Keywords: | EP/P031447/1; EP/N031938/1; DMS-1916221; CCF-1934904; U01-CA057030 |
| JEL: | C1 |
| Date: | 2022–04–21 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:111889&r= |
| By: | Meijiang Wang; Jingyu He; P. Richard Hahn |
| Abstract: | Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically provide inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulations studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.10963&r= |
| By: | Huiling Yuan; Guodong Li; Junhui Wang |
| Abstract: | This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12933&r= |
| By: | Isaiah Andrews; Anna Mikusheva |
| Abstract: | We consider estimation in moment condition models and show that under squared error loss and bounds on identification strength, asymptotically admissible (i.e. undominated) estimators must be Lipschitz functions of the sample moments. GMM estimators are in general discontinuous in the sample moment function, and are thus inadmissible under weak identification. We show, by contrast, that quasi-Bayes posterior means and bagged, or bootstrap aggregated, GMM estimators have superior continuity properties, while results in the literaure imply that they are equivalent to GMM when identification is strong. In simulations calibrated to published instrumental variables specifications, we find that these alternatives often outperform GMM. Hence, quasi-Bayes and bagged GMM present attractive alternatives to GMM. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12462&r= |
| By: | Yao Luo; Peijun Sang |
| Abstract: | Estimating structural models is an essential tool for economists. However, existing methods are often inefficient either computationally or statistically, depending on how equilibrium conditions are imposed. We propose a class of penalized sieve estimators that are consistent, asymptotic normal, and asymptotically efficient. Instead of solving the model repeatedly, we approximate the solution with a linear combination of basis functions and impose equilibrium conditions as a penalty in searching for the best fitting coefficients. We apply our method to an entry game between Walmart and Kmart. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.13488&r= |
| By: | Liangjun Su; Thomas Tao Yang; Yonghui Zhang; Qiankun Zhou |
| Abstract: | This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. One-stage and multiple-stage procedures are both considered. The former works well in terms of the true positive rate only if the marginal effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak marginal effects. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we use the OCMT procedure on a dataset we extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of the out-of-sample forecast root mean square errors, compared with competing methods. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12023&r= |
| By: | Charles F. Manski |
| Abstract: | Econometricians have usefully separated study of estimation into identification and statistical components. Identification analysis aims to place an informative upper bound on what may be learned about population parameters of interest with specified sample data. Statistical decision theory has studied decision making with sample data without reference to identification. This paper asks if and how identification analysis is useful to statistical decision theory. I show that the answer is positive and simple when the relevant parameter (true state of nature) is point identified. However, a subtlety arises when the true state is partially identified, and a decision must be made under ambiguity. Then the performance of some criteria, particularly minimax regret, is enhanced by permitting randomized choice of an action, which essentially requires availability of sample data. I show that an informative upper bound on the performance of decision making holds when the knowledge assumed in identification analysis is combined with sample data enabling randomized choice. I emphasize that using sample data to randomize choice is conceptually distinct from its traditional econometric use to infer population parameters. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.11318&r= |
| By: | Haroon Mumtaz; Michele Piffer |
| Abstract: | This paper introduces a flexible local projection that generalizes the model by Jord\'a (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first application shows that the fiscal multiplier is stronger in recession than in expansion only in response to contractionary fiscal shocks, but not in response to expansionary fiscal shocks. We then show that financial shocks generate effects on the economy that increase more than proportionately in the size of the shock when the shock is negative, but not when the shock is positive. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.13150&r= |
| By: | Tien Mai; The Viet Bui; Quoc Phong Nguyen; Tho V. Le |
| Abstract: | This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue would be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation-maximization (EM) method that allows to deal with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method would be expensive, we propose a new estimation method based on the idea that the choice probabilities of unconnected link observations can be exactly computed by solving systems of linear equations. We further design a new algorithm, called as decomposition-composition (DC), that helps reduce the number of systems of linear equations to be solved and speed up the estimation. We compare our proposed algorithms with some standard baselines using a dataset from a real network and show that the DC algorithm outperforms the other approaches in recovering missing information in the observations. Our methods work with most of the recursive route choice models proposed in the literature, including the recursive logit, nested recursive logit, or discounted recursive models. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12992&r= |
| By: | Philipp Otto |
| Abstract: | This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects in the conditional variance, as they are usually present in spatial econometric applications. Furthermore, spatial and temporal cross-variable effects are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a real-world example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spill-overs. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12472&r= |
| By: | Timothy Christensen; Hyungsik Roger Moon; Frank Schorfheide |
| Abstract: | We derive optimal statistical decision rules for discrete choice problems when the decision maker is unable to discriminate among a set of payoff distributions. In this problem, the decision maker must confront both model uncertainty (about the identity of the true payoff distribution) and statistical uncertainty (the set of payoff distributions must be estimated). We derive "efficient-robust decision rules" which minimize maximum risk or regret over the set of payoff distributions and which use the data to learn efficiently about features of the set of payoff distributions germane to the choice problem. We discuss implementation of these decision rules via the bootstrap and Bayesian methods, for both parametric and semiparametric models. Using a limits of experiments framework, we show that efficient-robust decision rules are optimal and can dominate seemingly natural alternatives. We present applications to treatment assignment using observational data and optimal pricing in environments with rich unobserved heterogeneity. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.11748&r= |
