DSGE modeling of business cycle properties of Czech labor market

• Název práce v češtině: DSGE modeling of business cycle properties of Czech labor market
• Název v anglickém jazyce: DSGE modeling of business cycle properties of Czech labor market
• Klíčová slova: unemployment, endogenous wages, alternating offer bargaining, business cycles, small open economy DSGE model, Bayesian estimation
• Klíčová slova anglicky: unemployment, endogenous wages, alternating offer bargaining, business cycles, small open economy DSGE model, Bayesian estimation
• Akademický rok vypsání: 2013/2014
• Typ práce: diplomová práce
• Jazyk práce: angličtina
• Ústav: Institut ekonomických studií (23-IES)
• Vedoucí / školitel: Ing. Aleš Maršál, Ph.D.
• Řešitel: skrytý - zadáno vedoucím/školitelem
• Datum přihlášení: 19.06.2014
• Datum zadání: 19.06.2014
• Datum a čas obhajoby: 23.06.2016 11:00
• Místo konání obhajoby: IES
• Datum odevzdání elektronické podoby: 11.05.2016
• Datum proběhlé obhajoby: 23.06.2016
• Oponenti: Mgr. Lukáš Rečka, Ph.D.

Předběžná náplň práce

Motivation:

Fluctuations of unemployment are difficult to capture by macroeconomic modeling. New Keynesian models are quite successful in this field mainly due to the assumptions of nominal rigidities and demand-determined employment. Nominal rigidities are crucial modeling features explaining business cycle properties of unemployment and limiting its sharp responses.

This approach is subject to at least four points of criticism. Firstly, the wage inertia (dependence of current wages on its past values) is not explained in these models, it is just assumed. Second, the wages imposed by the model, would be never chosen in practice by the agents. Also, this class of models does not employ the fact that wages are often constant for extended periods of time and wages in these models are always indexed to technology growth and inflation. Then there are further policy issues such as influence of unemployment benefits, which these models do not assess (Christiano et al., 2013).

In this thesis, we won’t assume that wages are exogenously subjected to nominal rigidities and will instead derive the wage inertia as an outcome of equilibrium of a small open economy DSGE model for the Czech Republic. I will show that this model accounts for key business cycle properties of Czech labor market.

Hypotheses:

Hypothesis #1: Employment frictions substantially changes the model dynamics and improves the forecasting properties of the model.
Hypothesis #2: Equilibrium solution of period-by-period bargaining problem has quantitatively comparable implications to exogenous frictions.
Hypothesis #3: A relatively small increase of real wage induces an increase of demand for output and consequently an increase of consumption and employment.

Methodology:

We follow the standard New Keynesian set-up with price stickiness introduced via Calvo pricing, but in contrast to the leading empirical models, we introduce labor market frictions into the model by employing an alternating offer bargaining model proposed by Rubinstein (1982), so that wages are not subject to exogenous nominal rigidities and instead depend on negotiations between workers and firms (Christiano et al., 2013).

We find the equilibrium of the economy and linearize the system. I will calibrate the deep parameters and observable quantities will be estimated. I will calculate the posterior density using Bayesian formula and the likelihood function for the model which is solved by the Kalman formula (Kalman, 1960). The sequence of posterior draws is obtained using Markov chain Monte Carlo method (Neal, 1993) or a random walk Metropolis–Hasting chain.

Expected Contribution:

I will calibrate and then estimate DSGE model of a small open economy using Bayesian estimation methods and data for the Czech Republic. I will then show response of Czech labor market to technological and monetary shocks and assess the forecasting performance of the model for a medium horizon. This work might give grounds to further discussion of incorporating labor market frictions into the DSGE framework endogenously.

