Research

22 pages, 1741 KiB

Open AccessArticle

Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data

by Duo Qin, Sophie van Huellen, Qing Chao Wang and Thanos Moraitis

Econometrics 2022, 10(2), 22; https://doi.org/10.3390/econometrics10020022 - 19 Apr 2022

Cited by 2 | Viewed by 3079

Aggregate financial conditions indices (FCIs) are constructed to fulfil two aims: (i) The FCIs should resemble non-model-based composite indices in that their composition is adequately invariant for concatenation during regular updates; (ii) the concatenated FCIs should outperform financial variables conventionally used as leading [...] Read more.

Aggregate financial conditions indices (FCIs) are constructed to fulfil two aims: (i) The FCIs should resemble non-model-based composite indices in that their composition is adequately invariant for concatenation during regular updates; (ii) the concatenated FCIs should outperform financial variables conventionally used as leading indicators in macro models. Both aims are shown to be attainable once an algorithmic modelling route is adopted to combine leading indicator modelling with the principles of partial least-squares (PLS) modelling, supervised dimensionality reduction, and backward dynamic selection. Pilot results using US data confirm the traditional wisdom that financial imbalances are more likely to induce macro impacts than routine market volatilities. They also shed light on why the popular route of principal-component based factor analysis is ill-suited for the two aims. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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25 pages, 1549 KiB

Open AccessArticle

Model Validation and DSGE Modeling

by Niraj Poudyal and Aris Spanos

Econometrics 2022, 10(2), 17; https://doi.org/10.3390/econometrics10020017 - 07 Apr 2022

Cited by 2 | Viewed by 3339

Abstract

The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of [...] Read more.

The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive adequacy and secure the former before assessing the latter. The paper untangles the statistical from the substantive premises of inference to delineate the above-mentioned issues and propose solutions. The discussion revolves around a typical DSGE model using US quarterly data. It is shown that this model is statistically misspecified, and when respecified to arrive at a statistically adequate model gives rise to the Student’s t VAR model. This statistical model is shown to (i) provide a sound basis for testing the DSGE overidentifying restrictions as well as probing the identifiability of the deep parameters, (ii) suggest ways to meliorate its substantive inadequacy, and (iii) give rise to reliable forecasts and policy simulations. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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15 pages, 500 KiB

Open AccessEditor’s ChoiceArticle

A Theory-Consistent CVAR Scenario for a Monetary Model with Forward-Looking Expectations

by Katarina Juselius

Econometrics 2022, 10(2), 16; https://doi.org/10.3390/econometrics10020016 - 06 Apr 2022

Cited by 3 | Viewed by 2256

Abstract

A theory-consistent CVAR scenario describes a set of testable regularities capturing basic assumptions of the theoretical model. Using this concept, the paper considers a standard model for exchange rate determination with forward-looking expectations and shows that all assumptions about the model’s shock structure [...] Read more.

A theory-consistent CVAR scenario describes a set of testable regularities capturing basic assumptions of the theoretical model. Using this concept, the paper considers a standard model for exchange rate determination with forward-looking expectations and shows that all assumptions about the model’s shock structure and steady-state behavior can be formulated as testable hypotheses on common stochastic trends and cointegration. The basic stationarity assumptions of the monetary model failed to obtain empirical support. They were too restrictive to explain the observed long persistent swings in the real exchange rate, the real interest rates, and the inflation and interest rate differentials. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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25 pages, 495 KiB

Open AccessArticle

Causal Transmission in Reduced-Form Models

by Vassilios Bazinas and Bent Nielsen

Econometrics 2022, 10(2), 14; https://doi.org/10.3390/econometrics10020014 - 24 Mar 2022

Cited by 1 | Viewed by 3066

Abstract

We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two [...] Read more.

We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables given the catalyst. The method combines elements from instrumental variable analysis and Cholesky decomposition of structural vector autoregressions. We give conditions for uniqueness of the causal transmission. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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14 pages, 426 KiB

Open AccessFeature PaperArticle

Jointly Modeling Male and Female Labor Participation and Unemployment

by David H. Bernstein and Andrew B. Martinez

Econometrics 2021, 9(4), 46; https://doi.org/10.3390/econometrics9040046 - 07 Dec 2021

Cited by 1 | Viewed by 3692

Abstract

The COVID-19 pandemic resulted in the most abrupt changes in U.S. labor force participation and unemployment since the Second World War, with different consequences for men and women. This paper models the U.S. labor market to help to interpret the pandemic’s effects. After [...] Read more.

The COVID-19 pandemic resulted in the most abrupt changes in U.S. labor force participation and unemployment since the Second World War, with different consequences for men and women. This paper models the U.S. labor market to help to interpret the pandemic’s effects. After replicating and extending Emerson’s (2011) model of the labor market, we formulate a joint model of male and female unemployment and labor force participation rates for 1980–2019 and use it to forecast into the pandemic to understand the pandemic’s labor market consequences. Gender-specific differences were particularly large at the pandemic’s outset; lower labor force participation persists. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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20 pages, 497 KiB

Open AccessFeature PaperArticle

Forecasting US Inflation in Real Time

by Chad Fulton and Kirstin Hubrich

Econometrics 2021, 9(4), 36; https://doi.org/10.3390/econometrics9040036 - 09 Oct 2021

Cited by 2 | Viewed by 4345

Abstract

We analyze real-time forecasts of US inflation over 1999Q3–2019Q4 and subsamples, investigating whether and how forecast accuracy and robustness can be improved with additional information such as expert judgment, additional macroeconomic variables, and forecast combination. The forecasts include those from the Federal Reserve [...] Read more.

