C54 - Quantitative Policy ModelingReturn

Though DSGE models have provided very modest macroeconomic modeling results, they have enjoyed some massive support both from theorists and practitioners. To find an explanation for this state, this paper reviews the reasons why DSGE models fail to perform. For this purpose, first we present the opinions of leading American economists on DSGE models before the Committee on Science and Technology, U.S. House of Representatives in 2010. According to many experts in the USA, DSGE models are considered to be one of the main culprits of the recent economic crisis. Then we provide our own analysis where we point to some relevant drawbacks in the models' construction that may lead to their failure. They are the continuum notion, log-linearization issue and natural production concept. Finally, we suggest the return to the endogenous cycle macroeconomic modeling approach as an alternative solution to DSGE models. In our opinion, this approach may better capture the more complex nature of economic processes with the existence of non-periodic oscillation and chaos. With respect to the current state of macroeconomic modeling, we consider both microeconomic and macroeconomic approaches at least as two equivalent alternatives.

The article is focused on the analysis of export performance of the German economy, including the model scenarios that illustrate the transmission of shocks to the external environment of the Germany into the Czech economy. The analysis is motivated by the fact that the German economy is by far the largest trading partner of the Czech Republic since its establishment in 1993 and that Germany plays the role of an economic "locomotive" in the broader European region.

Knowledgeability about interest rates set by a central bank is very important for all participants in an economy. In this paper we have used publicly available data to model how Czech National Bank manipulates its 2W repo rate when conducts its monetary policy. For this purpose, eight indicators are chosen. They are the Consumer Price Index (CPI), GDP growth rate (HDP), the monthly exchange rate EURCZK (KURZ), the monthly growth rate of monetary aggregate M2 (M2), the monthly unemployment rate (NEZAM), the monetary policy interest rate of the European Central Bank (EBC), the two-week Prague Interbank Interest rate PRIBOR14 and Economic Sentiment Indicator (IES). First, they are used as explanatory variables and then as the input signals to two different artificial neural network types with different architecture: the multilayer perceptron (MLP) and radial basis function (RBF) nets with different number of hidden neurons to model 2W repo rate of CNB. As a result, we fi nd that while the RBF network fails to provide stable results superior to the one of the linear model, the MLP network always can deliver better results than the one of the linear model. The best results are achieved with a network with only two hidden neurons. Further, these results are relatively stable with minimum time needed to complete the calculation. The MLP network therefore seems to be a promising tool for modeling the 2W repo rate of CNB.