ERN: Other Econometrics: Single Equation Models (Topic)最新文献

{"title":"Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: A General Dynamic Factor Model Approach","authors":"M. Hallin, Carlos Trucíos","doi":"10.2139/ssrn.3748736","DOIUrl":"https://doi.org/10.2139/ssrn.3748736","url":null,"abstract":"Beyond their importance from the regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Unfortunately, due to the curse of dimensionality, their accurate estimation and forecast in large portfolios is quite a challenge. To tackle this problem, two procedures are proposed, being one of them based on a filtered historical simulation method in which high-dimensional conditional covariance matrices are estimated via a general dynamic factor model with infinite-dimensional factor space and conditionally heteroscedastic factors, and the other one based on a residual-based bootstrap scheme. The procedures are applied to a panel with concentration ratio close to one. Backtesting and scoring results indicate that both VaR and ES are accurately estimated under our methods, which outperforms alternative approaches available in the literature.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124800909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long‐Term Effects of Fiscal Stimulus and Austerity in Europe","authors":"S. Gechert, G. Horn, Christoph Paetz","doi":"10.1111/obes.12287","DOIUrl":"https://doi.org/10.1111/obes.12287","url":null,"abstract":"We analyze whether there are negative (positive) long-term effects of austerity measures (stimulus measures) on potential output growth. Based on the approach of Blanchard and Leigh (2013) and Fatas and Summers (2016) and using a novel dataset of narratively identified fiscal policy shocks, we estimate the impact of these shocks on potential output. We robustly find strong and persistent long-run multiplier effects for most European Countries in the early years after the financial crisis and subsequent Euro Area crisis. We conclude that early stimulus was beneficial even in the long-run, while the subsequent turn to austerity was badly timed and thus not only deepened the crisis but caused evitable hysteresis effects.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiparametric Fourier-Dependent Sieve IV Estimator (SPIV) For Truncated Data","authors":"Nir Billfeld, Moshe Kim","doi":"10.2139/ssrn.3308012","DOIUrl":"https://doi.org/10.2139/ssrn.3308012","url":null,"abstract":"The validity of the IV estimator relies on the orthogonality with respect to the random disturbance. However, in cases of endogenously truncated data as well as in other instances (e.g., censored data) which is very frequently the nature of data used in empirical research, there exists severe contamination in the disturbance due to the endogenous selection process. Such a contamination implies that even if the instrumental variable and the random disturbance are unconditionally independent, they are yet conditionally jointly dependent given the selection variable. The rationale is that the endogenous selection process generates a comovement between the IV and the disturbance which is related to the variation in the selection equation’s covariates. This contamination propagates additional bias introduced into the parameter estimates of the various covariates. Consequently, not only does the conventional IV not solve the problem it is intended to, but rather it introduces additional bias into the parameter estimates of the various covariates of the substantive equation. Our empirical implementation shows that even under mild correlation between the random disturbances, the resulting bias in the estimated parameter of the endogenous covariate in the substantive equation can amount to almost tenfold the true parameter value. We offer a semiparametric Fourier-dependent Sieve IV (SPIV) estimator correcting for both truncation as well as endogeneity biases. The Fourier estimator is a functional of the orthonormal polynomial sequence family. The most attractive feature of this estimator for our purposes is that it intrinsically prevents potential multicollinearity problems, a feature the kernel estimator does not possess. The proposed estimator removes the hurdle which prevents orthogonality under truncation or other misspecifications. Using Monte Carlo simulations attest to very high accuracy of our offered semiparametric Sieve IV estimator as well as high efficiency as reflected by √n consistency. These results have been verified by utilizing 2,000,000 different distribution functions, generating 100 million realizations to construct the various data sets.