Non-recursive Identification Research Articles

Efficacy of Monetary Policy Transmission During the Flexible Inflation Targeting Regime in India

This article builds a Structural Vector Autoregressive model and employs non-recursive identification restrictions to examine the effectiveness of the Monetary policy transmission mechanism in India during the Flexible Inflation Targeting regime (2016–2023). The results indicate that policy rate shocks have a significant negative impact on domestic output and prices during this regime. The findings further, reveal evidence of an exchange rate puzzle during the Flexible Inflation Targeting regime. Our results give credence to the RBI’s move towards a Flexible indicator targeting approach as all the macroeconomic variables of interest, that is, Domestic output and inflation produce plausible estimates in response to a monetary policy shock. JEL Classification: C32, E52, E5, E52, F41

Comparison Study between Open-Loop and Closed-Loop Identification for Industrial Hydraulics Actuator System

In control applications involving motion control, precise control of the Industrial Hydraulics Actuator (IHA) is necessary to accurately assess the position of the actuator rod. Non-recursive identification is used to model the system with an open-loop and closed-loop approach for this paper. The grey box approach is used to estimate the continuous model and parameter estimation for the system. The System Identification toolbox in MATLAB is used for the model estimation. The process starts by obtaining the input-output data from the experimental work. The input-output data validation outcome reveals a best-fit percentage for open-loop 88.91% and closed-loop 95.43%, as well as a small Root Mean Square Error (RMSE). This indicates a successful validation between the estimated model and the actual system due to a high degree of agreement. The open-loop and closed-loop identification have proven successful as there is no noteworthy disparity in the outcomes. The closed-loop system can be applied to unstable systems and any potential system.

Does risk matter more in recessions than in expansions? Implications for monetary policy

We employ a nonlinear vector autoregression and a non-recursive identification strategy to show that an equal-sized uncertainty shock generates a larger contraction in real activity when growth is low (as in recessions) than when growth is high (as in expansions). An estimated New Keynesian model with recursive preferences replicates these state-contingent responses when approximated to third order around its risky steady state due to a stronger upward nominal pricing bias in recessions than in expansions. Empirical evidence supports this state-contingent channel, and we show that it can greatly reduce the ability of systematic monetary policy to stabilize output during recessions.

Identification of a Class of Precision Motion Systems with Uncertain Hysteretic Nonlinearities

In this paper, we propose a nonrecursive identification algorithm for a class of precision motion systems with uncertain hysteresis nonlinearities. This class of precision motion systems can be modeled by a Hammerstein system with a hysteresis nonlinearity. The hysteresis nonlinearity can be symmetric, asymmetric, rate-independent, or rate-dependent. The proposed identification algorithm is a two-stage algorithm that identifies the hysteresis nonlinearity and the linear system using measurements of the input and output of the Hammerstein system. The first stage of the algorithm uses a delayed impulse signal with simple least squares to identify the linear part of the Hammerstein system. The delayed impulse signal is shown to be persistently exciting of a sufficient order for the considered model structure. The second stage of the algorithm uses a sinusoidal input and the identified linear model obtained from the first stage to construct an estimate of the unknown intermediate signal, which represents the output of the hysteresis nonlinearity. Finally, a model of the hysteresis nonlinearity is obtained using the sinusoidal input and the constructed estimate of the intermediate signal. The identification algorithm is demonstrated on an experimental setup that includes a piezoelectric actuator, which exhibits a hysteresis nonlinearity and badly damped dynamics.

Uncertainty across volatility regimes

SummaryWe propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty. This approach allows us to simultaneously address two major questions in the empirical literature: Is uncertainty a cause or effect of decline in economic activity? Does the relationship between uncertainty and economic activity change across macroeconomic regimes? Results based on a small‐scale vector autoregression with US monthly data suggest that (i) uncertainty is an exogenous source of decline of economic activity, and (ii) the effects of uncertainty shocks amplify in periods of economic and financial turmoil.

TIME-VARYING MACROECONOMIC IMPACTS OF GLOBAL ECONOMIC POLICY UNCERTAINTY TO A SMALL OPEN ECONOMY: EVIDENCE FROM INDONESIA

This paper studies macroeconomic impacts of global economic policy uncertainty shocks to a small open economy. To that end, I use monthly Indonesian data along with a measure of global economic policy uncertainty developed by Baker et al. (2016) and Davis (2016) and estimate a time-varying parameter Bayesian structural VAR with non-recursive identification using framework proposed by Canova and Pérez Forero (2015). I find that global economic policy uncertainty shocks lead to a reduction in prices, interest rate, and trade balance in all global events included in the estimation. The impact on output, however, largely varies across events. A surprise movement of global economic policy uncertainty triggers a contraction in output around the 2008 global financial crisis but, following the 2016 US presidential election, output reacts positively to the shock. Despite these notable variations in the responses of output, the proportion of the forecast error variance of output due to the shock is very small and decreases rapidly over time—which indicates that the shock presents an inconsequential effect to output. Nonetheless, the proportion of the forecast error variance of trade balance due to the shock is considerably higher than the forecast error variance of output and inflation. This further suggests that, via international trade, a global economic policy uncertainty shock could still pose harm for Indonesia.

