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  1. Bridging factor and sparse models
    Autor*in: Fan, Jianqing; Masini, Ricardo; Medeiros, Marcelo C.
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a... mehr

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    Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to efficiently explore all the information in high-dimensional datasets. The method is based on a exible model for panel data, called factor-augmented regression model with both observable, latent common factors, as well as idiosyncratic components as high-dimensional covariate variables. This model not only includes both factor regression and sparse regression as specific models but also significantly weakens the cross-sectional dependence and hence facilitates model selection and interpretability. The methodology consists of three steps. At each step, remaining cross-section dependence can be inferred by a novel test for covariance structure in high-dimensions. We developed asymptotic theory for the factoraugmented sparse regression model and demonstrated the validity of the multiplier bootstrap for testing high-dimensional covariance structure. This is further extended to testing highdimensional partial covariance structures. The theory and methods are further supported by an extensive simulation study and applications to the construction of a partial covariance network of the financial returns and a prediction exercise for a large panel of macroeconomic time series from FRED-MD database.

     
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    hdl: 10419/249729
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 681
    Schlagworte: Factor models; sparse regression; high-dimensional; supervised learning; hypothesis testing; covariance structure
    Umfang: 1 Online-Ressource (circa 78 Seiten), Illustrationen
  2. Jumps in stock prices: new insights from old data
    Autor*in: Johnson, James A.; Medeiros, Marcelo C.; Paye, Bradley S.
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    We characterize jump dynamics in stock market returns using a novel series of intraday prices covering over 80 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news... mehr

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    We characterize jump dynamics in stock market returns using a novel series of intraday prices covering over 80 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news often involving major wars drives jump activity in early decades, whereas scheduled news and especially news pertaining to monetary policy drives jump activity in recent decades. Jump variation measures forecast excess stock market returns, consistent with theory. Results support models featuring a separate jump factor such that risk premium dynamics are not fully captured by volatility state variables.

     
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    hdl: 10419/249730
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 682
    Umfang: 1 Online-Ressource (circa 54 Seiten), Illustrationen
  3. Lockdown effects in US states: an artificial counterfactual approach
    Autor*in: Carneiro, Carlos B.; Ferreira, Iuri H.; Medeiros, Marcelo C.; Pires, Henrique F.; Zilberman, Eduardo
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies,... mehr

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    We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies, and divide them among treated and control groups. For each treated state, we construct an artificial counterfactual. On average, and in the very short-run, the counterfactual accumulated number of cases would be two times larger if lockdown policies were not implemented.

     
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    hdl: 10419/249721
    Auflage/Ausgabe: This version: January 2021
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 673
    Schlagworte: Covid-19 cases; lockdown effects; mobility; ArCo; synthetic control
    Umfang: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  4. Do we exploit all information for counterfactual analysis?
    benefits of factor models and idiosyncratic correction
    Autor*in: Fan, Jianqing; Masini, Ricardo; Medeiros, Marcelo C.
    Erschienen: [2020]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the... mehr

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    The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. In high-dimensional setting, we often use principal component or (weakly) sparse regression to estimate counterfactuals. Do we use enough data information? To better estimate the effects of price changes on the sales in our case study, we propose a general framework on counterfactual analysis for high dimensional dependent data. The framework includes both principal component regression and sparse linear regression as specific cases. It uses both factor and idiosyncratic components as predictors for improved counterfactual analysis, resulting a method called Factor-Adjusted Regularized Method for Treatment (FarmTreat) evaluation. We demonstrate convincingly that using either factors or sparse regression is inadequate for counterfactual analysis in many applications and the case for information gain can be made through the use of idiosyncratic components. We also develop theory and methods to formally answer the question if common factors are adequate for estimating counterfactuals. Furthermore, we consider a simple resampling approach to conduct inference on the treatment effect as well as bootstrap test to access the relevance of the idiosyncratic components. We apply the proposed method to evaluate the effects of price changes on the sales of a set of products based on a novel large panel of sale data from a major retail chain in Brazil and demonstrate the benefits of using additional idiosyncratic components in the treatment effect evaluations.

