Recursive Identification Scheme Research Articles

The Macroeconomic Impact of Fiscal Policy Shocks: What do the Indian Data Say?

This article examines the impact of fiscal policy shocks on a set of macroeconomic variables in India, using a seasonally adjusted quarterly dataset for the period 2003–2019. The Gregory-Hansen residual-based test for cointegration and the vector error correction model (VECM) are applied, and impulse responses based on a recursive identification scheme (Cholesky decomposition) is used to assess the effects of a gross tax revenue shock and a government spending shock on economic activity, while accounting for the possibility of structural breaks. The findings indicate that an unexpected fiscal policy shock has an immediate expansionary impact on real GDP and a sudden positive effect on inflation (WPI) and the repo rate; and that the impact is permanent and away from the zero equilibrium. The careful design of policies by the central bank and the government as crucial macroeconomic stabilisation tools is imperative. JEL Classification: E6, H20, H30

Recursive least squares identification of heat exchanger system using block-structured models

This study provides a recursive parametric identification scheme for a liquid-saturated steam heat exchanger system. The recursive identification scheme uses block-structured Wiener and Hammerstein models as model structure and recursive least squares estimation scheme as the parameter estimation method. The estimated block-oriented model provides higher accuracy of estimation than linear models provided in the literature. From the simulation results, it is observed that the Wiener model can provide 88% goodness-of-FIT, whereas Hammerstein model can provide 96% goodness-of-FIT using the said technique.

Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks

Recent structural VAR studies of the monetary transmission mechanism have voiced concerns about the use of recursive identification schemes based on short-run exclusion restrictions. This paper characterizes the effects on impulse propagation of informational constraints embodying classical Cholesky-type timing restrictions in otherwise standard DSGE models. We formally show that timing restrictions can produce non-trivial moving average components of rational expectations solutions, or even serve as an independent source of model-based nonfundamentalness, thereby hampering impulse response analysis via VAR procedures. We then derive population conditions for existence of VAR representations of DSGE economies exhibiting timing restrictions, and numerically explore their bearing on shock identification in a range of monetary models of the business cycle. Our analysis reveals that dynamic New Keynesian models admit invertible equilibrium representations as well as fast-converging VAR coefficient matrices under empirically tenable parameterizations. This alleviates concerns about identification and lag truncation bias: low-order Cholesky-VARs do well at retrieving the true aggregate effects of monetary policy shocks in a Cholesky world.

Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation*

AbstractThis paper provides insights into the time‐varying dynamics of the German business cycle over the last five decades. To do so, I employ an open‐economy time‐varying parameter VAR with stochastic volatility, which I estimate by quasi‐Bayesian techniques. The reduced‐form analysis reveals substantial shifts in the variables’ long‐run growth rates and shock volatilities over time. German trend inflation has strongly decreased and settled at a historically low level. GDP growth volatility exhibits marked fluctuations over time and has dropped to historically low levels only after the global financial crisis. The structural analysis employs externally identified oil supply shocks along with a recursive identification scheme to identify key macroeconomic shocks. The analysis reveals strong fluctuations in both the impact responses of macroeconomic aggregates to these shocks and the shock propagation processes. Thus, I conclude that business cycle stabilization in Germany is driven by both good policy and good luck.

Adaptive decentralized finite‐time tracking control for uncertain interconnected nonlinear systems with input quantization

AbstractCompared with the previous references, very few results are available to study control scheme directly for interconnected nonlinear systems (INSs) with strongly interconnected terms described by unknown state‐dependent‐nonlinear functions and input quantization in the finite‐time control literature. In this article, an adaptive decentralized finite‐time tracking control (ADFTTC) algorithm is developed to address the system output tracking problem for a class of INSs with input quantization and strongly interconnected terms. To reduce the computation complexity, the neural network technique is employed to reconstruct the derivative of virtual control laws in each step. By employing adding a power integrator technique, an adaptive decentralized finite‐time tracking controller is constructed, which guarantees all signals in the closed‐loop systems converge to a neighborhood of the origin. Moreover, the developed controller avoids the “singularity” and “explosion of complexity” problems in the recursive design scheme. An example of an inverted pendulum confirms the ADFTTC scheme.

Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks

Recent structural VAR studies of the monetary transmission mechanism have voiced concerns about the use of recursive identification schemes based on short-run exclusion restrictions. We trace out the effects on impulse propagation of informational constraints embodying classical Cholesky-timing restrictions in otherwise standard Dynamic New Keynesian (DNK) models. By reinforcing internal propagation mechanisms and enlarging a model's equilibrium state space, timing restrictions may produce a non-trivial moving average component of the equilibrium representation, making finite order VARs a poor approximation of true adjustment paths to monetary impulses, albeit correctly identified. They can even serve as an independent source of model-based nonfundamentalness, thereby hampering shock identification via VAR methods. This notwithstanding, restricted DNK models are shown to feature (i) invertible equilibrium representations for the observables and (ii) fast-converging VAR coefficient matrices under empirically tenable parameterizations. This alleviates concerns about identification and lag truncation bias: low-order Cholesky-VARs do well at uncovering the transmission of monetary impulses in a truly Cholesky world.

Time-Varying Dynamics of the German Business Cycle: A Comprehensive Investigation

This paper provides insights into the time-varying dynamics of the German business cycle over the last five decades. To do so, I employ an open-economy time-varying parameter VAR with stochastic volatility, which I estimate by quasi-Bayesian techniques. The reduced-form analysis reveals substantial shifts in the variables’ long-run growth rates and shock volatilities over time. German trend inflation has strongly decreased and settled at a historically low level. GDP growth volatility exhibits marked fluctuations over time and has dropped to historically low levels only after the global financial crisis. The structural analysis employs externally identified oil supply shocks along with a recursive identification scheme to identify key macroeconomic shocks. The analysis reveals strong fluctuations in both the impact responses of macroeconomic aggregates to these shocks and the shock propagation processes. Thus, I conclude that business cycle stabilization in Germany is driven by both good policy and good luck.

Adaptive Practical Fast Finite-Time Consensus Protocols for High-Order Nonlinear Multi-Agent Systems With Full State Constraints

This paper mainly addresses the issue of fast finite-time consensus (FTC) algorithms for a group of high-order uncertain nonlinear non-strict-feedback Multi-agent Systems (MASs) with time-varying asymmetric full state constraints. In order to guarantee the state constraints are satisfied, an appropriate asymmetric nonlinear mapping (NM) is employed to transform the original full state constrained system into a corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory and backstepping recursive design scheme, the uncertainties and non-strict-feedback form are considered to propose a distributed adaptive FTC protocol which can guarantee tracking error reaching a region in finite time. Finally, an example is given to show that the designed control law can perform effective control.

The effect of government spending innovations on the Ethiopian economy

PurposeThe purpose of this paper is to estimate the size of government spending components’ multipliers for the Ethiopian economy over the sample period of 2001Q1 up to 2017Q4.Design/methodology/approachThe effects of government spending are analyzed by applying short-run contemporaneous restrictions for the identification of shocks in an SVAR in order to estimate multipliers for the small open economy. Accordingly, recursive identification scheme is used in this study.FindingsFrom the impulse response functions, the authors found that aggregate government spending is less effective in stimulating the economy for the study period as evidenced by almost zero multipliers. This can be due to many structural and conjunctural factors that tend to lower the multiplier effects. At a disaggregate level, real GDP responds negatively to capital spending while its effect on recurrent spending is positive and insignificant on impact. The variation to real GDP is best explained by the variation in capital spending as compared to recurrent spending.Originality/valueThough almost none in number, little research has been conducted in Ethiopia related to the effect of government spending shock on output. But this research deviates from the previous study by introducing a new methodology which is SVAR with cholesky decomposition. The previous study, however, used Bayesian VAR. Besides to that, using cholesky identification scheme, government spending is decomposed in to recurrent and capital spending to see the effect of government spending components on output and government spending multipliers are also computed both at an aggregate and disaggregate level.

Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics

In area of control, model-based robust identification is rare, and studies in presence of unknown noise statistics are especially seldom. The robust estimation problem for time-varying Wiener output-error systems is considered in this paper. An adaptive filtering-based recursive identification scheme is proposed to distinguish nonlinear time-varying characteristics in complex noise environments. Firstly, a virtual equivalent state space model is constructed to achieve adaptive Kalman filtering. In filter design, a weighted noise estimator based on Sage-Husa principle is introduced, and is sensitive to noise changes. Secondly, the state estimates obtained by filters are used to form the unknown intermediate variables in information vectors. Then, a recursive estimation method based on multiple iterations is developed, and the convergence of identification is confirmed by martingale hyperconvergence theorem. Finally, the numerical simulation results verify the theoretical findings.

Entrepreneurshp at the labour market: a case of precariat and informal employment

The concept of precariat is becoming the increasingly important subject of interdisciplinary research that involves both qualitative and quantitative approaches. The formation of the precariat is associated with the neoliberal reforms carried out in the last decades, as well as significant institutional and technological changes. The process of precarisation is accompanied by an increase in insecurity, instability, and flexibility, which is most characteristic of informal and precarious work. Our paper analyzes the relationship between informal employment as a proxy precariat and unemployment rates, industrial production index and real wages using the structural VAR approach for monthly data in the period 2010-2018. The relationship between the variables is checked by imposing restrictions on the recursive scheme (Cholesky identification) as well as the recursive identification scheme (Cholesky decomposition) in the SVAR model. Our analysis of the impulse response caused by the positive shock of informal employment confirmed that in the short term, the increase in the share of people employed in the informal sector causes, above all, a decline in real wages. There are weak responses from the industrial production index, which leads to the conclusion about the leveling effect of institutions and institutions. In addition, the paper employs the process of modeling the development of informal employment using the ARIMA model. Our results showing the trends in the development of informal employment demonstrate the existence of cyclical resilience which negatively affects economic development and requires structural reforms.

Which oil shocks really matter in equity markets?

This paper examines the relationship between structural oil shocks and US equity markets. The recent oil shock decomposition of Ready (2018) is reconsidered and refined, providing a clearer delineation between shocks to equity market discount rates and aggregate demand, leading to an oil shock specification which attributes substantially more explanatory power to the latter in explaining equity market variation. Providing links with the literature dating back to Kilian and Park (2009), an explicit role is given to precautionary demand shocks using an independent measure constructed from oil futures data, reducing the role of the supply shocks obtained as the final residual in the recursive identification scheme. In an extended sample that allows an analysis of the oil/equity market relationship since the global financial crisis, the modified aggregate demand shocks have approximately twice as much explanatory power for stock return variation than the demand shocks of Ready (2018). The importance of these shocks in driving oil price changes and equity market volatility has only increased since the financial crisis, with the role of supply shocks diminishing. Once these demand effects are accounted for, there is little relationship between precautionary demand shocks and equity returns, in contrast to the existing literature.

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

SummaryThis paper investigates the problem of adaptive output‐feedback neural network (NN) control for a class of switched pure‐feedback uncertain nonlinear systems. A switched observer is first constructed to estimate the unmeasurable states. Next, with the help of an NN to approximate the unknown nonlinear terms, a switched small‐gain technique‐based adaptive output‐feedback NN control scheme is developed by exploiting the backstepping recursive design scheme, input‐to‐state stability analysis, the common Lyapunov function method, and the average dwell time (ADT) method. In the recursive design, the difficulty of constructing an overall Lyapunov function for the switched closed‐loop system is dealt with by decomposing the switched closed‐loop system into two interconnected switched systems and constructing two Lyapunov functions for two interconnected switched systems, respectively. The proposed controllers for individual subsystems guarantee that all signals in the closed‐loop system are semiglobally, uniformly, and ultimately bounded under a class of switching signals with ADT, and finally, two examples illustrate the effectiveness of theoretical results, which include a switched RLC circuit system.