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117017838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scoring Rule for Factor and Autoregressive Models Under Misspecification","authors":"R. Casarin, Fausto Corradin, F. Ravazzolo, D. Sartore","doi":"10.2139/ssrn.3219696","DOIUrl":"https://doi.org/10.2139/ssrn.3219696","url":null,"abstract":"Factor models (FM) are now widely used for forecasting with large set of time series. Another class of models, which can be easily estimated and used in a large dimensional setting, is multivariate autoregressive models (MAR), where independent autoregressive processes are assumed for the series in the panel. We compare the forecasting abilities of FM and MAR models when assuming both models are misspecified and the data generating process is a vector autoregressive model. We establish which conditions need to be satisfied for a FM to overperform MAR in terms of mean square forecasting error. The condition indicates in presence of misspecification that FM is not always overperforming MAR and that the FM predictive performance depends crucially on the parameter values of the data generating process. Building on the theoretical relationship between FM and MAR predictive performances, we provide a scoring rule which can be evaluated on the data to either select the model, or combine the models in forecasting exercises. Some numerical illustrations are provided both on simulated data and on wel-known large economic datasets. The empirical results show that the frequency of the true positive signals is larger when FM and MAR forecasting performances differ substantially and it decreases as the horizon increases.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121733642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Empirical Validation Protocol for Large-Scale Agent-Based Models","authors":"S. Barde, Sander van der Hoog","doi":"10.2139/ssrn.2992473","DOIUrl":"https://doi.org/10.2139/ssrn.2992473","url":null,"abstract":"Despite recent advances in bringing agent-based models (ABMs) to the data, the estimation or calibration of model parameters remains a challenge, especially when it comes to large-scale agentbased macroeconomic models. Most methods, such as the method of simulated moments (MSM), require in-the-loop simulation of new data, which may not be feasible for such computationally heavy simulation models. The purpose of this paper is to provide a proof-of-concept of a generic empirical validation methodology for such large-scale simulation models. We introduce an alternative ‘large-scale’ empirical validation approach, and apply it to the Eurace@Unibi macroeconomic simulation model (Dawid et al., 2016). This model was selected because it displays strong emergent behaviour and is able to generate a wide variety of nonlinear economic dynamics, including endogenous business- and financial cycles. In addition, it is a computationally heavy simulation model, so it fits our targeted use-case. The validation protocol consists of three stages. At the first stage we use Nearly-Orthogonal Latin Hypercube sampling (NOLH) in order to generate a set of 513 parameter combinations with good space-filling properties. At the second stage we use the recently developed Markov Information Criterion (MIC) to score the simulated data against empirical data. Finally, at the third stage we use stochastic kriging to construct a surrogate model of the MIC response surface, resulting in an interpolation of the response surface as a function of the parameters. The parameter combinations providing the best fit to the data are then identified as the local minima of the interpolated MIC response surface. The Model Confidence Set (MCS) procedure of Hansen et al. (2011) is used to restrict the set of model calibrations to those models that cannot be rejected to have equal predictive ability, at a given confidence level. Validation of the surrogate model is carried out by re-running the second stage of the analysis on the so identified optima and cross-checking that the realised MIC scores equal the MIC scores predicted by the surrogate model. The results we obtain so far look promising as a first proof-of-concept for the empirical validation methodology since we are able to validate the model using empirical data series for 30 OECD countries and the euro area. The internal validation procedure of the surrogate model also suggests that the combination of NOLH sampling, MIC measurement and stochastic kriging yields reliable predictions of the MIC scores for samples not included in the original NOLH sample set. In our opinion, this is a strong indication that the method we propose could provide a viable statistical machine learning technique for the empirical validation of (large-scale) ABMs","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126223262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing Long-Term Coupling and Short-Term Decoupling Crude Oil and Natural Gas Prices","authors":"Hayette Gatfaoui","doi":"10.2139/ssrn.2872780","DOIUrl":"https://doi.org/10.2139/ssrn.2872780","url":null,"abstract":"Recent research has focused on the prevailing relationship between crude oil and natural gas prices. Nowadays, it is well acknowledged that crude oil prices often drive natural gas prices. However, their relationship is unstable over time so that it exhibits regime changes. As a result, crude oil and natural gas prices tend to decouple in the short term while coupling together in the long term. Our present work captures the long-term coupling and short-term decoupling behaviors of crude oil and natural gas prices. Using Kalman filter methodology in linear regressions, we describe such time-varying relationship while allowing for stochastic coefficients (e.g. capturing local linear trend, non-stationarity). As a result, our findings open the door to possible short- and/or long-term arbitrage strategies between crude oil and natural gas prices.