     
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    hdl: 10419/249726
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 678
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  5. Online action learning in high dimensions: a new exploration rule for contextual et-greedy heuristics
    Autor*in: Flores, Claudio C.; Medeiros, Marcelo C.
    Erschienen: [2020]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    Bandit problems are pervasive in various fields of research and are also present in several practical applications. Examples, including dynamic pricing and assortment and the design of auctions and incentives, permeate a large number of sequential... mehr

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    Bandit problems are pervasive in various fields of research and are also present in several practical applications. Examples, including dynamic pricing and assortment and the design of auctions and incentives, permeate a large number of sequential treatment experiments. Different applications impose distinct levels of restrictions on viable actions. Some favor diversity of outcomes, while others require harmful actions to be closely monitored or mainly avoided. In this paper, we extend one of the most popular bandit solutions, the original et-greedy heuristics, to high-dimensional contexts. Moreover, we introduce a competing exploration mechanism that counts with searching sets based on order statistics. We view our proposals as alternatives for cases where pluralism is valued or, in the opposite direction, cases where the end-user should carefully tune the range of exploration of new actions. We find reasonable bounds for the cumulative regret of a decaying et-greedy heuristic in both cases, and we provide an upper bound for the initialization phase that implies the regret bounds when order statistics are considered to be at most equal but mostly better than the case when random searching is the sole exploration mechanism. Additionally, we show that endusers have sufficient exibility to avoid harmful actions since any cardinality for the higher-order statistics can be used to achieve stricter upper bound. In a simulation exercise, we show that the algorithms proposed in this paper outperform simple and adapted counterparts.

     
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    hdl: 10419/249722
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 674
    Umfang: 1 Online-Ressource (circa 44 Seiten), Illustrationen
  6. Machine learning advances for time series forecasting
    Autor*in: Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F.
    Erschienen: [2020]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized... mehr

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    In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.

     
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    hdl: 10419/249727
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 679
    Umfang: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  7. Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations
    Autor*in: Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F.
    Erschienen: [2020]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle... mehr

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    There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an L1 mixingale type condition on the centered conditional covariance matrices. This condition covers L1-NED sequences and strong (ff-) mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications that were ruled out by assumption in previous studies.

     
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    hdl: 10419/249728
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 680
    Umfang: 1 Online-Ressource (circa 31 Seiten), Illustrationen
  8. Residual based nodewise regression in factor models with ultra-high dimensions: analysis of mean-variance portfolio efficiency and estimation of out-of-sample and constrained maximum Sharpe ratios
    Autor*in: Caner, Mehmet; Medeiros, Marcelo C.; Vasconcelos, Gabriel F. R.
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a... mehr

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    We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a new hybrid model where factor models are combined with feasible nodewise regression. Returns are generated from increasing number of factors plus idiosyncratic components (errors). The precision matrix of the idiosyncratic terms is assumed to be sparse, but the respective covariance matrix can be non-sparse. Since the nodewise regression is not feasible due to unknown nature of errors, we provide a feasible-residual based nodewise regression to estimate the precision matrix of errors, as a new method. Next, we show that the residual-based nodewise regression provides a consistent estimate for the precision matrix of errors. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with increasing number of factors. Benefiting from the consistency of the precision matrix estimate of returns, we show that: (1) the portfolios in high dimensions are mean-variance efficient; (2) maximum out-of-sample Sharpe ratio estimator is consistent and the number of assets slows the convergence up to a logarithmic factor; (3) the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (4) the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios.

     
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    hdl: 10419/249732
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 684
    Schlagworte: Portfolio-Management; Kapitalmarkttheorie; Regressionsanalyse; Schätztheorie
    Umfang: 1 Online-Ressource (circa 73 Seiten), Illustrationen
  9. The proper use of Google Trends in forecasting models
    Autor*in: Medeiros, Marcelo C.; Pires, Henrique
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    It is widely known that Google Trends has become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends... mehr

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    It is widely known that Google Trends has become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.