Outline:

1. Literature survey: Introduction of the micro-founded optimization-based modeling and the solution framework of the DSGE models.
2. Model: I will introduce agents and their decision problems, derive equilibrium of the model.
3. Solution, Bayesian estimation: I will calibrate the model and then estimate it.
4. Analysis: I will present estimates of the model, calculate impulse responses to structural and nonstructural shocks and discuss its forecasting performance.
5. Concluding remarks: Brief summarization of my findings, their implications and discussion of possible extensions of the model.

Core Bibliography:

Adolfson, M., Laséen, S., Lindé, J., Villani, M., 2007. Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, Elsevier, vol. 72(2), 481-511.

An, S., Schorfheide, F., 2007a. Bayesian analysis of DSGE models. Econometric Reviews, 26(2-4), 113-172.

Canova, F., 2007. Methods for Applied Macroeconomic Research. Princeton University Press, New Jersey.

Christiano, L., J., Trabandt, M. S., Eichenbaum, 2013. Unemployment and business cycles. NBER Working Paper No. 19265

Christiano, L., J., Trabandt, M. S., Walentin, K., 2011. Introducing financial frictions and umeployment into a small open economy model. Working Paper Series 214, Sveriges Riksbank

DeJong, D., Dave, C., 2007. Structural Macroeconometrics. Princeton University Press, New Jersey.

Del Negro, M., Schorfheide, F., 2012. DSGE model-based forecasting. Staff Reports 554, Federal Reserve Bank of New York.

Guerrón-Quintana, P., Nason, J., 2012. Bayesian estimation of DSGE models. Working Papers 12-4, Federal Reserve Bank of Philadelphia.

Kalman, R., 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82 (1), 35–45.

Kydland, F., Prescott, E., 1977. Rules Rather Than Discretion: The Inconsistency of Optimal Plans. Journal of Political Economy 85 (3), 473–491.

Kydland, F., Prescott, E., 1982. Time to Build and Aggregate Fluctuations. Econometrica, Econometric Society, vol. 50(6), 1345-1370.

Lucas, R., 1976. Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), 19-46.

Musil, K., Vašíček, O., 2006. Behavior of the Czech Economy: New Open Economy Macroeconomics DSGE Model. Brno: CVKSČE MU, WP č. 23/2006.

Neal, R., 1993. Probabilistic Inference Using Markov Chain Monte Carlo Methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto.

Roger, S., Vlcek, J., 2012. Macrofinancial Modeling At Central Banks. IMF Working Papers 12/21.

Schorfheide, F., 2011. Estimation and Evaluation of DSGE Models: Progress and Challenges. NBER Working Papers 16781.

Sorolla, V., Raurich. X., 2014. Growth, Unemployment and Wage Inertia. E14/309 UB Economics Working Papers

Předběžná náplň práce v anglickém jazyce

Motivation:

Fluctuations of unemployment are difficult to capture by macroeconomic modeling. New Keynesian models are quite successful in this field mainly due to the assumptions of nominal rigidities and demand-determined employment. Nominal rigidities are crucial modeling features explaining business cycle properties of unemployment and limiting its sharp responses.

This approach is subject to at least four points of criticism. Firstly, the wage inertia (dependence of current wages on its past values) is not explained in these models, it is just assumed. Second, the wages imposed by the model, would be never chosen in practice by the agents. Also, this class of models does not employ the fact that wages are often constant for extended periods of time and wages in these models are always indexed to technology growth and inflation. Then there are further policy issues such as influence of unemployment benefits, which these models do not assess (Christiano et al., 2013).

In this thesis, we won’t assume that wages are exogenously subjected to nominal rigidities and will instead derive the wage inertia as an outcome of equilibrium of a small open economy DSGE model for the Czech Republic. I will show that his model accounts for key business cycle properties of Czech labor market.

Hypotheses:

Hypothesis #1: Employment frictions substantially changes the model dynamics and improves the forecasting properties of the model.
Hypothesis #2: Equilibrium solution of period-by-period bargaining problem has quantitatively comparable implications to exogenous frictions.
Hypothesis #3: A relatively small increase of real wage induces an increase of demand for output and consequently an increase of consumption and employment.

