# Markku LanneUniversity of Helsinki | HY · Faculty of Social Sciences

Markku Lanne

PhD

## About

86

Publications

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2,333

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Citations since 2017

Introduction

Additional affiliations

April 2013 - present

## Publications

Publications (86)

We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n2+n(n−1)/2 suitably selected moment conditions, when at least n – 1 of the...

Theories often make predictions about the signs of the effects of economic shocks on observable variables, thus implying inequality constraints on the parameters of a structural vector autoregression (SVAR). We introduce a new Bayesian procedure to evaluate the probabilities of such constraints, and, hence, to validate the theoretically implied eco...

We consider estimation of the structural vector autoregression (SVAR) by the generalized method of moments (GMM). Given non-Gaussian errors and a suitable set of moment conditions, the GMM estimator is shown to achieve local identification of the structural shocks. The optimal set of moment conditions can be found by well-known moment selection cri...

Franses (Empir Econ, 2018. https://doi.org/10.1007/s00181-018-1417-8) criticised the practice in the empirical literature of replacing expected inflation by the sum of realised future inflation and an error in estimating the parameters of the new Keynesian Phillips curve (NKPC). In particular, he argued that this assumption goes against the Wold de...

We propose imposing data-driven identification constraints to alleviate the multimodality problem arising in the estimation of poorly identified dynamic stochastic general equilibrium models under non-informative prior distributions. We also devise an iterative procedure based on the posterior density of the parameters for finding these constraints...

We study the evolution of U.S. inflation by means of a new noncausal autoregressive model with time-varying parameters that outperforms the corresponding causal and constant-parameter noncausal models in terms of fit and forecast accuracy. Our model also beats the unobserved component stochastic volatility (UCSV) model, one of the best-performing u...

We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by-product. We apply the methods to postwar US inflation and GDP growth. The noncausal m...

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are needed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, ident...

We propose a new generalized forecast error variance decomposition with the attractive property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the generalized impulse response function, and it can easily be obtained by simulation. The new decomposition is illustrated in a...

We study whether the accuracy of news announcements matters for the impact of news on exchange rate volatility. We use high-frequency EUR/USD returns and releases of 20 US macroeconomic indicators, and measure the precision of news in three different ways. When the precision is defined by the size of the first revision of the previous month's figur...

We develop tests for predictability in a first-order ARMA model often suggested for stock returns. Instead of the conventional
ARMA model, we consider its non-Gaussian and noninvertible counterpart that has identical autocorrelation properties but allows
for conditional heteroskedasticity prevalent in stock returns. In addition to autocorrelation,...

We propose a new methodology for ranking in probability the commonly proposed drivers of inflation in the new Keynesian model. The approach is based on Bayesian model selection among restricted vector autoregressive (VAR) models, each of which embodies only one or none of the candidate variables as the driver. Simulation experiments suggest that ou...

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introd...

We use noncausal autoregressions to examine the persistence properties of quarterly U.S. consumer price inflation from 1970:1-2012:2. These nonlinear models capture the autocorrelation structure of the inflation series as accurately as their conventional causal counterparts, but they allow for persistence to depend on the size and sign of shocks to...

We propose a noncausal autoregressive model with time-varying parameters, and apply it to U.S. postwar inflation. The model fits the data well, and the results suggest that inflation persistence follows from future expectations. Persistence has declined in the early 1980s and slightly increased again in the late 1990s. Estimates of the new Keynesia...

We propose an estimation method of the new Keynesian Phillips curve (NKPC) based on a univariate noncausal autoregressive model for the inflation rate. By construction, our approach avoids a number of problems related to the GMM estimation of the NKPC. We estimate the hybrid NKPC with quarterly U.S. data (1955:1-2010:3), and both expected future in...

Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looking Phillips curve models to improve on simple univariate models in forecasting U.S. inflation. While this indeed is the case when the benchmark is a causal autoregression, little change in forecast accuracy is detected when a noncausal autoregression...

We develop likelihood-based tests for autocorrelation and predictability in a first order non- Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated but, in the non-Gaussian case, dependent and nonlinearly...

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series, The noncausal models consistently outperform the causal models. For a collection of quarterly time series, the improvement in...

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors....

This note provides a warning against careless use of the generalized method of moments (GMM) with time series data. We show that if time series follow non-causal autoregressive processes, their lags are not valid instruments, and the GMM estimator is inconsistent. Moreover, endogeneity of the instruments may not be revealed by the J-test of overide...

No consensus has emerged on how to deal with overnight returns when calculating realized volatility in markets where trading does not take place 24 hours a day. This paper explores several common volatility applications, investigating how the chosen treatment of overnight returns affects the results. For example, the selection of the best volatilit...

The paper briefly presents experiences in research and teaching cooperation between academic and official statistics institutions in Finland. In addition, some general and international aspects are discussed. Focus is in survey methodology but also empirical economics is considered. The paper introduces a suggestion that the EU (Eurostat) should pr...