     
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    hdl: 10419/249731
    Auflage/Ausgabe: This version: Mar 2021
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 683
    Schlagworte: Google trends; forecasting; nowcasting; big data
    Umfang: 1 Online-Ressource (circa 18 Seiten), Illustrationen
  10. Short-term Covid-19 forecast for latecomers
    Autor*in: Medeiros, Marcelo C.; Street, Alexandre; Valladão, Davi; Vasconcelos, Gabriel; Zilberman, Eduardo
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental... mehr

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    The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers i.e., countries where cases of the disease started to appear some time after others. In particular, we propose a penalized (LASSO) regression with an error correction mechanism to construct a model of a latecomer in terms of the other countries that were at a similar stage of the pandemic some days before. By tracking the number of cases in those countries, we forecast through an adaptive rolling-window scheme the number of cases and deaths in the latecomer. We apply this methodology to four different countries: Brazil, Chile, Mexico, and Portugal. We show that the methodology performs very well. These forecasts aim to foster a better short-run management of the health system capacity and can be applied not only to countries but to different regions within a country, as well.

     
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    hdl: 10419/249718
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 670
    Schlagworte: Coronavirus; Prognoseverfahren; Regressionsanalyse; Kointegration; Sterblichkeit; Theorie; Brasilien
    Umfang: 1 Online-Ressource (circa 31 Seiten), Illustrationen
  11. From zero to hero: realized partial (co)variances
    Autor*in: Bollerslev, Tim; Medeiros, Marcelo C.; Patton, Andrew J.; Quaedvlieg, Rogier
    Erschienen: [2021]
    Verlag:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen, Kinnebrock and Shephard (2010) and Bollerslev, Li, Patton and Quaedvlieg (2020a) to allow for a finer... mehr

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    This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen, Kinnebrock and Shephard (2010) and Bollerslev, Li, Patton and Quaedvlieg (2020a) to allow for a finer decomposition of realized (co)variances. The new "realized partial (co)variances" allow for multiple thresholds with various locations, rather than the single fixed threshold of zero used in semi (co)variances. We adopt methods from machine learning to choose the thresholds to maximize the out-ofsample forecast performance of time series models based on realized partial (co)variances. We find that in low dimensional settings it is hard, but not impossible, to improve upon the simple fixed threshold of zero. In large dimensions, however, the zero threshold embedded in realized semi covariances emerges as a robust choice.