Methodology:

We follow the standard New Keynesian set-up with price stickiness introduced via Calvo pricing, but in contrast to the leading empirical models, we introduce labor market frictions into the model by employing an alternating offer bargaining model proposed by Rubinstein (1982), so that wages are not subject to exogenous nominal rigidities and instead depend on negotiations between workers and firms (Christiano et al., 2013).

We find the equilibrium of the economy and linearize the system. I will calibrate the deep parameters and observable quantities will be estimated. I will calculate the posterior density using Bayesian formula and the likelihood function for the model which is solved by the Kalman formula (Kalman, 1960). The sequence of posterior draws is obtained using Markov chain Monte Carlo method (Neal, 1993) or a random walk Metropolis–Hasting chain.

Expected Contribution:

I will calibrate and then estimate DSGE model of a small open economy using Bayesian estimation methods and data for the Czech Republic. I will then show response of Czech labor market to technological and monetary shocks and assess the forecasting performance of the model for a medium horizon. This work might give grounds to further discussion of incorporating labor market frictions into the DSGE framework endogenously.

Outline:

1. Literature survey: Introduction of the micro-founded optimization-based modeling and the solution framework of the DSGE models.
2. Model: I will introduce agents and their decision problems, derive equilibrium of the model.
3. Solution, Bayesian estimation: I will calibrate the model and then estimate it.
4. Analysis: I will present estimates of the model, calculate impulse responses to structural and nonstructural shocks and discuss its forecasting performance.
5. Concluding remarks: Brief summarization of my findings, their implications and discussion of possible extensions of the model.

Core Bibliography:

Adolfson, M., Laséen, S., Lindé, J., Villani, M., 2007. Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, Elsevier, vol. 72(2), 481-511.

An, S., Schorfheide, F., 2007a. Bayesian analysis of DSGE models. Econometric Reviews, 26(2-4), 113-172.

Canova, F., 2007. Methods for Applied Macroeconomic Research. Princeton University Press, New Jersey.

Christiano, L., J., Trabandt, M. S., Eichenbaum, 2013. Unemployment and business cycles. NBER Working Paper No. 19265

Christiano, L., J., Trabandt, M. S., Walentin, K., 2011. Introducing financial frictions and umeployment into a small open economy model. Working Paper Series 214, Sveriges Riksbank

DeJong, D., Dave, C., 2007. Structural Macroeconometrics. Princeton University Press, New Jersey.

Del Negro, M., Schorfheide, F., 2012. DSGE model-based forecasting. Staff Reports 554, Federal Reserve Bank of New York.

Guerrón-Quintana, P., Nason, J., 2012. Bayesian estimation of DSGE models. Working Papers 12-4, Federal Reserve Bank of Philadelphia.

Kalman, R., 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82 (1), 35–45.

Kydland, F., Prescott, E., 1977. Rules Rather Than Discretion: The Inconsistency of Optimal Plans. Journal of Political Economy 85 (3), 473–491.

Kydland, F., Prescott, E., 1982. Time to Build and Aggregate Fluctuations. Econometrica, Econometric Society, vol. 50(6), 1345-1370.

Lucas, R., 1976. Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), 19-46.

Musil, K., Vašíček, O., 2006. Behavior of the Czech Economy: New Open Economy Macroeconomics DSGE Model. Brno: CVKSČE MU, WP č. 23/2006.

Neal, R., 1993. Probabilistic Inference Using Markov Chain Monte Carlo Methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto.

Roger, S., Vlcek, J., 2012. Macrofinancial Modeling At Central Banks. IMF Working Papers 12/21.

Schorfheide, F., 2011. Estimation and Evaluation of DSGE Models: Progress and Challenges. NBER Working Papers 16781.

Sorolla, V., Raurich. X., 2014. Growth, Unemployment and Wage Inertia. E14/309 UB Economics Working Papers