This article examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England. We attempt to quantify the effects of these purchases by focusing on the impact ...of lower long-term interest rates on the wider economy. We use three different models to estimate the impact of QE on output and inflation: a large Bayesian vector autoregression (VAR), a change-point structural VAR and a time-varying parameter VAR. Our estimates suggest that QE may have had a peak effect on the level of real GDP of around 1½% and a peak effect on annual CPI inflation of about 1¼% points.
This paper provides a characterisation of the degree of cross-sectional dependence in a two dimensional array, {xit, i = 1, 2, ...N; t = 1, 2, ..., T} in terms of the rate at which the variance of ...the cross-sectional average of the observed data varies with N. Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of xt
= (x
1t
, x
2t
, ..., xNt
)′ rises with N. We represent the degree of cross-sectional dependence by α, which we refer to as the ‘exponent of cross-sectional dependence’, and define it by the standard deviation, Std(x̄t
) = O (N
α–1), where x̄t
is a simple cross-sectional average of xit
. We propose bias corrected estimators, derive their asymptotic properties for α > 1/2 and consider a number of extensions. We include a detailed Monte Carlo simulation study supporting the theoretical results. We also provide a number of empirical applications investigating the degree of inter-linkages of real and financial variables in the global economy.
Summary
This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including ...GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when conditioning on other relevant information, but the performance of the latter varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the highest forecast improvements relative to existing text‐based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most.
Measurement of factor strength: Theory and practice Bailey, Natalia; Kapetanios, George; Pesaran, M. Hashem
Journal of applied econometrics,
August 2021, 2021-08-00, 20210801, Volume:
36, Issue:
5
Journal Article
Peer reviewed
Open access
This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The estimator is based on the number of statistically significant factor loadings, ...taking multiple testing into account. Both cases of observed and unobserved factors are considered. The small sample properties of the proposed estimator are investigated using Monte Carlo experiments. It is shown that the proposed estimation and inference procedures perform well and have excellent power properties, especially when the factor strength is sufficiently high. Empirical applications to factor models for asset returns show that out of 146 factors recently considered in the finance literature, only the market factor is truly strong, while all other factors are at best semi‐strong, with their strength varying considerably over time. Similarly, we only find evidence of semi‐strong factors using a large number of US macroeconomic indicators.
. In this paper we provide tests for the unit‐root hypothesis against the occurrence of an unspecified number of breaks which may be larger than 2 but smaller that the maximum number of breaks ...allowed, m, in univariate time‐series models. The advocated procedure is considerably less computationally intensive than those widely used in the literature. We provide critical values for the test and examine its small sample properties through Monte Carlo experiments.
The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be ...adequately modeled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modeling. This article provides an alternative method to information criteria as a tool for estimating the number of factors in large dimensional factor models. The new method is robust to considerable cross-sectional and temporal dependence. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favorable for the new method and suggest that it is a reasonable alternative to existing methods.
This paper constructs a coincident indicator for the United Kingdom employing—for the first time—a novel set of “faster” indicators recently published by the Office for National Statistics. Most of ...these variables were first released during the COVID‐19 pandemic with the aim of facilitating economics and social research by more timely indicators. The empirical evidence suggests that a coincident indicator based on a novel weekly dataset successfully captures the economic conditions in real time. We further use this indicator in an out‐of‐sample macroeconomic nowcasting exercise targeting monthly economic growth, prices and retail sales.
This paper investigates the predictive ability of a brand new dataset that is based on big unstructured data published by the UK Office for National Statistics as “Faster Indicators of UK Economic ...Activity.” We consider some indicative ways for use in macroeconomic nowcasting. Even though the ONS confirms that the newly introduced big data‐based indicators are not constructed with forecasting purposes in mind, and applied researchers should be cautious when using them in this way, a simple out‐of‐sample nowcasting exercise reveals partial evidence that this dataset has some predictive power over GDP growth. Our results, which show a positive and encouraging first step towards the use of this type of data, suggest that national statistics agencies should allocate more resources in constructing big data‐based databases.
We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation ...issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.
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