scholarly journals Balanced Growth Approach to Tracking Recessions

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 14
Author(s):  
Marta Boczoń ◽  
Jean-François Richard
Keyword(s):  
Autoregressive Process ◽  
Vector Autoregressive ◽  
Balanced Growth ◽  
Economic Agents ◽  
Us Economy ◽  
Forecasting Performance ◽  
The Us ◽  
Vector Autoregressive Process ◽  
Dynamic Stochastic ◽  
Business Cycle Model

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.

scholarly journals Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model

2015 ◽  
Vol 19 (2) ◽  
Author(s):  
Stelios Bekiros ◽  
Alessia Paccagnini
Keyword(s):  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dsge Model ◽  
Time Data ◽  
Vector Autoregressive ◽  
Us Economy ◽  
The Us ◽  
Dynamic Stochastic ◽  
Statistical Identification ◽  
Density Forecasts

AbstractAlthough policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.

Some ARMA models for dependent sequences of poisson counts

1988 ◽  
Vol 20 (4) ◽  
pp. 822-835 ◽  
Author(s):  
Ed Mckenzie
Keyword(s):  
Asymptotic Behaviour ◽  
Discrete Time ◽  
Joint Distribution ◽  
Autoregressive Process ◽  
Two Dimensional ◽  
Arma Models ◽  
Time Reversibility ◽  
Vector Autoregressive ◽  
Arma Processes ◽  
Vector Autoregressive Process

A family of models for discrete-time processes with Poisson marginal distributions is developed and investigated. They have the same correlation structure as the linear ARMA processes. The joint distribution of n consecutive observations in such a process is derived and its properties discussed. In particular, time-reversibility and asymptotic behaviour are considered in detail. A vector autoregressive process is constructed and the behaviour of its components, which are Poisson ARMA processes, is considered. In particular, the two-dimensional case is discussed in detail.

Modelling sectoral spillovers in the USA (1992–2015)

2020 ◽  
Vol 47 (3) ◽  
pp. 561-595
Author(s):  
Konstantinos N. Konstantakis ◽  
Panayotis G. Michaelides ◽  
Theofanis Papageorgiou ◽  
Theodoros Daglis
Keyword(s):  
Information Technology ◽  
General Equilibrium ◽  
Methodological Approach ◽  
Robustness Analysis ◽  
Autoregressive Process ◽  
Spillover Effects ◽  
Content Type ◽  
Us Economy ◽  
The Us ◽  
The Impact

PurposeThis research paper uses a novel methodological approach to investigate the spillover effects among the key sectors of the US economy.Design/methodology/approachThe paper links the US sectors via a node theoretic scheme based on a general equilibrium framework, whereas it estimates the general equilibrium equation as a Global Vector Autoregressive process, taking into consideration the potential existence of dominant units.FindingsBased on our findings, the dominant sector in the US economy, for the period 1992–2015, is the sector of information technology, finance and communications, a fact that gives credence to the view that the US economy is a service-driven economy. In addition, the US economy seems to benefit by the increased labour mobility across knowledge-intensive sectors, thus avoiding the ‘employment trap’ which in turn enabled the US economy to overcome the financial crisis of 2007.Originality/valueFirstly, the paper models by means of a network approach which is based on a general equilibrium framework, the linkages between the US sectors while treating the sector of information, technology, communications and finance as dominant, as dictated by its degree of centrality in the network structure. Secondly, the paper offers a robustness analysis regarding both the existence and the identification of dominant sectors (nodes) in the US economy. Thirdly, the paper studies a wide period, namely 1992–2015, fully capturing the recent global recession, while acknowledging the impact of the global crisis through the introduction of the relevant exogenous dummy variables; Lastly and most importantly, it is the first study to apply the GVAR approach in a network general equilibrium framework at the sectoral level.

scholarly journals Justification of Wold’s Theorem and the Unbiasedness of a Stable Vector Autoregressive Time Series Model Forecasts

2017 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Iberedem A. Iwok
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Process ◽  
Time Series Model ◽  
Average Process ◽  
Moving Average Process ◽  
Autoregressive Time Series ◽  
Vector Autoregressive ◽  
Vector Autoregressive Process ◽  
Vector Time Series

In this work, the multivariate analogue to the univariate Wold’s theorem for a purely non-deterministic stable vector time series process was presented and justified using the method of undetermined coefficients. By this method, a finite vector autoregressive process of order  [] was represented as an infinite vector moving average () process which was found to be the same as the Wold’s representation. Thus, obtaining the properties of a  process is equivalent to obtaining the properties of an infinite  process. The proof of the unbiasedness of forecasts followed immediately based on the fact that a stable VAR process can be represented as an infinite VEMA process.

IDENTIFICATION AND DICHOTOMIZATION OF LONG- AND SHORT-RUN RELATIONS OF COINTEGRATED VECTOR AUTOREGRESSIVE MODELS

2001 ◽  
Vol 17 (5) ◽  
pp. 889-912 ◽  
Author(s):  
Cheng Hsiao
Keyword(s):  
Prior Information ◽  
Autoregressive Process ◽  
Rank Condition ◽  
Vector Autoregressive ◽  
Long Run ◽  
Vector Autoregressive Process ◽  
Short Run ◽  
Cowles Commission ◽  
Necessary And Sufficient ◽  
The Relationship

We show that the usual rank condition is necessary and sufficient to identify a vector autoregressive process whether the variables are I(0) or I(d) for d = 1,2,.... We then use this rank condition to demonstrate the interdependence between the identification of short-run and long-run relations of cointegrated process. We find that both the short-run and long-run relations can be identified without the existence of prior information to identify either relation. But if there exists a set of prior restrictions to identify the short-run relation, then this same set of restrictions is sufficient to identify the corresponding long-run relation. On the other hand, it is in general not possible to identify the long-run relations without information on the complete structure. The relationship between the identification of a vector autoregressive process and a Cowles Commission dynamic simultaneous equations model is also clarified.

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