Empirical Testing of the New Keynesian Phillips Curve in the Czech Republic

New keynesian Phillips curve (NKPC) has become a central model to study the relation between inflation and real economic activity, notably in the framework of optimal monetary policy design. However, some recent evidence suggests that empirical data are usually at odds with the underlying theory. The model due to its inherent structure represents a statistical challenge in its own right. Since Galí and Gertler (1999) published their seminal paper introducing estimation via GMM techniques, they have triggered a heated debate on its empirical relevance. Their approach has been heavily criticised by later authors, mainly on the grounds of questionable behaviour of GMM estimator in the NKPC context and/or its small sample properties. The common criticism includes sensitivity to the choice of instrument set, weak identification and small sample bias. In this thesis I propose a new estimation strategy that provides a remedy to above mentioned shortcomings and allows to obtain reliable estimates. The procedure exploits recent advances in GMM theory as well as in other fields of statistics, in particular in the area of time series factor analysis and bootstrap. The proposed estimation strategy consists of several consecutive steps: first, to reduce a small sample bias resulting from excessive use of instruments I summarize all available information by employing factor analysis and include estimated factors into information set. In the second step I use statistical information criteria to select optimal instruments and eventually I obtain confidence intervals on parameters using bootstrap method. In NKPC context all these methods were used for the first time and can also be used independently. Their combination however provides synergistic effect that helps to improve the properties of estimates and to check the efficiency of given steps. Obtained results suggest that NKPC model can explain Czech inflation dynamics fairly well and provide some support for underlying theory. Among other things the results imply that the policy of disinflation may not be as costly with respect to a loss in aggregate product as earlier versions of Phillips curve would indicate. However, finding a good proxy for real economic activity has proved to be a difficult task. In particular we demonstrated that results are conditional on how the measure is calculated, some measures even showed countercyclical behaviour. This issue -- in the thesis discussed only in passing -- is a subject of future research. In addition to the proposed strategy and provided parameter estimates the thesis brings some partial simulation-based findings. Simulations elaborate on earlier literature on naive bootstrap in GMM context and study performance of bootstrap modifications of unit root and KPSS test