Adaptive Fuzzy Fault-Tolerant Control for Uncertain Nonlinear Switched Stochastic Systems with Time-Varying Output Constraints

Adaptive fuzzy fault-tolerant control problem for a class of uncertain switched stochastic nonlinear systems with time-varying asymmetric output constraints is addressed in this study. Under the action of well-designed asymmetric nonlinear mapping, fuzzy control technology, and backstepping recursive design scheme; the actuator faults of both loss of effectiveness and lock-in-place are considered to develop the adaptive fuzzy controller. The boundedness of all signals as well as the convergence of the output tracking error of the closed-loop plant to an arbitrary small neighborhood about zero are guaranteed by the developed fuzzy adaptive control strategy, and the time-varying output constraints are not violated. A simulation example is worked out to demonstrate the validity of the proposed control scheme.

Generalized convergence conditions of the parameter adaptation algorithm in discrete-time recursive identification and adaptive control

In this paper, we extend convergence conditions for the parameter adaptation algorithm, used in discrete-time recursive identification schemes, or in adaptive control. Whereas the classical stability analysis of this algorithm consists in checking the strictly real positiveness of an associated transfer function, we demonstrate that convergence can be obtained even when this condition is not fulfilled, under some assumptions on the algorithm forgetting factors. These results regarding both deterministic and stochastic contexts are obtained by analyzing convergence with a prescribed degree of stability.

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach

Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA). A kernel ridge regression functional series TARMA (FS-TARMA) recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.

The Panel VAR Approach to Modelling the Housing Wealth Effect: Evidence from selected European post-transition economies

Abstract Following Friedman’s permanent income hypothesis and Ando and Modigliani’s lifecycle hypothesis, this paper empirically studies the role of house prices and income in determining the dynamic behaviour of consumption in selected European post-transition economies using the panel vector autoregression (PVAR) approach and quarterly data covering the period from the first quarter of 2002 until the second quarter of 2012. With the shocks being recognized using the customary recursive identification scheme, we found that the response of personal consumption to the housing wealth shock is initially positive, but short lived.

Macroeconomic Effects of Oil Price Fluctuations in Colombia

This research aims to study the effects of oil price changes on the Colombian economy during 2001:Q1 to 2016:Q2. A structural vector auto-regression model in the spirit of Blanchard and Gali (2010) is estimated under a recursive identification scheme, where unexpected oil price variations are exogenous relative to the contemporaneous values of the remaining variables. Drawing on impulse-response estimates, a 10% increase in the oil price generates the following accumulated orthogonalized responses: i) a contemporaneous 0.4% increase in GDP growth, later on the effect reaches its maximum in the first quarter (1.7% increase) and starts to decay after two quarters; ii) a contemporaneous 1.2% decrease in unemployment, then the effect remains slightly negative and reaches its maximum after ten quarters (5.1% decrease); iii) a contemporaneous 0.9% decrease in inflation, followed by an 0.2% increase by quarter three, and thereafter the effect remains slightly negative.

Adaptive fuzzy output-feedback dynamic surface control of MIMO switched nonlinear systems with unknown gain signs

This paper investigates the problem of adaptive fuzzy tracking control for a class of multi-input–multi-output (MIMO) switched uncertain nonlinear systems with unknown gain signs and unmeasurable states. Fuzzy logic systems are used to approximate the unknown nonlinear functions, a fuzzy MIMO switched observer is designed to estimate the unmeasurable states. The Nussbaum-type functions are utilized to handle the unknown gain signs of the system under study. A switched-dynamic-surface-based adaptive fuzzy control approach is then established by exploiting the average dwell time method and backstepping and the dynamic surface control technique, which constructs multiple switched first-order filters to overcome the multiple “explosion of complexity” problem when applying the backstepping recursive design scheme. Also, the proposed approach extends the classical dynamic surface control technique from its original non-switched nonlinear version to a switched nonlinear version. It is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately boundedness under a class of switching signals with average dwell time, and the tracking errors converge to a small neighborhood of the origin. A mass–spring–damper system with controller switching as a practical example is provided to demonstrate the effectiveness of the proposed design method.