Research



This paper estimates an otherwise conventional Markov-switching DSGE model by incorporating asset prices in monetary policy rules, in order to identify the policy stance by the Federal Reserve toward fluctuations in asset price. Using the data from 1954:Q3 to 2009:Q2, I find that the monetary policy responded more actively to asset price movements as well as to inflation and output growth prior to the late 1960s and during the two latest stock market boom episodes of 1994-2000 and 2003-2007. Based on the monetary policy regime probability estimates, this paper establishes a systematic pattern between the monetary policy stance and macroeconomic uncertainty: as uncertainty soars, the monetary authority tends to take a less active stance in reacting to fluctuations in inflation, output, and stock prices. A series of counterfactual exercises have two implications on how macroeconomic performance is affected by the monetary policy regimes and rules. First, a counterfactual simulation assuming the active policy regime over the entire sample period yields gains in output and inflation prior to the mid-1980s, but losses in the variables for the post mid-1980s era. The losses are particularly pronounced for the periods of high macroeconomic uncertainty, indicating that the Fed's policy stance conditional on uncertainty helps improve the performance of the economy. Second, gains of responding to stock prices in terms of output and inflation are more evident in the 1983-2009 subsample than in the earlier sample period.



This paper studies the importance of information stickiness in accounting for business cycles, forecast errors, and disagreement about economic activity using a dynamic stochastic general equilibrium (DSGE) model that features information and nominal rigidities. Our main findings have five folds. First, the estimation reveals that inattentive consumers and workers turn out to be crucial modeling features in enhancing the goodness-of-fit of the model, while the contribution of inattentive firms is relatively limited. Regarding inflation dynamics, our finding highlights the importance of nominal price rigidity over information stickiness of firms. Second, the counterfactual experiment shows that the fluctuations in output become less volatile under the presence of sticky information. This tendency is more pronounced during recessions. Third, the model-implied disagreement about economic activity closely tracks various measures of disagreement and macroeconomic uncertainty. Our results indicate that rising disagreement is mainly driven by adverse shocks that are relatively larger than positive shocks and business cycles are characterized by gradual booms and sudden downturns. Fourth, sticky information models have significant explanatory power for the time-varying nature of the mean inflation forecast error from the Survey of Professional Forecasters. We demonstrate that the positive mean forecast errors of the 1970s, the prolonged negative errors in the 1980s and 1990s, and the systematic pattern of the inflation forecast errors in the 2000s are well rationalized by sticky information models. Finally, we find that sticky information plays a distinctive and crucial role in explaining U.S. data which cannot be completely substituted by noisy information.



Sticky price models associated with firms’ forward-looking price-setting behavior play a central role in macroeconomic modeling and monetary policy analysis. Rudd and Whelan (2006), however, reject the empirical relevance of the forward-looking behavior in accounting for inflation dynamics, based on expected future real marginal cost from a VAR model. This paper demonstrates that their results against sticky price models with substantial reliance on forward-looking behavior are contingent upon the forecasting models for expected future marginal cost. To this end, we employ a conventional DSGE model as an alternative forecasting model and find that the DSGE-based expected future marginal cost offers significant explanatory power for the dynamics of inflation. In addition, this paper makes the following two points that highlight the importance of forward-looking behavior. First, we demonstrate that sticky price models, with emphasis on the presence of forward-looking behavior, can generate the puzzling negative dependence of changes in inflation on its own lag documented by Rudd and Whelan (2006). Second, the DSGE model fails to replicate the observed dynamic cross-correlation between output gap and inflation, unless it is associated with both forward- and backward-looking price-setting behavior.



This paper studies the effects of future monetary policy shocks unanticipated by private agents using an estimated new Keynesian dynamic stochastic general equilibrium model framework. Analysis of U.S. data from 1967:Q1 to 2008:Q1 shows that the information structure on monetary policy substantially improves the model's fit to data compared to the conventional contemporaneous-shocks-only counterpart. To examine the role of agents' foresight about future monetary policy shocks, I conduct a counterfactual analysis on agents' information flows. If, throughout the sample period, agents had possessed perfect foresight about future monetary policy shocks, the business cycle fluctuations would have been milder as the volatility of key macroeconomic variables drops markedly. In addition, I find that the model-implied uncertainty about future monetary policy contains significant explanatory power for disagreement---cross-sectional dispersion of forecasts---in the Survey of Professional Forecasters.




We empirically assess how Korean economy has responded to structural shocks across different monetary policy regimes by employing a Markov-switching dynamic stochastic general equilibrium (MS-DSGE) model. Using the data from 1976 to 2013, we find that allowing for the regime-switching aspect both in monetary policy rules and shock volatilities is a crucial setup in improving the model's fit with Korean data. The regime estimates indicate that monetary policy has responded more aggressively to inflation, but less strongly to output, after launching the Inflation Targeting (IT) policy in the late 1990s. The identified regimes have three implications on macroeconomic performance in Korea. First, the introduction of the IT monetary policy has contributed to a sharp reduction in the level as well as the volatility of inflation in the 2000s. Second, technology shocks are the most important driver of output fluctuations in Korea as the major economic crises in Korea are mainly explained by adverse shocks on technology. Finally, it would have been possible to achieve higher output and lower inflation simultaneously if the IT monetary policy regime were maintained over the entire sample period.


