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Behavioral Expectations in New Keynesian DSGE Models: Evidence from India's COVID-19 Recovery and Vaccination Program

This paper extends the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework by incorporating behavioral expectations to analyze the moments of India's output gap and inflation rate, with a particular focus on the impacts of COVID-19 and vaccination programs. While DSGE models traditionally rely on rational expectations, we demonstrate that behavioral expectations more accurately capture the distributional characteristics of India's output gap and inflation rates data. Utilizing both Hodrick-Prescott and Kalman filters, we estimate the output gap and establish congruence with the moments of the simulated output gap. Concurrently, employing the initial negative demand shock values of the output gap, we calibrate the persistence parameters of negative aggregate demand (AD) and positive aggregate supply (AS) shocks, alongside the initial magnitude of the positive supply shock to achieve correspondence with post-COVID actual average values of the output gap and inflation rate. To ensure model precision, we implement Mahalanobis distance minimization for model calibration. Our findings elucidate that vaccination programs generated significant positive supply shocks, counterbalancing the prolonged negative demand shock precipitated by the COVID-19 pandemic. Moreover, the analysis reveals that the positive supply shocks' persistence parameter exceeds that of the negative demand shocks, indicating the exceptional efficacy of India's vaccination strategy. Furthermore, this research advances the scholarly contributions of Dasgupta and Rajeev (2023) by furnishing a quantitative DSGE framework that complements their static simple Keynesian analysis.

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