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Article
Publication date: 1 August 1997

Ken Holden

Analyses and checks the annual forecasts produced each autumn from four prominent UK economic modelling organizations. Compares these forecasts with those of three Bayesian

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Abstract

Analyses and checks the annual forecasts produced each autumn from four prominent UK economic modelling organizations. Compares these forecasts with those of three Bayesian vectorautoregressive models. Examines the accuracy for each set of forecasts up to four years ahead and for different horizons. Examines the direction of the forecasts and the effect of forming simple combinations of the different forecasts. Finds evidence that while the BVAR forecasts are inferior to those from the economic models, they contain information which could be used in order to improve the other forecasts.

Details

Journal of Economic Studies, vol. 24 no. 4
Type: Research Article
ISSN: 0144-3585

Keywords

Abstract

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

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Book part
Publication date: 18 January 2022

Andreas Pick and Matthijs Carpay

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider…

Abstract

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

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Book part
Publication date: 13 December 2013

Refet S. Gürkaynak, Burçin Kısacıkoğlu and Barbara Rossi

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample…

Abstract

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

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Article
Publication date: 23 July 2020

Tolulope Temilola Osinubi and Philip Akanni Olomola

The study examines the dynamic relationship among globalisation, income inequality and poverty in Mexico, Indonesia, Nigeria and Turkey (MINT countries) between 1980 and 2018.

Abstract

Purpose

The study examines the dynamic relationship among globalisation, income inequality and poverty in Mexico, Indonesia, Nigeria and Turkey (MINT countries) between 1980 and 2018.

Design/methodology/approach

A Bayesian vector autoregressive (BVAR) approach is used as a technique of estimation hanging on the fact that the method uses prior distribution for the estimated parameters.

Findings

The results show that globalisation is a strong predictor of itself in all the MINT countries only in the short run. In the long run, income inequality and poverty strongly influence globalisation, respectively, in Indonesia and Turkey, while globalisation still has more impact on itself in Nigeria. Income inequality has a strong endogenous impact on itself in Mexico and Indonesia over the time horizon, whereas globalisation and poverty are strong predictors of income inequality in the long run in Nigeria and Turkey, respectively. Also, poverty strongly influences itself in all the MINT countries in all the periods, meaning that poverty begets itself in all the MINT countries, except for Indonesia in the long run.

Practical implications

The study suggests that all the MINT countries should ensure political stability and a strong institutional framework to gain from the process of globalisation and to experience reductions in the levels of income inequality and poverty.

Originality/value

This study is distinct from other studies in the sense that an overall globalisation index (GBI) as used by Dreher et al. (2008) is used for the globalisation variable, and the Multidimensional Poverty Index (MPI) is used to capture poverty in all the MINT countries. Also, the research paper uses a BVAR approach as against the classical VAR, and this helps in solving over-fitting problems.

Details

Journal of Economic and Administrative Sciences, vol. 37 no. 2
Type: Research Article
ISSN: 1026-4116

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Article
Publication date: 26 July 2013

Rangan Gupta and Monique Reid

The objective of this paper is to explore the sensitivity of industry‐specific stock returns to monetary policy and macroeconomic news. The paper looks at a range of…

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Abstract

Purpose

The objective of this paper is to explore the sensitivity of industry‐specific stock returns to monetary policy and macroeconomic news. The paper looks at a range of industry‐specific South African stock market indices and evaluates the sensitivity of these indices to various unanticipated macroeconomic shocks.

Design/methodology/approach

The authors begin with an event study, which examines the immediate impact of macroeconomic shocks on the stock market indices, and then use a Bayesian vector autoregressive (BVAR) analysis, which provides insight into the dynamic effects of the shocks on the stock market indices, by allowing them to treat the shocks as exogenous through appropriate setting of priors defining the mean and variance of the parameters in the VAR.

Findings

The results from the event study indicate that with the exception of the gold mining index, where the CPI surprise plays a significant role, monetary surprise is the only variable that consistently negatively affects the stock returns significantly, both at the aggregate and sectoral levels. The BVAR model based on monthly data, however, indicates that, in addition to the monetary policy surprises, the CPI and PPI surprises also affect aggregate stock returns significantly. However, the effects of the CPI and PPI surprises are quite small in magnitude and are mainly experienced at shorter horizons immediately after the shock.

Originality/value

To the best of the authors' knowledge, this is the first study conducted on South Africa which analyses the impact of a wide range of unanticipated macroeconomic shocks on stock returns. This paper improves on earlier efforts by using measures of monetary policy, as well as other macroeconomic news, which more cleanly isolates the unanticipated elements of the monetary policy variable and other macroeconomic indicators, in studying the impact of these surprises on stock returns in South Africa.

Details

Studies in Economics and Finance, vol. 30 no. 3
Type: Research Article
ISSN: 1086-7376

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Book part
Publication date: 30 August 2019

Zhe Yu, Raquel Prado, Steve C. Cramer, Erin B. Quinlan and Hernando Ombao

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates…

Abstract

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
ISBN: 978-1-78973-241-2

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Article
Publication date: 18 May 2010

Guangling “Dave” Liu, Rangan Gupta and Eric Schaling

This paper aims to develops an estimable hybrid model that combines the micro‐founded DSGE model with the flexibility of the atheoretical VAR model.

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Abstract

Purpose

This paper aims to develops an estimable hybrid model that combines the micro‐founded DSGE model with the flexibility of the atheoretical VAR model.

Design/methodology/approach

The model is estimated via the maximum likelihood technique based on quarterly data on real gross national product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out‐of‐sample forecasts from the hybrid model are then compared with the forecasts generated from the Classical and Bayesian variants of the VAR for the period 2001:1‐2005:4.

Findings

The results indicate that, in general, the estimated hybrid‐DSGE model outperforms the classical VAR, but not the Bayesian VARs in terms of out‐of‐sample forecasting performances.

Research limitations/implications

The model lacks nominal shocks and needs to be extended into a small open economy framework.

Practical implications

The paper was able to show that, even though the DSGE model is outperformed by the BVAR, a microfounded theoretical DSGE model has a future in forecasting the South African economy.

Originality/value

To the best of the authors' knowledge, this is the first attempt to use an estimable DSGE model to forecast the South African economy.

Details

Journal of Economic Studies, vol. 37 no. 2
Type: Research Article
ISSN: 0144-3585

Keywords

Content available
Book part
Publication date: 18 January 2022

Abstract

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

Book part
Publication date: 30 September 2021

Zhiming Long and Rémy Herrera

This study first calculates a profit rate for China’s economy over the period 1952–2014; the rate shows a downward trend in the long term but also exhibits cyclical…

Abstract

This study first calculates a profit rate for China’s economy over the period 1952–2014; the rate shows a downward trend in the long term but also exhibits cyclical fluctuations. Then, structural vector autoregressive models are used to examine the Chinese economic structure and, thanks to impulse response functions, the role of the profit rate in investment, capital accumulation, and GDP growth rates. Then, based on a priori constraints relative to this structure, the study tests whether these assumptions are verified over the period studied in the context of the transformations of China. The impulse response functions are further examined by using Bayesian analysis. Finally, the authors conclude that the period from 1952 to 2014 should be divided into several sub-periods with distinct structural characteristics.

Details

Imperialism and Transitions to Socialism
Type: Book
ISBN: 978-1-80043-705-0

Keywords

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