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123427221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence Intervals for High-Dimensional Partially Linear Single-Index Models","authors":"Thomas Gueuning, G. Claeskens","doi":"10.2139/ssrn.2770528","DOIUrl":"https://doi.org/10.2139/ssrn.2770528","url":null,"abstract":"We study partially linear single-index models where both model parts may contain high-dimensional variables. While the single-index part is of fixed dimension, the dimension of the linear part is allowed to grow with the sample size. Due to the addition of penalty terms to the loss function in order to provide sparse estimators, such as obtained by lasso or smoothly clipped absolute deviation, the construction of confidence intervals for the model parameters is not as straightforward as in the classical low-dimensional data framework. By adding a correction term to the penalized estimator a desparsified estimator is obtained for which asymptotic normality is proven. We study the construction of confidence intervals and hypothesis tests for such models. The simulation results show that the method performs well for high-dimensional single-index models.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the (Political) Origin of ‘Corporate Governance’ Species","authors":"Massimiliano Vatiero","doi":"10.1111/joes.12147","DOIUrl":"https://doi.org/10.1111/joes.12147","url":null,"abstract":"Although economies, business practices, and living standards have converged since WWII, corporate structures continue to differ among the advanced economies of the world. Looking at the diversity of corporate structures of large-sized firms around the world (and over time) would fascinate Charles Darwin. This work develops a critical review of the literature on political determinants of corporate governance through the Darwinian theory (including some Lamarckian aspects). As Darwin, in his work \"On the Origin of Species\", explicates the diversity of species of tortoises, finches, and iguanas of the Galapagos Islands, so Darwinism may contribute in understanding the origin and the persistence of corporate diversity. In particular, this article takes into account politics-driven variations, their inheritances, and the subsequent selection of advantageous \"corporate\" attributes.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129265259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximating Time Varying Structural Models with Time Invariant Structures","authors":"F. Canova, Filippo Ferroni, C. Matthes","doi":"10.2139/ssrn.2697582","DOIUrl":"https://doi.org/10.2139/ssrn.2697582","url":null,"abstract":"The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identification and inferential distortions when a constant parameter model is incorrectly assumed are examined. Likelihood and VAR-based estimates of the structural dynamics when parameter variations are neglected are compared. Time variations in the financial frictions of Gertler and Karadi's (2010) model are studied.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114587408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Case of Gold and Silver: A New Algorithm for Pairs Trading","authors":"Dr.Jay Desai, Arti Trivedi, Nisarg A Joshi","doi":"10.2139/ssrn.2152324","DOIUrl":"https://doi.org/10.2139/ssrn.2152324","url":null,"abstract":"In this paper we propose a new algorithm for pairs trading. Pairs Trading is a very popular trading strategy also known as market neutral position. The basic idea is to create a long/short position with securities that move together. Securities having strong co-relation (We propose correlation greater then 0.90) can be traded by using the proposed method in this paper. The basic concept of stochastic is applied to find the entry and exit points of a trade. The algorithm is tested on gold and silver prices for market neutral position. From a researcher’s point of view as per Efficient Market Hypothesis theory, pairs trading strategy should not result in positive returns as the past behavior of a stock price reflects the information flow of past. And has no effect on the future prices. The main objective of this research is to propose a simple method of pairs trading useful to market practitioners and researchers. The proposed algorithm generated 100% accurate trades with return of 44.45% for the test period.","PeriodicalId":219959,"journal":{"name":"ERN: Other Econometrics: Single Equation Models (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115240850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}