In this paper, we propose a simulation-based method for computing point and density forecasts for univariate noncausal and non-Gaussian autoregressive processes. Numerical methods are needed to forecast such time series because the prediction problem is generally nonlinear and no analytic solution is therefore available. According to a limited simu...

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical literature, nonfundamental s...

In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible mo...

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the posterior model probability provides a convenient model s...

In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and...

It is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across states. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standa...

We study the impact of positive and negative macroeconomic U.S. and European news announcements in different phases of the business cycle on the high-frequency volatility of the EUR/USD exchange rate. The results suggest that news effects depend on the state of the economy. In general, news increases volatility more in good times than in bad times....

We study the functioning of secured and unsecured interbank markets in the presence of credit risk. The model generates empirical predictions that are in line with developments during the 2007–09 financial crisis. Interest rates decouple across secured and unsecured markets following an adverse shock to credit risk. The scarcity of underlying colla...

This paper exploits the fact that implied volatilities calculated from identical call and put options have often been empirically found to differ, although they should be equal in theory. We propose a new bivariate mixture multiplicative error model and show that it is a good fit to Nikkei 225 index call and put option implied volatility (IV). A go...

In this paper, we incorporate time-varying mixing probabilities into univariate and bivariate mixture multiplicative error models. Switching between the regimes is governed by an observable predetermined variable. The models are applicable to positive-valued time series, and are particularly well-suited for different financial volatility measures....

Using GARCH-in-Mean models, we study the robustness of the risk–return relationship in monthly U.S. stock market returns (1928:1–2004:12) with respect to the specification of the conditional mean equation. The issue is important because in this commonly used framework, unnecessarily including an intercept is known to distort conclusions. The existe...

This paper provides a simple epidemiology model where households, when forming their inflation expectations, rationally adopt the past release of inflation with certain probability rather than the forward-looking newspaper forecast as suggested in Carroll [2003, Macroeconomic Expectations of Households and Professional Forecasters, Quarterly Journa...

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is affected b...

Different identification schemes for monetary policy shocks have been proposed in the literature. They typically specify just-identifying restrictions in a standard structural vector autoregressive (SVAR) framework. Thus, in this framework the different schemes cannot be checked against the data with statistical tests. We consider different approac...

The role of expectations for economic fluctuations has received considerable attention in recent business cycle analysis. We exploit Markov regime switching models to identify shocks in cointegrated structural vector autoregressions and investigate different identification schemes for bivariate systems comprising U.S. stock prices and total factor...

A central issue of monetary policy analysis is the specification of monetary policy shocks. In a structural vector autoregressive setting there has been some controversy about which restrictions to use for identifying the shocks because standard theories do not provide enough information to fully identify monetary policy shocks. In fact, to compare...

The role of expectations for economic ∞uctuations has received considerable attention in recent business cycle analysis. We exploit Markov regime switching models to identify shocks in cointegrated structural vector autoregressions and investigate difierent identiflcation schemes for bivariate systems comprising U.S. stock prices and total factor p...

This paper exploits the fact that implied volatilities calculated from identical call and put options have often been empirically found to differ, although they should be equal in theory. We propose a new bivariate mixture multiplicative error model and show that it is a good fit to Nikkei 225 index call and put option implied volatility (IV). A go...

We compare forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar and the Japanese Yen obtained both directly and through decomposition. Decomposing the realized volatility into its continuous sample path and jump components, and modeling and forecasting them separately instead of directly forecasting...

We compare the accuracy of the survey forecasts and forecasts implied by economic binary options on the U.S. non-farm payroll change. For the first-release data both the market-based and survey forecasts are biased, while they are rational and approximately equally accurate for later releases. Both forecasts are also more accurate for later release...

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility...

The paper studies a factor GARCH model and develops test procedures which can be used to test the number of factors needed to model the conditional hete roskedasticity in the considered time series vector. Assuming normally distributed errors the parameters of the model can be straightforwardly estimated by the method of maximum likelihood. Ineffic...

We argue that a transaction tax is likely to amplify, not dampen, volatility in the foreign exchange mar-kets. Our argument stems from the decentralised trading practice and the presumable discrepancy be-tween ‘informed’ and ‘uninformed’ traders’ valuations. Since informed traders’ valuations are likely to be less dispersed, a transaction tax penal...

A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of deutschemark/dollar and yen/dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used autoregr...

The low power of the standard Wald test in a GARCH-in-Mean model with an unnecessary intercept is shown to explain the apparent absence of a risk–return tradeoff in stocks. The importance of this finding is illustrated with monthly U.S. data.

According to several empirical studies US inflation and nominal interest rates as well as the real interest rate can be described as unit root processes. These results imply that nominal interest rates and expected inflation do not move one-for-one in the long run, which is incongruent with theoretical models. In this paper we introduce a new nonli...

The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economist...

In structural vector autoregressive (SVAR) models identifying restrictions for shocks and impulse responses are usually derived from economic theory or institutional constraints. Sometimes the restrictions are insufficient for identifying all shocks and impulse responses. In this paper it is pointed out that specific distributional assumptions can...

In this paper we study a new class of nonlinear GARCH models. Special interest is devoted to models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable is mainly motivated by the de...