| By: | Edmund S. Crawley; Martin Holm; Hakon Tretvoll |
| Abstract: | The standard model of permanent and transitory income is known to be misspecified. Estimates of income volatility in the model differ depending on the type of data moments used—levels or differences—and how these moments are weighted in the estimation. We propose two changes to the standard model. First, we account for the time-aggregated nature of observed income data. Second, we allow transitory shocks to persist for varying lengths of time. With only one additional parameter, our proposed model consistently recover the parameters of the income process irrespective of the estimation method. To the extent that researchers employ the standard model, we advise special caution with the use of first-difference moments. |
| Keywords: | Income uncertainty; Inequality; Household finance |
| JEL: | E21 E24 J30 |
| Date: | 2022–05–12 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2022-26&r= |
| By: | Xianfei Hui; Baiqing Sun; Yan Zhou |
| Abstract: | Predicting the dynamic volatility in financial market provides a promising method for risk prediction, asset pricing and market supervision. Barndorff-Nielsen and Shephard model (BN-S) model, used to capture the stochastic behavior of high-frequency time series, is an accepted stochastic volatility model with L\' evy process. Although this model is attractive and successful in theory, it needs to be improved in application. We build a new generalized BN-S model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Calculation results show that new model outperforms the classic model in volatility forecasting. Experiments on Dow Jones Industrial Average futures price data are conducted to verify feasibility and practicability of our proposed approach. Numerical examples are provided to illustrate the theoretical result. Three machine learning algorithms are applied to estimate new model parameter. Compared with the classical model, our method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility more flexible and has ideal performance. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12657&r= |
| By: | Nguyen, Hoang (Örebro University School of Business); Virbickaite, Audrone (CUNEF Universidad) |
| Abstract: | Stock and oil relationship is usually time-varying and depends on the current economic conditions. In this study, we propose a new Dynamic Stochastic Mixed data frequency sampling (DSM) copula model, that decomposes the stock-oil relationship into a short-run dynamic stochastic component and a long-run component, governed by related macro- nance variables. We nd that in ation/interest rate, uncertainty and liquidity factors are the main drivers of the long-run co-dependence. We show that investment portfolios, based on the proposed DSM copula model, are more accurate and produce better economic outcomes as compared to other alternatives. |
| Keywords: | Stock-Oil; Copula; MIDAS; SMC; Portfolio allocation; Hedging |
| JEL: | C32 C52 C58 G11 G12 |
| Date: | 2022–05–19 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2022_005&r= |
| By: | Linwei Li; Paul-Amaury Matt; Christian Heumann |
| Abstract: | The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a parameter-free regression network termed RegPred Net. The exchange rate to forecast is treated as a stochastic process. It is assumed to follow a generalization of Brownian motion and the mean-reverting process referred to as the generalized Ornstein-Uhlenbeck (OU) process, with time-dependent coefficients. Using past observed values of the input time series, these coefficients can be regressed online by the cells of the first half of the network (Reg). The regressed coefficients depend only on - but are very sensitive to - a small number of hyperparameters required to be set by a global optimization procedure for which, Bayesian optimization is an adequate heuristic. Thanks to its multi-layered architecture, the second half of the regression network (Pred) can project time-dependent values for the OU process coefficients and generate realistic trajectories of the time series. Predictions can be easily derived in the form of expected values estimated by averaging values obtained by Monte Carlo simulation. The forecasting accuracy on a 100 days horizon is evaluated for several of the most important FX rates such as EUR/USD, EUR/CNY, and EUR/GBP. Our experimental results show that the RegPred Net significantly outperforms ARMA, ARIMA, LSTMs, and Autoencoder-LSTM models in this task. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12914&r= |
| By: | Arthur Charpentier (UQAM - Université du Québec à Montréal = University of Québec in Montréal); Emmanuel Flachaire (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université) |
| Abstract: | Top incomes are often related to Pareto distribution. To date, economists have mostly used Pareto Type I distribution to model the upper tail of income and wealth distribution. It is a parametric distribution, with interesting properties, that can be easily linked to economic theory. In this paper, we first show that modeling top incomes with Pareto Type I distribution can lead to biased estimation of inequality, even with millions of observations. Then, we show that the Generalized Pareto distribution and, even more, the Extended Pareto distribution, are much less sensitive to the choice of the threshold. Thus, they can provide more reliable results. We discuss different types of bias that could be encountered in empirical studies and, we provide some guidance for practice. To illustrate, two applications are investigated, on the distribution of income in South Africa in 2012 and on the distribution of wealth in the United States in 2013. |
| Keywords: | Pareto distribution,Top incomes,Inequality measures |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03649428&r= |
| By: | Mikkel Bennedsen (Aarhus University and CREATES); Eric Hillebrand (Aarhus University and CREATES); Sebastian Jensen (Aarhus University and CREATES) |
| Abstract: | We investigate the relationship between per capita gross domestic product and per capita carbon dioxide emissions using national-level panel data for the period 1960-2018. We propose a novel semiparametric panel data methodology that combines country and time fixed effects with a nonparametric neural network regression component. Globally and for the regions OECD and Asia, we find evidence of an inverse U-shaped relationship, often referred to as an environmental Kuznets curve (EKC). For OECD, the EKC-shape disappears when using consumption-based emissions data, suggesting the EKC-shape observed for OECD is driven by emissions exports. For Asia, the EKC-shape becomes even more pronounced when using consumption-based emissions data and exhibits an earlier turning point. JEL classifcation: C14, C23, C45, C51, C52, C53 Key words: Territorial carbon dioxide emissions, Consumption-based carbon dioxide emissions, Environmental Kuznets curve, Climate econometrics, Panel data, Machine learning, Neural networks |
| Date: | 2022–05–24 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2022-09&r= |