GIMME A BREAK! IDENTIFICATION AND ESTIMATION OF THE MACROECONOMIC EFFECTS OF MONETARY POLICY SHOCKS IN THE UNITED STATES

We employ a non-recursive identification scheme to identify the effects of a monetary policy shock in a Structural Vector Autoregressive (SVAR) model for the US post-WWII quarterly data. The identification of the shock is achieved via heteroskedasticity, and different on-impact macroeconomic responses are allowed for (but not imposed) in each volatility regime. We show that the impulse responses obtained with the suggested non-recursive identification scheme are quite similar to those conditional on a recursive VAR estimated with pre-1984 data. In contrast, recursive vs. non-recursive identification schemes return different short-run responses of output and investment during the Great Moderation. Robustness checks dealing with a different definition of investment, an alternative break-point, and federal funds futures rates as an indicator of the monetary policy stance are documented and discussed.

Gimme a Break! Identification and Estimation of the Macroeconomic Effects of Monetary Policy Shocks in the U.S.

We employ a non-recursive identification scheme to identify the effects of a monetary policy shock in a Structural Vector Autoregressive (SVARs) model for the U.S. post-WWII quarterly data. The identification of the shock is achieved via heteroskedasticity, and different on-impact macroeconomic responses are allowed for (but not imposed) in each volatility regime. We show that the impulse responses obtained with the suggested non-recursive identification scheme are quite similar to those conditional on a recursive VAR estimated with pre-1984 data. In contrast, recursive vs. non-recursive identification schemes return different short-run responses of output and investment during the Great Moderation. Robustness checks dealing with a different definition of investment, an alternative breakpoint, and federal funds futures rates as an indicator of the monetary policy stance are documented and discussed.

Monetary Policy and Inflation in India: A Structural VAR Analysis

This paper provides a Vector Auto-Regression (VAR) analysis of inflation and monetary policy. It identifies monetary policy shocks in a benchmark recursive structure of VARs with two alternative inflation measures, viz., headline WPI and core inflation measure. Unlike the results in case of WPI, the response of core prices declined swiftly, following a positive call money interest rate shock. Thus, the interest rate channel is stronger for core prices. It is pertinent as RBI currently operates its monetary management through interest rate channel. The study checks robustness of results of the benchmark recursive model against alternative ordering and non-recursive identification schemes. Finally, a core price targeting scenario is generated in the benchmark VAR model with non-recursive identification scheme.

Monetary Policy and Inflation in India: A Structural VAR Analysis

This paper provides a Vector Auto-Regression (VAR) analysis of inflation and monetary policy. It identifies monetary policy shocks in a benchmark recursive structure of VARs with two alternative inflation measures, viz., headline WPI and core inflation measure. Unlike the results in case of WPI, the response of core prices declined swiftly, following a positive call money interest rate shock. Thus, the interest rate channel is stronger for core prices. It is pertinent as RBI currently operates its monetary management through interest rate channel. The study checks robustness of results of the benchmark recursive model against alternative ordering and non-recursive identification schemes. Finally, a core price targeting scenario is generated in the benchmark VAR model with non-recursive identification scheme.

Real exchange rates and target interest rates in a simple VAR model

This paper proposes a simple vector autoregressions (VAR) model with (real) output and exchange rate shocks on interest rates. Rather than assuming non-recursive identification schemes, we test the identifying assumption of the error term decompositions. Applying the model to quarterly data on major currencies against the U.S. dollar (USD) from 1974 to 1997, interest rate shocks explain - after 3 years - 16% of Canadian dollar/USD (CAD) real exchange rate variations and less than 2% for the mark/USD and yen/USD. Positive innovations of interest rates bring about (transitory) CAD real appreciations in differences and (permanent) appreciations in levels. Canadian real output is more explained by domestic interest rate shocks (19%) than Germany’s (5%) or Japan’s (0.2%). Canada is smaller than the other economies and CAD has been shown to suffer from “fear of floating”. Our findings support the proposition that domestic shocks dominate output variance under fixed exchange rates. They are also consistent with structural interpretations of the VAR.

Adaptive control for linear slowly time-varying systems using direct least-squares estimation

In this paper, we propose a least-squares-based non-recursive identification algorithm capable of dealing with slowly time-varying parameters. The minimized criterion is an L 2 norm of the identification error with forgetting factor. The proposed estimation algorithm does not require explicit knowledge on the noise bound or the region where the true parameters lie. Finally, a pole placement hybrid controller is designed and the system's state is shown to be bounded.

Robust adaptive identification of slowly time-varying parameters with bounded disturbances

In this paper, the robustness limitations of current recursive identification algorithms are highlighted. Then, it is shown how a particular non-recursive identification algorithm may improve the robustness to bounded disturbances and slowly time-varying parameters, at the expense of performing an on-line eigenvalue decomposition on a symmetric semidefinite positive matrix. Furthermore, contrary to “robustified” recursive identification algorithms, this algorithm does not require any a priori knowledge of a bound on the disturbances and of a bound on the unknown parameter values.