We use the Autoregressive Conditional Duration (ACD) framework of Engle and Russell (1998) to study the effect of trading volume on price duration (ie the time lapse between consecutive price changes) of a stock listed both in the domestic and the foreign market. As a case study we use the example of Nokia’s share, which is actively traded both in...

In this paper we consider a GARCH-in-Mean (GARCH-M) model based on the so-called z distribution. This distribution is capable of modeling moderate skewness and kurtosis typically encountered in financial return series, and the need to allow for skewness can be readily tested. We apply the new GARCH-M model to study the relationship between risk and...

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the article and further informal checks, the model is capable of capturing both of the typical characteristics of the short-term interest rate: volatility persistence and the dep...

The use of asymptotic critical values in stationarity tests against the alternative of a unit root process is known to lead to over-rejections in finite samples when the considered process is stationary but highly persistent. We claim that, in recent parametric tests, this is caused by estimation errors which result when the autoregressive paramete...

The expectations hypothesis of the term structure of interest rates is tested with monthly Eurodollar deposit rates for maturities of 1, 3 and 6 months covering the period 1983:1-1999:6. Classical regression based tests indicate rejection, while tests in a new model allowing for potential regime shifts that have not occurred in the sample period le...

Two types of unit root tests which accommodate a structural level shift at a known point in time are extended to the situation where the break date is unknown. It is shown that for any estimator for the break date the tests have the same asymptotic distribution as the corresponding tests under the known break date assumption. Different estimators o...

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the article and further informal checks, the model is capable of capturing both of the typical characteristics of the short-term interest rate: volatility persistence and the dep...

The pollution-convergence hypothesis is formalized in a neoclassical growth model with optimal emissions reduction: pollution growth rates are positively correlated with output growth (scale effect) but negatively correlated with emission levels (defensive effect). This dynamic law is empirically tested for two major and regulated air pollutants -...

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form, and additional deterministic mean and trend terms are allowed for. Prior to the tests, the deterministic parts and other nuisance parameters of the data generation process are estimated in a first step....

A number of unit root tests which accommodate a deterministic level shift at a known point in time are compared in a Monte Carlo study. The tests differ in the way they treat the deterministic term of the DGP. It turns out that tests which estimate the deterministic term by a GLS procedure under the unit root null hypothesis are superior in terms o...

In some cases the unit root or near unit root behavior of linear autoregressive models fitted to economic time series is not in accordance with the underlying economic theory. To accommodate this feature we consider a threshold autoregressive (TAR) process with the threshold effect only in the intercept term. Although these processes are stationary...

Unit root tests are considered for time series with innovational outliers. The function representing the outliers can have a very general nonlinear form and additional deterministic mean and trend terms are allowed for. Prior to the tests the deterministic parts and other nuisance parameters of the data generation process are estimated in a first s...

Tests of the Fisher effect are plagued by high persistence in interest rates. Instead of standard regression analysis and asymptotic results, methods relying on local-to-unity asymptotics are employed in testing for the Fisher effect with monthly U.S. data covering the period 1953:1-1990:12. These procedures are extensions of a recently presented m...

In some cases the unit root or near unit root behavior of linear autoregressive models fitted to economic time series is not in accordance with the underlying economic theory. To accommodate this feature we consider a threshold autoregressive process with the threshold effect only in the intercept term. Although these proceses are stationary, their...

The term structure of interest rates is often modelled as a cointegrated system with the yield spreads forming the cointegrating vectors. Testing whether the yield spreads span the cointegration space is problematic because conventional tests on the cointegration vectors tend to overreject when the largest autoregressive roots deviate from unity, a...

Previous literature indicates that stock returns are predictable by several strongly autocorrelated forecasting variables, especially at longer horizons. It is suggested that this finding is spurious and follows from a neglected near unit root problem. Instead of the commonly used t-test, we propose a test that can be considered as a general test o...

Abstract -The ability of yield spreads to predict changes in long-term interest rates implied by the expectations hypothesis is usually rejected. It is suggested that this rejection is often caused by high persistence in the spread when standard inference is employed. Instead, the asymptotically valid method of Cavanagh et al. (1995) is applied to...

The term structure of Finnish HELIBOR interest rates is studied by modelling it as a co-integrated system. There are three co-integrating vectors among the six rates. They can be identified as the spreads between the two and one and three and one month rates, and a third vector tending to keep the yield curve linear. Co-integration analysis of part...

The paper briefly presents experiences in research and teaching cooperation between academic and official statistics institutions in Finland. In addition, some general and international aspects are discussed. Focus is in survey methodology but also empirical economics is considered. The paper introduces a suggestion that the EU (Eurostat) should pr...

This is a supplementary appendix to "Noncausal Vector Autoregression".

We study whether the accuracy of the news announcements matters in the impact of news on excchange rate volatility. We use high-frequency USD/EUR returns and releases concerning 19 US macroeconomic indicators, and measure the precision of news in dierent ways. The results consistently suggest that investors react signicantly stronger to announcemen...