Empirical Testing of the New Keynesian Phillips Curve in the Czech Republic

Model nové keynesiánské křivky (NKPC) se v posledních letech stal ústředním modelem pro zkoumání vztahu mezi inflací a reálnou ekonomickou aktivitou, a to zejména v souvislosti s problematikou optimálního nastavení měnové politiky. Ukazuje se však, že není úplně jednoduché sladit výchozí teorii s napozorovanými daty a model představuje vzhledem ke své struktuře zajímavou statistickou výzvu. Vzrušenou debatu o empirické relevanci modelu vyvolal zejména vlivný článek Galí a Gertler (1999), kde autoři představili ekonometrický postup vedoucí k odhadu parametrů metodou GMM. Jejich přístup byl později kritizován zejména z důvodu problematických vlastností tohoto estimátoru v kontextu modelu NKPC a/nebo z důvodu jeho chování v malých výběrech. Ke klíčovým problémům patří zejména citlivost odhadů na volbu instrumentů a riziko slabé identifikace, nebo neidentifikace parametrů. V této práci navrhuji prakticky aplikovatelný postup, který výše uvedená rizika minimalizuje a umožňuje získat spolehlivé odhady modelu NKPC. Postup těží z pokroků ve statistické teorii dosáhnutých v oblasti estimátoru GMM v posledních letech, ale také z pokroků v dalších oblastech, především v aplikaci faktorové analýzy na časové řady a bootstrapu. Empirické ověření modelu představuje souslednost vzájemně navazujících kroků: v prvním kroku je z důvodu zkreslení odhadů v malých výběrech při použití nadbytečného počtu instrumentálních proměnných pomocí faktorové analýzy redukován jejich počet a vytvořena informační množina, ze které jsou ve druhém kroku vybrány na základě statistických (informačních) kriterií optimální instrumenty. Následně je výběrové rozdělení parametrů stanoveno pomocí metody bootstrap. Všechny aplikované metody byly v kontextu modelu NKPC použity poprvé a mohou být použity také samostatně. Jejich kombinace však přináší synergický efekt, který zlepšuje vlastnosti odhadů, a zároveň umožňuje ohodnotit efektivnost jednotlivých kroků. Výsledky naznačují, že model NKPC je možné k popisu inflační dynamiky v České republice použít a přináší tak jistou podporu pro výchozí teorii. Kromě dalších implikací z výsledků vyplývá, že boj s inflací není z pohledu vlivu na agregátní výstup tak nákladný, jak postulují dřívější pojetí Phillipsovy křivky. Problémem zůstává zejména volba vhodné míry pro reálnou ekonomickou aktivitu a podmíněnost výsledků vzhledem k jejímu výpočtu. Některé míry dokonce vykazovaly proticyklický charakter. Tato problematika -- v této práci probíraná pouze okrajově -- představuje výzvu pro budoucí výzkum v této oblasti. Kromě navrženého postupu a odhadu parametrů, práce přináší také dílčí zjištění získané na základě simulací. Simulace obohacují dřívější literaturu především o poznatky týkající se chování naivního bootstrapu v případě GMM estimátoru a chování bootstrapových verzí testů jednotkového kořene, resp. KPSS testu.New keynesian Phillips curve (NKPC) has become a central model to study the relation between inflation and real economic activity, notably in the framework of optimal monetary policy design. However, some recent evidence suggests that empirical data are usually at odds with the underlying theory. The model due to its inherent structure represents a statistical challenge in its own right. Since Galí and Gertler (1999) published their seminal paper introducing estimation via GMM techniques, they have triggered a heated debate on its empirical relevance. Their approach has been heavily criticised by later authors, mainly on the grounds of questionable behaviour of GMM estimator in the NKPC context and/or its small sample properties. The common criticism includes sensitivity to the choice of instrument set, weak identification and small sample bias. In this thesis I propose a new estimation strategy that provides a remedy to above mentioned shortcomings and allows to obtain reliable estimates. The procedure exploits recent advances in GMM theory as well as in other fields of statistics, in particular in the area of time series factor analysis and bootstrap. The proposed estimation strategy consists of several consecutive steps: first, to reduce a small sample bias resulting from excessive use of instruments I summarize all available information by employing factor analysis and include estimated factors into information set. In the second step I use statistical information criteria to select optimal instruments and eventually I obtain confidence intervals on parameters using bootstrap method. In NKPC context all these methods were used for the first time and can also be used independently. Their combination however provides synergistic effect that helps to improve the properties of estimates and to check the efficiency of given steps. Obtained results suggest that NKPC model can explain Czech inflation dynamics fairly well and provide some support for underlying theory. Among other things the results imply that the policy of disinflation may not be as costly with respect to a loss in aggregate product as earlier versions of Phillips curve would indicate. However, finding a good proxy for real economic activity has proved to be a difficult task. In particular we demonstrated that results are conditional on how the measure is calculated, some measures even showed countercyclical behaviour. This issue -- in the thesis discussed only in passing -- is a subject of future research. In addition to the proposed strategy and provided parameter estimates the thesis brings some partial simulation-based findings. Simulations elaborate on earlier literature on naive bootstrap in GMM context and study performance of bootstrap modifications of unit root and KPSS test

Potential Product, Output Gap and Uncertainty Rate Associated with Their Determination while Using the Hodrick-Prescott Filter

In various fields of macroeconomic modelling, researchers often face the problem of decomposing time series into trend component and cycle fluctuations. While there are several potentially useful methods to perform the task in question, Hodrick-Prescott (HP) fi lter seems to have remained (despite some serious criticism) the most popular approach over the past decade. In this article I propose a straightforward and easy-to-implement bootstrap procedure for building pointwise and simultaneous confidence intervals around "point estimates" produced by HP filter. The principle of proposed method can be described as follows: first, we use maximum entropy bootstrap (Vinod, 2004, 2006) to approximate ensemble from which original time series is drawn and then apply the HP filter directly to each bootstrap replication. If necessary, the proposed method can be adapted to allow for uncertainty in the smoothing parameter. Practical usefulness of our approach is demonstrated with an application to the GDP data. Results are encouraging - obtained confi dence intervals for the trend and cyclical component are overall plausible thus supplying a researcher with some measure of uncertainty related to HP filtering. Finally, we demonstrate that a former approach to build confidence intervals for HP filter (Gallego and Johnson, 2005) leads to erratic inference for cycle due to the shape-destroying block bootstrap sampling.r, potential product, output gap, Hodrick-Prescott filter, confidence intervals, bootstrap

