Estimating nonlinear dynamic equilibrium models by matching impulse responses

Ruge-Murciá, Francisco J.

doi:10.1016/j.econlet.2020.109624

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The LP model has many advantages over the traditional SVAR approach, including the following: LP models are easier to estimate because they rely merely on simple linear regressions; t-point or joint-wise inference is easily conducted; the LP method is more robust to misspecifications, and does not suffer from the curse of dimensionality inherent in VARs; they easily accommodate experimentation with highly nonlinear and flexible specifications that may be impractical in a multivariate context (Jordà 2005 ) (also see Adämmer 2019 ; Barnichon and Brownlees 2019 ; Montes-Rojas 2019 ); LPs are appealing because they are flexible and enable nonlinear and state-dependent impulse responses that are easy to compute (Ruge-Murcia 2020 ); and LPIRFs allow us to distinguish the responses of trading volume to return shocks in different return regimes. Thus, the results may provide richer insights into the case of investor behavior and may allow more realistic policy suggestions considering the effect of the existing regime.…”

Section: Data and Empirical Methodologymentioning

confidence: 99%

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Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors’ overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock markets. To the best of our knowledge, this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude. We examine whether investors in an emerging stock market (Borsa Istanbul) exhibit overconfidence behavior using a feed-forward, neural network, nonlinear Granger causality test and nonlinear impulse-response functions based on local projections. These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional, multivariate time series. The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature, which is the key contribution of the study. The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon. Overconfidence is more persistent in the low- than in the high-return regime. In the negative interest-rate period, a high-return regime induces overconfidence behavior, whereas in the positive interest-rate period, a low-return regime induces overconfidence behavior. Based on the empirical findings, investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies, particularly in low-return regimes.

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Section: Data and Empirical Methodologymentioning

confidence: 99%

“…LPs are appealing because they are flexible and enable nonlinear and state-dependent impulse responses that are easy to compute (Ruge-Murcia 2020 ); and…”

Section: Data and Empirical Methodologymentioning

confidence: 99%

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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“…The closest approach to estimating a nonlinear DSGE framework is probably the one by Ruge-Murcia (2014). He estimates a small-scale third-order approximated DSGE model with an impulse-response matching procedure based on a class of nonlinear VARs as auxiliary models for the purpose of indirect inference via a classical minimum distance estimator.…”

Section: Related Literaturementioning

confidence: 99%

Uncertainty and Monetary Policy During the Great Recession

Pellegrino

Castelnuovo

Caggiano

2022

Int Economic Review

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We employ a nonlinear VAR framework and a state-of-the-art identi…cation strategy to document the large response of real activity to a …nancial uncertainty shock during and in the aftermath of the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We then use the estimated framework to quantify the output loss due to the large uncertainty shock that materialized in 2008Q3. We …nd such a shock to be able to explain about 60% of the output loss in the 2008-2014 period. The same estimated model unveils the role successfully played by the Federal Reserve in limiting the output loss that would otherwise have occurred had monetary policy been conducted as in normal times. Finally, we show that the rule estimated during the great recession is able to deliver an economic outcome closer to the ‡exible price one than the rule describing the Federal Reserve's conduct in normal times.

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Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentiment

Polyzos

2023

Research in International Business and Finance

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Estimating nonlinear dynamic equilibrium models by matching impulse responses

Cited by 4 publications

References 23 publications

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Uncertainty and Monetary Policy During the Great Recession

Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentiment

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