We analyze real-time forecasts of US inflation over 1999Q3–2019Q4 and subsamples, investigating whether and how forecast accuracy and robustness can be improved with additional information such as expert judgment, additional macroeconomic variables, and forecast combination. The forecasts include those from the Federal Reserve Board’s Tealbook, the Survey of Professional Forecasters, dynamic models, and combinations thereof. While simple models remain hard to beat, additional information does improve forecasts, especially after 2009. Notably, forecast combination improves forecast accuracy over simpler models and robustifies against bad forecasts; aggregating forecasts of inflation’s components can improve performance compared to forecasting the aggregate directly; and judgmental forecasts, which may incorporate larger and more timely datasets in conjunction with model-based forecasts, improve forecasts at short horizons. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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21 pages, 673 KiB

Open AccessArticle

Forecasting FOMC Forecasts

by S. Yanki Kalfa and Jaime Marquez

Econometrics 2021, 9(3), 34; https://doi.org/10.3390/econometrics9030034 - 14 Sep 2021

Cited by 2 | Viewed by 3463

Abstract

(Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves. Monetary policy is forward-looking, [...] Read more.

(Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves. Monetary policy is forward-looking, and as part of the FOMC’s effort toward transparency, the FOMC publishes its (forward-looking) economic projections. The overall views on the economy of the FOMC participants–as characterized by the median of their projections for inflation, unemployment, and the Fed’s policy rate–are themselves predictable by information publicly available at the time of the FOMC’s meeting. Their projections also communicate systematic behavior on the part of the FOMC’s participants. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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35 pages, 516 KiB

Open AccessFeature PaperArticle

Selecting a Model for Forecasting

by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry

Econometrics 2021, 9(3), 26; https://doi.org/10.3390/econometrics9030026 - 25 Jun 2021

Cited by 7 | Viewed by 4833

Abstract

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a [...] Read more.

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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14 pages, 403 KiB

Open AccessArticle

Are Soybean Yields Getting a Free Ride from Climate Change? Evidence from Argentine Time Series Data

by Hildegart Ahumada and Magdalena Cornejo

Econometrics 2021, 9(2), 24; https://doi.org/10.3390/econometrics9020024 - 04 Jun 2021

Cited by 7 | Viewed by 3386

Abstract

We analyze the influence of climate change on soybean yields in a multivariate time-series framework for a major soybean producer and exporter—Argentina. Long-run relationships are found in partial systems involving climatic, technological, and economic factors. Automatic model selection simplifies dynamic specification for a [...] Read more.

We analyze the influence of climate change on soybean yields in a multivariate time-series framework for a major soybean producer and exporter—Argentina. Long-run relationships are found in partial systems involving climatic, technological, and economic factors. Automatic model selection simplifies dynamic specification for a model of soybean yields and permits encompassing tests of different economic hypotheses. Soybean yields adjust to disequilibria that reflect technological improvements to seed and crops practices. Climatic effects include (a) a positive effect from increased CO

_{2}

concentrations, which may capture accelerated photosynthesis, and (b) a negative effect from high local temperatures, which could increase with continued global warming. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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19 pages, 413 KiB

Open AccessFeature PaperArticle

Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

by Eric Hillebrand, Søren Johansen and Torben Schmith

Econometrics 2020, 8(4), 41; https://doi.org/10.3390/econometrics8040041 - 02 Nov 2020

Cited by 2 | Viewed by 3936

Abstract

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two [...] Read more.

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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16 pages, 303 KiB

Open AccessFeature PaperArticle

Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?

by Michael P. Clements

Econometrics 2020, 8(2), 16; https://doi.org/10.3390/econometrics8020016 - 06 May 2020

Cited by 6 | Viewed by 3609

Abstract

We apply a bootstrap test to determine whether some forecasters are able to make superior probability assessments to others. In contrast to some findings in the literature for point predictions, there is evidence that some individuals really are better than others. The testing [...] Read more.

We apply a bootstrap test to determine whether some forecasters are able to make superior probability assessments to others. In contrast to some findings in the literature for point predictions, there is evidence that some individuals really are better than others. The testing procedure controls for the different economic conditions the forecasters may face, given that each individual responds to only a subset of the surveys. One possible explanation for the different findings for point predictions and histograms is explored: that newcomers may make less accurate histogram forecasts than experienced respondents given the greater complexity of the task. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

35 pages, 1330 KiB

Open AccessArticle

Balanced Growth Approach to Tracking Recessions

by Marta Boczoń and Jean-François Richard

Econometrics 2020, 8(2), 14; https://doi.org/10.3390/econometrics8020014 - 23 Apr 2020

Viewed by 4021

Abstract

In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely [...] Read more.

In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely upon an Error Correction Mechanism to adjust to changes in target ratios driven by an underlying state Vector AutoRegressive process. Our proposal is illustrated by an application to a pilot Real Business Cycle model for the US economy from 1948 to 2019. An extensive recursive validation exercise over the last 35 years, covering 3 recessions, is used to highlight its parameters invariance, tracking and 1- to 3-step ahead forecasting performance, outperforming those of an unconstrained benchmark Vector AutoRegressive model. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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28 pages, 1686 KiB

Open AccessArticle

HAR Testing for Spurious Regression in Trend

by Peter C. B. Phillips, Xiaohu Wang and Yonghui Zhang

Econometrics 2019, 7(4), 50; https://doi.org/10.3390/econometrics7040050 - 16 Dec 2019

Cited by 4 | Viewed by 5433

Abstract

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in [...] Read more.

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips 1986, 1998; Sun 2004, 2014b) the usual t test and HAC standardized test fail to control size as the sample size

n \to \infty

in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors

K \to \infty,

all three statistics, including the HAR test, diverge and fail to control size as

n \to \infty

. These findings are relevant to high-dimensional nonstationary time series regressions where machine learning methods may be employed. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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