     
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    hdl: 10419/249720
    Schriftenreihe: Texto para discussão / PUC Rio, Departamento de Economia ; no. 672
    Schlagworte: High-frequency data; realized variation; volatility forecasting
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen
  12. Central bank intervention, bubbles and risk in Walrasian financial markets
    Autor*in: Chang, Chia-Lin; Ilomäki, Jukka; Laurila, Hannu; McAleer, Michael
    Erschienen: February 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Weitere Identifier:
    hdl: 1765/115605
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 7
    Schlagworte: Zentralbank; Staatliche Einflussnahme; Asymmetrische Information; Spekulationsblase; Finanzmarkt; Investitionsrisiko; Rationalität; Inflationserwartung
    Umfang: 1 Online-Ressource (circa 18 Seiten)
  13. Risk analysis of energy in Vietnam
    Autor*in: Duc Hong Vo; Ngoc Phu Tran; Tam Nguyen-Thanh Duong; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    hdl: 1765/115616
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 18
    Schlagworte: Marktrisiko; Energiewirtschaft; Industrie; Risikomaß; Vietnam
    Umfang: 1 Online-Ressource (circa 21 Seiten), Illustrationen
  14. The impact of jumps and leverage in forecasting the co-volatility of oil and gold futures
    Autor*in: Asai, Manabu; Gupta, Rangan; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    hdl: 1765/115614
    Schriftenreihe: [Econometric Institute Research papers] ; EI2019, 16
    Schlagworte: Ölmarkt; Goldmarkt; Futures; Volatilität; Prognose; USA
    Umfang: 1 Online-Ressource (circa 24 Seiten), Illustrationen
  15. CO2 emissions, energy consumption and economic growth
    evidence from the Trans-Pacific Partnership
    Autor*in: Duc Hong Vo; Nguyen, Ha; Anh The Vo; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    hdl: 1765/115609
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 11
    Schlagworte: Energiekonsum; Erneuerbare Energie; Kernenergie; Treibhausgas-Emissionen; Wirtschaftswachstum; Environmental Kuznets Curve; Zeitreihenanalyse; Kointegration; Kausalanalyse; Asiatisch-pazifischer Raum
    Umfang: 1 Online-Ressource (circa 33 Seiten)
  16. What they did not tell you about algebraic (non-)existence, mathematical (IR-)regularity and (non-)asymptotic properties of the full BEKK dynamic conditional covariance model
    Autor*in: McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Format: Online
    Weitere Identifier:
    hdl: 1765/115612
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 14
    Schlagworte: Dynamische Ökonometrie; Korrelation; Hedging
    Umfang: 1 Online-Ressource (circa 12 Seiten)
  17. Size, internationalization and university rankings
    evaluating and predicting times higher education (THE) data for Japan
    Autor*in: McAleer, Michael; Nakamura, Tamotsu; Watkins, Clinton
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 765/115610
    Auflage/Ausgabe: Revised: March 2019
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 12
    Schlagworte: Hochschule; Ranking-Verfahren; Betriebsgröße; Internationale Geschäftsbeziehungen; Japan
    Umfang: 1 Online-Ressource (circa 25 Seiten), Illustrationen
  18. Rent seeking for export licenses
    application to the Vietnam rice market
    Autor*in: Tan Ngoc Vu; Duc Hong Vo; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/115607
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 9
    Schlagworte: Handelshemmnisse; Exportbeschränkung; Rent-Seeking; Freihandel; Reismarkt; Vietnam
    Umfang: 1 Online-Ressource (circa 34 Seiten)
  19. What they did not tell you about algebraic (non-)existence, mathematical (IR-)regularity and (non-)asymptotic properties of the dynamic conditional correlation (DCC) model
    Autor*in: McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/115611
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 13
    Schlagworte: Dynamische Ökonometrie; Korrelation; Hedging
    Umfang: 1 Online-Ressource (circa 16 Seiten)
  20. Energy consumption and economic growth
    evidence from Vietnam
    Autor*in: Nguyen Ha Minh; Ngoc Hoang Bui; Duc Hong Vo; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/115606
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 8
    Schlagworte: Energiekonsum; Wirtschaftswachstum; Cobb-Douglas-Produktionsfunktion; Zeitreihenanalyse; Kointegration; Kausalanalyse; Vietnam
    Umfang: 1 Online-Ressource (circa 27 Seiten), Illustrationen
  21. Corporate financial distress of industry level listings in an emerging market
    Autor*in: Duc Hong Vo; Binh Vo-Ninh Pham; Trung Vu-Thanh Pham; McAleer, Michael
    Erschienen: March 2019
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/115613
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 15
    Schlagworte: Insolvenz; Aktiengesellschaft; Industrie; Logit-Modell; Vietnam
    Umfang: 1 Online-Ressource (circa 33 Seiten)
  22. Fake news and propaganda
    Trump's democratic America and Hitler's National Socialist (Nazi) Germany
    Autor*in: Allen, David E.; McAleer, Michael
    Erschienen: [2019]
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/115615
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 17
    Umfang: 1 Online-Ressource (circa 21 Seiten), Illustrationen
  23. Modelling the relationship between crude oil and agricultural commodity prices
    Autor*in: Duc Hong Vo; Tan Ngoc Vu; Anh The Vo; McAleer, Michael
    Erschienen: December 2018
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Weitere Identifier:
    hdl: 1765/115608
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 10
    Schlagworte: Agrarpreis; Rohstoffpreis; Ölpreis; Volatilität; Schock; VAR-Modell; Dekompositionsverfahren
    Umfang: 1 Online-Ressource (circa 53 Seiten), Illustrationen
  24. Drawbacks in the 3-factor approach of Fama and French
    (2018)
    Autor*in: Allen, David E.; McAleer, Michael
    Erschienen: [2019]
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Weitere Identifier:
    hdl: 1765/115746
    Schriftenreihe: [Econometric Institute research papers] ; EI2019, 20
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  25. Testing multiple non-nested factor demand systems
    Autor*in: Manera, Matteo; McAleer, Michael
    Erschienen: 1999

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Druck
    Schriftenreihe: Array ; 1999,6
    Schlagworte: Faktornachfrage; Produktionsfunktion; Industrie; Multivariate Analyse; USA
    Umfang: 32 S