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To model and forecast a monthly pine sawtimber (PST) stumpage price, y t , data collected across 11 southern states in the U.S., we adopt a new semiparametric approach where the first phase adopts a nonparametric method called "Time Series Central Subspace with Covariates" (TSCS-C) to extract sufficient information about y t through a univariate time series { d t } , which is a linear combination of a set of past values of y t and a high dimensional covariate vector x t of sale characteristics. Then, { d t } alone is used as the predictor series to build a parametric nonlinear time series model for y t . This yields a new semiparametric nonlinear time series model for y t . Assessment in terms of out-of-sample forecasts of monthly PST stumpage prices show that our semiparametric model with the covariate x t has the smallest average forecasting error compared to another semiparametric nonlinear time series model without x t and two other parametric counterparts based on multiplicative seasonal autoregressive integrated moving average models with and without x t . This data underscores the ability of our semiparametric approach to first reduce the dimensionality of x t and a set of past values of y t significantly using the TSCS-C nonparametric methodology and then to produce a superior nonlinear time series model. [ABSTRACT FROM AUTHOR]