6. "Dissecting the Effects of Terms of Trade Shocks on the Korean Economy" joint with Jinho Choi (Bank of Korea) and Manho Kang (Bank of Korea)

Micro- as well as macro-level analysis on the terms of trade for Korea are conducted. We demonstrate that the deteriorated terms of trade since the mid-1990s are largely attributable to declines in export prices of manufacturing goods and surges in energy import prices. A further investigation using a vector autoregressive model identified by sign restrictions on impulse responses suggests a harmonious view: the structural innovation that reduces export prices and increases import prices is the most significant driver of terms of trade fluctuations of Korea. Although the shock exacerbates terms of trade, it is clearly associated with an expansionary effect on output, which is more pronounced at longer horizons. This finding indicates that the terms of trade worsening since the mid-1990s may not be as detrimental to macroeconomic performance in Korea as is often assumed without being explicitly evaluated.


7. "No News is Good News" joint with Eric M. Leeper (IU), Giacomo Rondina (UCSD), and Todd B. Walker (IU) (in progress)

We estimate a standard dynamic stochastic general equilibrium model under three dfferent information structures to assess the importance of these informational assumptions. In the first information structure, agents receive news about future structural shocks, as in Beaudry and Portier (2006) and Schmitt-Grohé and Uribe (2012); in the second structure, agents observe noisy signals about current structural shocks; in the third structure, agents do not observe either news or noise. Data overwhelming support the noise-shock information structure. News (noise) shocks shift spectral power from the lower (higher) end to the higher (lower) end of the spectrum, which forces internal propagation mechanisms to work harder (less hard) in models with news (noise) shocks. That data prefer noise shocks and the reallocation of spectral power to the lower end connects to Granger's (1969) "typical spectral shape" of macroeconomic variables. As a byproduct, the paper develops a novel estimation methodology for models with incomplete information.




In this paper, I compare government spending multipliers emerged from two conventional modeling approaches---estimated DSGEs and structural VARs---for the U.S. economy over the period 1983-2008. Controlling for differences in data and statistical methods, this article demonstrates that the resulting impact output multipliers for the VARs are substantially greater than those of the estimated DSGEs. Among the DSGE model features, habit formation in consumption and non-savers can help reconcile the gap between BVAR- and DSGE-based multipliers. However, extremely high degrees of consumption habit and fraction non-savers are necessary to make the DSGE multipliers comparable to those of VARs. These empirical results are robust under three different identification schemes used in the VAR literature of fiscal policy analysis. All these findings suggest that employing the structural VAR method does not account for the relatively large multipliers observed in the BVAR specification. Setting up a specific DSGE model, however, imposes particular ranges on multipliers a priori.



Rational expectations imply that the macroeconomic responses to deficit-financed government spending critically hinge on economic agents' beliefs about how debt innovations are financed by fiscal instruments. This paper quantifies the government spending multipliers with consideration of the intertemporal aspect of government budget behavior and distortionary fiscal financing, using vector autoregressions. To this end, I establish a novel identification strategy of government spending shocks financed by future tax hikes. By applying the method to U.S. data from 1947 to 2007, I find that distorting fiscal financing substantially dampens the stimulus effects of government spending in both the short and medium runs. In addition, the identification strategy reveals that spending reversals---initial government spending increases financed by medium-run spending cuts---do not seem to happen in U.S. data.



I propose and apply a new approach to test the expectations hypothesis. In particular, I test the standard excess bond return regressions in continuous-time to examine the empirical evidence for the expectations hypothesis across an infinitesimal time period. For our model's estimates, I use the martingale regression based on time change for inference on continuous-time conditional mean models. This method is quite intuitive in that it identifies the true parameter value simply by imposing the martingale condition for the error process. I find results in favor of the expectations hypothesis if it is tested in a continuous-time setup. This finding suggests that the empirical evidence for the expectations hypothesis depends to a remarkable degree on the sampling frequency of the data.


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Joon Young Hur,
Jan 4, 2015, 9:41 AM
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Joon Young Hur,
Dec 5, 2014, 4:13 PM
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Joon Young Hur,
Jul 18, 2014, 8:26 PM
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Joon Young Hur,
Jul 18, 2014, 8:27 PM
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Joon Young Hur,
Jun 2, 2014, 9:35 PM
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Joon Young Hur,
Oct 1, 2014, 8:48 PM
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Joon Young Hur,
Nov 26, 2014, 3:50 PM
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Joon Young Hur,
Nov 26, 2014, 3:50 PM
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Joon Young Hur,
Jun 2, 2014, 9:32 PM
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Joon Young Hur,
Dec 28, 2014, 10:34 AM
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Joon Young Hur,
Jan 30, 2015, 10:33 AM
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Joon Young Hur,
Jan 26, 2015, 2:52 PM
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