Econometrics

Open AccessEditor’s ChoiceArticle

Forecast Accuracy Matters for Hurricane Damage

by

Econometrics 2020, 8(2), 18; https://doi.org/10.3390/econometrics8020018 - 14 May 2020

Cited by 8 | Viewed by 5390

I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. This relationship holds across [...] Read more.

I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. This relationship holds across a wide range of model specifications and when controlling for ex-ante uncertainty and potential endogeneity. Using a counterfactual exercise I find that the cumulative reduction in damage from forecast improvements since 1970 is about $82 billion, which exceeds the U.S. government’s spending on the forecasts and private willingness to pay for them. Full article

(This article belongs to the Collection Econometric Analysis of Climate Change)

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Open AccessFeature PaperEditor’s ChoiceArticle

Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors

by

Matteo Barigozzi

,

Marco Lippi

and

Matteo Luciani

Econometrics 2020, 8(1), 3; https://doi.org/10.3390/econometrics8010003 - 04 Feb 2020

Cited by 9 | Viewed by 4677

Abstract

Large-dimensional dynamic factor models and dynamic stochastic general equilibrium models, both widely used in empirical macroeconomics, deal with singular stochastic vectors, i.e., vectors of dimension r which are driven by a q-dimensional white noise, with

q < r

. The present paper [...] Read more.

Large-dimensional dynamic factor models and dynamic stochastic general equilibrium models, both widely used in empirical macroeconomics, deal with singular stochastic vectors, i.e., vectors of dimension r which are driven by a q-dimensional white noise, with

q < r

. The present paper studies cointegration and error correction representations for an

I (1)

singular stochastic vector

y_{t}

. It is easily seen that

y_{t}

is necessarily cointegrated with cointegrating rank

c \geq r - q

. Our contributions are: (i) we generalize Johansen’s proof of the Granger representation theorem to

I (1)

singular vectors under the assumption that

y_{t}

has rational spectral density; (ii) using recent results on singular vectors by Anderson and Deistler, we prove that for generic values of the parameters the autoregressive representation of

y_{t}

has a finite-degree polynomial. The relationship between the cointegration of the factors and the cointegration of the observable variables in a large-dimensional factor model is also discussed. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)

Open AccessEditor’s ChoiceArticle

A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

by

David Trafimow

Econometrics 2019, 7(2), 26; https://doi.org/10.3390/econometrics7020026 - 04 Jun 2019

Cited by 30 | Viewed by 8256

Abstract

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none [...] Read more.

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone. Full article

(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)

Open AccessEditor’s ChoiceArticle

Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

by

Pierre Perron

and

Yohei Yamamoto

Econometrics 2019, 7(2), 22; https://doi.org/10.3390/econometrics7020022 - 21 May 2019

Cited by 8 | Viewed by 5634

Abstract

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, [...] Read more.

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power. Full article

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Open AccessEditor’s ChoiceArticle

Covariance Prediction in Large Portfolio Allocation

by

,

,

and

Econometrics 2019, 7(2), 19; https://doi.org/10.3390/econometrics7020019 - 09 May 2019

Cited by 8 | Viewed by 6643

Abstract

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. [...] Read more.

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average return, information ratio and Sortino’s ratio. We find that no method is the best in all scenarios and the performance depends on the criterion, the period of analysis and the rebalancing strategy. Full article

(This article belongs to the Special Issue Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data)

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