Threshold effects and unit roots of real commodity prices since the mid-nineteenth century

This paper investigates whether a unit root process and nonlinearities can characterize real commodity prices for six major primary goods. An unconstrained two-regime threshold autoregressive model is used with an autoregressive unit root. Among the main results, it is found that terms of trade for agricultural, mineral, non-tropical, and non-oil goods are nonlinear processes that are characterized by a unit root process. The finding of nonlinearities explains why the deterioration of the terms of trade has been episodic. Additionally, we found there is no statistical evidence supporting the Prebisch-Singer hypothesis for agricultural, mineral, non-tropical, and non-oil goods.

Convolution Based Unit Root Processes: a Simulation Approach

We propose a convolution based approach to the simulation of a modified version of a unit root process where the state variable $Y_{t-1}$ is dependent on the innovation $\varepsilon_t$. The dependence structure is given by a copula function $C$. We study by simulation the effect of a negative correlation on the properties of unit roots. We call this process C-UR(1).

A Simple Encompassing Test for the Deterministic and Bilinear Unit Root Models

A new parameters’ encompassing test is proposed for deciding between the deterministic unit root processes with a structural break and the bilinear unit root model without such break. The test consists in testing three sets of hypotheses regarding parameters in a simple regression model. The test uses the t-ratio and F-statistics, of non-trivial distributions under the null hypothesis. The finite sample distributions for the relevant statistics are tabulated and the asymptotic distribution of the F-test is derived. The test has been applied for the daily stock price indices for 66 countries, for the period 1992-2001. The results support the conjecture that the bilinear model dominates the structural break model more often than the other way around. Also, it is likely that in practical applications the bilinear unit root process might be mistaken for the deterministic unit root process with a structural break.Financial support of INTAS project No. 03-51-3714 Nonstationary multivariate and nonlinear econometric models: theory and applications is gratefully acknowledged.

Are US real house prices stationary? New evidence from univariate and panel data

Many papers in the housing literature treat the intertemporal evolution of the logarithm of US real house prices as a unit root process. They also study the cointegration relationship among the logarithm of real house prices and fundamental economic variables such as income and they apply an error correction specification for modeling and forecasting real house prices. This paper argues that the logarithm of US real house price is not a unit root process. Instead, the evidence from a 120-year national dataset and metro area level and state level panel data sets supports the notion that US house prices are trend stationary. One result of this conclusion is that the validity of analyses of US house prices based on cointegration and error correction models needs to be reconsidered.

THE EFFICIENT MARKET HYPOTHESIS REVISITED: EVIDENCE FROM THE FIVE SMALL OPEN ASEAN STOCK MARKETS

The efficient market hypothesis (EMH), which suggests that returns of a stock market are unpredictable from historical price changes, is satisfied when stock prices are characterized by a random walk (unit root) process. A finding of unit root implies that stock returns cannot be predicted. This paper investigates the stock prices behavior of five ASEAN (Association of Southeast Asian Nations) countries i.e., Indonesia, Malaysia, Philippines, Singapore and Thailand, for the period from 1990:1 to 2009:1 using a two-regime threshold autoregressive (TAR) approach which allows testing nonlinearity and non-stationarity simultaneously. Among the main findings, our results indicate that stock prices of Malaysia and Thailand are a non-linear series and are characterized by a unit root process, consistent with the EMH. Furthermore, we find that stock prices of Indonesia, Philippines and Singapore follow a non-linear series, however, stock price indices are stationary processes that are inconsistent with the EMH.

SUMS OF EXPONENTIALS OF RANDOM WALKS WITH DRIFT

For many time series in empirical macro and finance, it is assumed that the logarithm of the series is a unit root process. Since we may want to assume a stable growth rate for the macroeconomics time series, it seems natural to potentially model such a series as a unit root process with drift. This assumption implies that the level of such a time series is the exponential of a unit root process with drift and therefore, it is of substantial interest to investigate analytically the behavior of the exponential of a unit root process with drift. This paper shows that the sum of the exponential of a random walk with drift converges in distribution, after rescaling by the exponential of the maximum value of the random walk process. A similar result was established in earlier work for unit root processes without drift. The results derived here suggest the conjecture that also in the case when the Dickey-Fuller test or the KPSS statistic is applied to the exponential of a unit root process with drift, these tests will asymptotically indicate stationarity.

Least Squares Estimators for Unit Root Processes with Locally Stationary Disturbance

The random walk is used as a model expressing equitableness and the effectiveness of various finance phenomena. Random walk is included in unit root process which is a class of nonstationary processes. Due to its nonstationarity, the least squares estimator (LSE) of random walk does not satisfy asymptotic normality. However, it is well known that the sequence of partial sum processes of random walk weakly converges to standard Brownian motion. This result is so-called functional central limit theorem (FCLT). We can derive the limiting distribution of LSE of unit root process from the FCLT result. The FCLT result has been extended to unit root process with locally stationary process (LSP) innovation. This model includes different two types of nonstationarity. Since the LSP innovation has time-varying spectral structure, it is suitable for describing the empirical financial time series data. Here we will derive the limiting distributions of LSE of unit root, near unit root and general integrated processes with LSP innovation. Testing problem between unit root and near unit root will be also discussed. Furthermore, we will suggest two kind of extensions for LSE, which include various famous estimators as special cases.