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Publication Metadata only A note on the geometric ergodicity of a nonlinear AR-ARCH model(Elsevier Science Bv, 2010) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AThis note studies the geometric ergodicity of nonlinear autoregressive models with conditionally heteroskedastic errors. A nonlinear autoregression of order p (AR(p)) with the conditional variance specified as the conventional linear autoregressive conditional heteroskedasticity model of order q (ARCH(q)) is considered. Conditions under which the Markov chain representation of this nonlinear AR-ARCH model is geometrically ergodic and has moments of known order are provided. The obtained results complement those of Liebscher [Liebscher, E., 2005. Towards a unified approach for proving geometric ergodicity and mixing properties of nonlinear autoregressive processes, journal of Time Series Analysis, 26,669-689] by showing how his approach based on the concept of the joint spectral radius of a set of matrices can be extended to establish geometric ergodicity in nonlinear autoregressions with conventional ARCH(q) errors.Publication Metadata only Is there a 'heat-or-eat' trade-off in the Uk?(Wiley, 2014) Beatty, Timothy K. M.; Blow, Laura; Crossley, Thomas F.; Department of Economics; Crossley, Thomas Fraser; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/ADo households cut back on food spending to finance the additional cost of keeping warm during spells of unseasonably cold weather? For households which cannot smooth consumption over time, we describe how cold weather shocks are equivalent to income shocks. We merge detailed household level expenditure data from older households with historical regional weather information. We find evidence that the poorest of older households cannot smooth fuel spending over the worst temperature shocks. Statistically significant reductions in food spending occur in response to winter temperatures 2 or more standard deviations colder than expected, which occur about 1 winter month in 40; reductions in food expenditure are considerably larger in poorer households.Publication Metadata only Martingale property of exchange rates and central bank interventionssd(Taylor & Francis Inc, 2003) Department of Economics; Yılmaz, Kamil; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 6111This article uses the variance ratio-based multiple comparison test and the Richardson-Smith Wald test procedures to test for the martingale property of daily exchange rates of seven major currencies vis-A-vis the U.S. dollar. To allow for the possibility that exchange rates are not governed by a single process throughout the float, the test statistics are calculated and plotted for fixed-length moving subsample windows rather than being applied to the full Sample. The results show that exchange rates do not always follow the martingale process. During the times of coordinated central bank interventions, exchange rates deviate from the martingale property.Publication Metadata only Maximum likelihood estimation of a noninvertible ARMA model with autoregressive conditional heteroskedasticity(Elsevier, 2013) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AWe consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to the so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial applications. Unlike in previous literature on maximum likelihood estimation of noncausal and/or noninvertible ARMA models and all-pass models, our estimation theory does allow for Gaussian innovations. We give conditions under which a strongly consistent and asymptotically normally distributed solution to the likelihood equations exists, and we also provide a consistent estimator of the limiting covariance matrix.Publication Metadata only On the survival of some unstable two-sided matching mechanisms(Physica-Verlag Gmbh & Co, 2005) Department of Economics; Ünver, Utku; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AIn the 1960s, three types of matching mechanisms were adopted in regional entry-level British medical labor markets to prevent unraveling of contract dates. One of these categories of matching mechanisms failed to prevent unraveling. Roth (1991) showed the instability of that failing category. One of the surviving categories was unstable as well, and Roth concluded that features of the environments of these mechanisms are responsible for their survival. However, Unver (2001) demonstrated that the successful yet unstable mechanisms performed better in preventing unraveling than the unsuccessful and unstable category in an artificial-adaptive-agent-based economy. In this paper, we conduct a human subject experiment in addition to short- and long-run artificial agent simulations to understand this puzzle. We find that both the unsuccessful and unstable mechanism and the successful and unstable mechanism perform poorly in preventing unraveling in the experiment and in short-run simulations, while long-run simulations support the previous Unver finding.Publication Metadata only Parameter estimation in nonlinear AR-GARCH models(Cambridge Univ Press, 2011) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AThis paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.Publication Metadata only Posterior-predictive evidence on us inflation using extended new keynesian phillips curve models with non-filtered data(Wiley, 2014) Basturk, Nalan; Ceyhan, S. Pinar; Van Dijk, Herman K.; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low-frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time-varying patterns in levels and volatilities. Forward- and backward-looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation-based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long-run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years.Publication Metadata only Testing for a unit root in a stationary ESTAR process(Taylor & Francis Inc, 2011) N/A; Department of Economics; Kılıç, Rehim; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AThis article develops a statistic for testing the null of a linear unit root process against the alternative of a stationary exponential smooth transition autoregressive model. The asymptotic distribution of the test is shown to be nonstandard but nuisance parameter-free and hence critical values are obtained by simulations. Simulations show that the proposed statistic has considerable power under various data generating scenarios. Applications to real exchange rates also illustrate the ability of our test to reject null of unit root when some of the alternative tests do not.Publication Metadata only Using survey information for improving the density nowcasting of U.S. GDP(Taylor & Francis Inc) Demircan, Hamza; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity," conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.Publication Metadata only Weak convergence to a matrix stochastic integral with stable processes(Cambridge Univ Press, 1997) Department of Economics; Caner, Mehmet; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/AThis paper generalizes the univariate results of Chan and Tran (1989, Econometric Theory 5, 354-362) and Phillips (1990, Econometric Theory 6, 44-62) to multivariate time series. We develop the limit theory for the least-squares estimate of a VAR(1) for a random walk with independent and identically distributed errors and for I(1) processes with weakly dependent errors whose distributions are in the domain of attraction of a stable law. The limit laws are represented by functionals of a stable process. A semiparametric correction is used in order to asymptotically eliminate the ''bias'' term in the limit law. These results are also an extension of the multivariate limit theory for square-integrable disturbances derived by Phillips and Durlauf (1986, Review of Economic Studies 53, 473-495). Potential applications include tests for multivariate unit roots and cointegration.