The Impact of Financial Variables on Czech Macroeconomic Developments:An Empirical Investigation

This paper investigates empirically to what extent financial variables can explain macroeconomic developments in the Czech Republic and how the results are sensitive to some (usually reasonable or routinely made) modeling choices. To this end, the dynamic model averaging/selection framework is applied to a universe of (potentially large) time-varying parameter VAR models, which allows one to assess the explanatory power of financial variables at each point in time. Based on a set of 27 competing models and an extensive ensemble of alternative specifications of those models, we find that financial variables were particularly relevant in explaining developments in the lead-up to and during economic downturns. By contrast, in tranquil times, models containing only traditional macroeconomic variables explained macroeconomic dynamics reasonably well. Within the broad set of financial variables considered, credit to the private sector, bank profitability, and leverage seem to be among the most relevant indicators

System Priors for Econometric Time Series

This paper introduces “system priors” into Bayesian analysis of econometric time series and provides a simple and illustrative application. Unlike priors on individual parameters, system priors offer a simple and efficient way of formulating well-defined and economically meaningful priors about model properties that determine the overall behavior of the model. The generality of system priors is illustrated using an AR(2) process with a prior that its dynamics comes mostly from business-cycle frequencies

Visual Analysis of Multivatiate Data

The article presents and investigates new possibilities of multivariate data visualization and their analytical convenience. We demonstrate elementary principles, algorithms and graphical outputs of modern visualization methods with particular focus on Bertin matrices, RADVIZ, Projection pursuit and parallel coordinates. Illustrative examples show their practical implementation into the process of multivariate data analysis, hence providing the reader with an idea of the wide range of their application.multivariate data visualization, Bertin matrix, RADVIZ, Projection pursuit, parallel coordinates

Changes in inflation dynamics under inflation targeting? Evidence from Central European countries ☆

a b s t r a c t a r t i c l e i n f o Many countries have implemented inflation targeting in recent decades. At the same time, the international conditions have been favorable, so it is hard to assess to what extent the success in stabilizing inflation should be attributed to good luck and to what extent to the specific policy framework. In this paper, we provide a novel look at the dynamics of inflation under inflation targeting, focusing on three Central European (CE) countries that adopted the IT regime at similar times and in similar environments. We use the framework of the open economy New Keynesian Phillips curve (NKPC) with time-varying parameters and stochastic volatility to recover changes in price-setting and expectation formation behavior and volatility of shocks. We employ Bayesian model averaging to tackle the uncertainty in the selection of instrumental variables and to account for the possible country-specific nature of inflation dynamics. The results suggest that inflation targeting does not itself automatically trigger changes in the inflation process, and the way the framework is implemented might matter. In particular, we find rather heterogeneous evolution of intrinsic inflation persistence and volatility of inflation shocks across these countries despite the fact that all three formally introduced inflation targeting a decade ago

Empirical Testing of New Keynesian Phillips Curve in Conditions of the Czech Republic in 1994 - 2003

New concepts have been presented in modelling of inflation dynamics recently, among others the new Keynesian Phillips curve (NKPC). There are several traditional ways of NKPC model validity testing, but none of them seems to be practically applicable in conditions of the Czech Republic. We tried to test the validity of NKPC on the basis of time series. For this purpose we applied an interesting non-traditional method proposed by Demery and Duck. This method does not rely on direct estimation of NKPC parameters, but relatively easy tests based on the cointegration analysis of time series are employed. Its application indicates that the NKPC model cannot be considered as effective in conditions of the Czech Republic; this model does not describe the inflation process sufficiently and it is not a suitable model for inflation prediction or for the choice of appropriate monetary (anti-inflation) policy.time series, new Keynesian Phillips curve, model, inflation, cointegration analysis

Changes in Inflation Dynamics under Inflation Targeting? Evidence from Central European Countries

The purpose of this paper is to provide a novel look at the evolution of inflation dynamics in selected Central European (CE) countries. We use the lens of the New Keynesian Phillips Curve (NKPC) nested within a time-varying framework. Exploiting a time-varying regression model with stochastic volatility estimated using Bayesian techniques, we analyze both the closed and open-economy version of the NKPC. The results point to significant differences between the inflation processes in three CE countries